A Comprehensive Study on Face Recognition Biases Beyond Demographics
IEEE Transactions on Technology and Society
Face recognition (FR) systems have a growing effect on critical decision-making processes. Recent works have shown that FR solutions show strong performance differences based on the user’s demographics. However, to enable a trustworthy FR technology, it is essential to know the influence of an extended range of facial attributes on FR beyond demographics. Therefore, in this work, we analyse FR bias over a wide range of attributes. We investigate the influence of 47 attributes on the verification performance of two popular FR models. The experiments were performed on the publicly available MAAD-Face attribute database with over 120M high-quality attribute annotations. To prevent misleading statements about biased performances, we introduced control group based validity values to decide if unbalanced test data causes the performance differences. The results demonstrate that also many non-demographic attributes strongly affect the recognition performance, such as accessories, hair-styles and colors, face shapes, or facial anomalies. The observations of this work show the strong need for further advances in making FR system more robust, explainable, and fair. Moreover, our findings might help to a better understanding of how FR networks work, to enhance the robustness of these networks, and to develop more generalized bias-mitigating face recognition solutions.
Explainable Face Image Quality Assessment
Darmstadt, TU, Master Thesis, 2021
The high performance of today’s face recognition systems is driven by the quality of the used samples. To ensure a high quality of face images enrolled in a system, face image quality assessment is performed on these images. However, if an image is rejected due to low quality it is not obvious to the user why. Showing what led to the low quality is useful to increase trust and transparency in the system. Previous work has never addressed the explainability of their quality estimation, but only the explainability of their face recognition approaches. In this work, we propose a gradient-based method that explains which pixels contribute to the overall image quality and which do not. By adding a quality node to the end of the model, we can calculate quality-dependent gradients and visualize them. The qualitative experiments are conducted on three different datasets and we also propose a general framework for quantitative analysis of face image quality saliency maps. With our method, we can assign quality values to individual pixels and provide a meaningful explanation of how face recognition models work and how they respond to face image quality impairments. Our method provides pixel-level explanations, requires no training, applies to any face recognition model, and also takes model-specific behavior into account. By explaining how the poor quality is caused, concrete instructions can be given to people who take pictures suitable for face recognition, face image standards can be adapted, and low-quality areas can be inpainted.
MAAD-Face: A Massively Annotated Attribute Dataset for Face Images
IEEE Transactions on Information Forensics and Security
Soft-biometrics play an important role in face biometrics and related fields since these might lead to biased performances, threaten the user’s privacy, or are valuable for commercial aspects. Current face databases are specifically constructed for the development of face recognition applications. Consequently, these databases contain a large number of face images but lack in the number of attribute annotations and the overall annotation correctness. In this work, we propose a novel annotation-transfer pipeline that allows to accurately transfer attribute annotations from multiple source datasets to a target dataset. The transfer is based on a massive attribute classifier that can accurately state its prediction confidence. Using these prediction confidences, a high correctness of the transferred annotations is ensured. Applying this pipeline to the VGGFace2 database, we propose the MAAD-Face annotation database. It consists of 3.3M faces of over 9k individuals and provides 123.9M attribute annotations of 47 different binary attributes. Consequently, it provides 15 and 137 times more attribute annotations than CelebA and LFW. Our investigation on the annotation quality by three human evaluators demonstrated the superiority of the MAAD-Face annotations over existing databases. Additionally, we make use of the large number of high-quality annotations from MAAD-Face to study the viability of soft-biometrics for recognition, providing insights into which attributes support genuine and imposter decisions. The MAAD-Face annotations dataset is publicly available.
MiDeCon: Unsupervised and Accurate Fingerprint and Minutia Quality Assessment based on Minutia Detection Confidence
IJCB 2021. IEEE/IARP International Joint Conference on Biometrics
IEEE International Joint Conference on Biometrics (IJCB) <2021, online>
An essential factor to achieve high accuracies in finger-print recognition systems is the quality of its samples. Previous works mainly proposed supervised solutions based on image properties that neglects the minutiae extraction process, despite that most fingerprint recognition techniques are based on detected minutiae. Consequently, a fingerprint image might be assigned a high quality even if the utilized minutia extractor produces unreliable information. In this work, we propose a novel concept of assessing minutia and fingerprint quality based on minutia detection confidence (MiDeCon). MiDeCon can be applied to an arbitrary deep learning based minutia extractor and does not require quality labels for learning. We propose using the detection reliability of the extracted minutia as its quality indicator. By combining the highest minutia qualities, MiDeCon also accurately determines the quality of a full fingerprint. Experiments are conducted on the publicly available databases of the FVC 2006 and compared against several baselines, such as NIST’s widely-used fingerprint image quality software NFIQ1 and NFIQ2. The results demonstrate a significantly stronger quality assessment performance of the proposed MiDeCon-qualities as related works on both, minutia- and fingerprint-level. The implementation is publicly available.
Mitigating Soft-Biometric Driven Bias and Privacy Concerns in Face Recognition Systems
Darmstadt, TU, Diss., 2021
Biometric verification refers to the automatic verification of a person’s identity based on their behavioural and biological characteristics. Among various biometric modalities, the face is one of the most widely used since it is easily acquirable in unconstrained environments and provides a strong uniqueness. In recent years, face recognition systems spread world-wide and are increasingly involved in critical decision-making processes such as finance, public security, and forensics. The growing effect of these systems on everybody’s daily life is driven by the strong enhancements in their recognition performance. The advances in extracting deeply-learned feature representations from face images enabled the high-performance of current face recognition systems. However, the success of these representations came at the cost of two major discriminatory concerns. These concerns are driven by soft-biometric attributes such as demographics, accessories, health conditions, or hairstyles. The first concern is about bias in face recognition. Current face recognition solutions are built on representation-learning strategies that optimize total recognition performance. These learning strategies often depend on the underlying distribution of soft-biometric attributes in the training data. Consequently, the behaviour of the learned face recognition solutions strongly varies depending on the individual’s soft-biometrics (e.g. based on the individual’s ethnicity). The second concern tackles the user’s privacy in such systems. Although face recognition systems are trained to recognize individuals based on face images, the deeply-learned representation of an individual contains more information than just the person’s identity. Privacy-sensitive information such as demographics, sexual orientation, or health status, is encoded in such representations. However, for many applications, the biometric data is expected to be used for recognition only and thus, raises major privacy issues. The unauthorized access of such individual’s privacy-sensitive information can lead to unfair or unequal treatment of this individual. Both issues are caused by the presence of soft-biometric attribute information in the face images. Previous research focused on investigating the influence of demographic attributes on both concerns. Consequently, the solutions from previous works focused on the mitigation of demographic-concerns only as well. Moreover, these approaches require computationally-heavy retraining of the deployed face recognition model and thus, are hardly-integrable into existing systems. Unlike previous works, this thesis proposes solutions to mitigating soft-biometric driven bias and privacy concerns in face recognition systems that are easily-integrable in existing systems and aim for more comprehensive mitigation, not limited to pre-defined demographic attributes. This aims at enhancing the reliability, trust, and dissemination of these systems. The first part of this work provides in-depth investigations on soft-biometric driven bias and privacy concerns in face recognition over a wide range of soft-biometric attributes. The findings of these investigations guided the development of the proposed solutions. The investigations showed that a high number of soft-biometric and privacy-sensitive attributes are encoded in face representations. Moreover, the presence of these soft-biometric attributes strongly influences the behaviour of face recognition systems. This demonstrates the strong need for more comprehensive privacy-enhancing and bias-mitigating technologies that are not limited to pre-defined (demographic) attributes. Guided by these findings, this work proposes solutions for mitigating bias in face recognition systems and solutions for the enhancement of soft-biometric privacy in these systems. The proposed bias-mitigating solutions operate on the comparison- and scorelevel of recognition system and thus, can be easily integrated. Incorporating the notation of individual fairness, that aims at treating similar individuals similarly, strongly mitigates bias of unknown origins and further improves the overall-recognition performance of the system. The proposed solutions for enhancing the soft-biometric privacy in face recognition systems either manipulate existing face representations directly or changes the representation type including the inference process for verification. The manipulation of existing face representations aims at directly suppressing the encoded privacy-risk information in an easily-integrable manner. Contrarily, the inference-level solutions indirectly suppress this privacy-risk information by changing the way of how this information is encoded. To summarise, this work investigates soft-biometric driven bias and privacy concerns in face recognition systems and proposed solutions to mitigate these. Unlike previous works, the proposed approaches are (a) highly effective in mitigating these concerns, (b) not limited to the mitigation of concerns origin from specific attributes, and (c) easilyintegrable into existing systems. Moreover, the presented solutions are not limited to face biometrics and thus, aim at enhancing the reliability, trust, and dissemination of biometric systems in general.
On Soft-Biometric Information Stored in Biometric Face Embeddings
IEEE Transactions on Biometrics, Behavior, and Identity Science
The success of modern face recognition systems is based on the advances of deeply-learned features. These embeddings aim to encode the identity of an individual such that these can be used for recognition. However, recent works have shown that more information beyond the user’s identity is stored in these embeddings, such as demographics, image characteristics, and social traits. This raises privacy and bias concerns in face recognition. We investigate the predictability of 73 different soft-biometric attributes on three popular face embeddings with different learning principles. The experiments were conducted on two publicly available databases. For the evaluation, we trained a massive attribute classifier such that can accurately state the confidence of its predictions. This enables us to derive more sophisticated statements about the attribute predictability. The results demonstrate that the majority of the investigated attributes are encoded in face embeddings. For instance, a strong encoding was found for demographics, haircolors, hairstyles, beards, and accessories. Although face recognition embeddings are trained to be robust against non-permanent factors, we found that specifically these attributes are easily-predictable from face embeddings. We hope our findings will guide future works to develop more privacy-preserving and bias-mitigating face recognition technologies.
Privacy-Enhancing Face Biometrics: A Comprehensive Survey
IEEE Transactions on Information Forensics and Security
Biometric recognition technology has made significant advances over the last decade and is now used across a number of services and applications. However, this widespread deployment has also resulted in privacy concerns and evolving societal expectations about the appropriate use of the technology. For example, the ability to automatically extract age, gender, race, and health cues from biometric data has heightened concerns about privacy leakage. Face recognition technology, in particular, has been in the spotlight, and is now seen by many as posing a considerable risk to personal privacy. In response to these and similar concerns, researchers have intensified efforts towards developing techniques and computational models capable of ensuring privacy to individuals, while still facilitating the utility of face recognition technology in several application scenarios. These efforts have resulted in a multitude of privacy–enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative requirements set forth in privacy laws and regulations, such as GDPR. The goal of this overview paper is to provide a comprehensive introduction into privacy–related research in the area of biometrics and review existing work on Biometric Privacy–Enhancing Techniques (B–PETs) applied to face biometrics. To make this work useful for as wide of an audience as possible, several key topics are covered as well, including evaluation strategies used with B–PETs, existing datasets, relevant standards, and regulations and critical open issues that will have to be addressed in the future.
Towards Quality-Aware Face Recognition
Darmstadt, TU, Bachelor Thesis, 2021
Recently, face recognition systems have reached near-perfect levels of performance on face verification tasks with images of high face image quality, which is the utility of a face image for recognition purposes. However, in the real world, images can vary greatly in quality. Some images may have the face partially occluded, other images might depict the subject in profile, and some images might be of low resolution. Due to these inherent variabilities, face recognition systems perform much worse in uncontrolled environments. In order to alleviate this issue, previous works focused on verification with sets of images for each person. By using the quality of each image in a weighted average, the output of each image is combined into one, placing a larger emphasis on high-quality images. However, not much research has been done into the effectiveness of directly incorporating image quality into the similarity metric, which would also allow quality-aware comparisons with single images. In this work, we propose a quality-aware similarity score, which uses MagFace  features and qualities as a basis. We construct a quality-aware similarity score by adapting the standard cosine similarity score with a model-specific quality value. This adaptation is based on a linear weight function of the cosine similarity score, which controls how the quality influences the quality-aware similarity score. We show that training the linear weight function is an optimization problem, which can be robustly solved with a computationally low-effort brute force approach. By incorporating the quality into our similarity score, we can improve the system’s capability to handle low-quality images. We prove the effectiveness of this approach by testing on a variety of face recognition benchmarks, including cross-age, cross-pose, cross-quality, and general unconstrained datasets. Additionally, we show robustness in training by providing a comparison of results when training on different datasets. The results show an improvement over the baseline in 14 out of 16 benchmarks. Moreover, the proposed approach beats state-of-the-art on several single image face recognition benchmarks such as AgeDB , CALFW , CFP-FP , CPLFW , and XQLFW  with a verification accuracy of 98.50%, 96.12%, 98.74%, 93.50% and 83.95% respectively. Additionally, we also achieve state-of-the-art performance on the video-based multi-media benchmarks IJB-B  and IJB-C , for the different FMRs 10−2, 10−3, 10−4 and 10−2, 10−3.
Towards Unsupervised Fingerprint Image Quality Assessment
Darmstadt, TU, Bachelor Thesis, 2021
Fingerprint matching is one of the most popular and reliable biometric techniques used in automatic verification of a person. During the verification process of a fingerprint, the areas and in more detail the information in these areas (minutiae) of the fingerprint are compared with the corresponding information of a second fingerprint, which is called fingerprint matching. However, the quality of the fingerprint images highly affects the recognition process. Generally, the biometric sample quality is defined on its impact on a biometric recognition system, the similarity between the sample and its source, and the quality of its physical features. Poor quality fingerprints often have areas or regions that are not clear or even missing, resulting in arbitrary changes in the structure of these areas. That results in an inaccurate matching caused by comparisons of damaged areas, which leads to inaccurate matching influencing the verification results again. That is the reason why assessing the fingerprint image quality is very important because the matching performance could be significantly affected by poor quality samples. One application area that is very much affected by this problem is forensics. Forensics often deal with partial fingerprints lifted from a surface, known as latent fingerprints. Since latent fingerprints are often of poor quality, the matching performance during the recognition process is often severely impaired. Quality assessment can be used to improve biometric systems that perform automatic recognition tasks like identification or verification. Quality estimation during the enrollment process is used to ensure the best possible quality of the biometric data, thus guaranteeing a good training of the biometric systems as well as a good performance. This is a typical reason why image quality assessment is required to evaluate the quality of the images and improve the recognition process. In this thesis, two new methods are proposed to accurately assess (a) the quality of a single minutia and (b) the quality of a fingerprint. The proposed minutia quality is based on its detection reliability. A minutia is classified by randomly generated subnetworks of a minutiae classifier that determines if a minutia is a true minutia or not. The various classification results are used to determine a robustness score, considered as the detection reliability. The proposed fingerprint quality assessment method applies a stochastic method to the detection reliabilities of the minutiae to determine the quality of the fingerprint. Since this thesis addresses these two problems, both kinds of quality assessments have to be evaluated separately. The proposed minutia quality assessment method (a) is compared with Mindtct. For evaluation purposes of (b), the proposed method is compared with the current state-of-the-art quality assessment method NFIQ2 as well as its predecessor NFIQ. All experiments are evaluated on the FVC2006 database using the Bozorth3 and MCC fingerprint matchers. It can be shown that the proposed method assesses the quality on minutiae-level (a) just as good and better as Mindtct on the most experiments without the need of handcrafted quality labels. Experiments on data acquired from an electrical field sensor, for example, show that the proposed method achieves on average a 0.03 lower FNMR at a FMR of 10−2 using Bozorth3. Furthermore, the quality assessment on fingerprint-level (b) outperforms the state-of-the-art quality assessment methods NFIQ2 and NFIQ. An improvement of the recognition performance on all databases captured by real sensor types could be achieved. Observations of experimental results at a FMR of 10−2 using Bozorth3 show that the proposed method achieves a FNMR that is about 0.003 lower than the FNMR achieved by NFIQ2 on optical sensor data after rejecting the 20% worst fingerprints. Furthermore, a 0.025 lower FNMR can also be achieved on data captured by a thermal sweeping sensor.
Anomaly-based Face Search
Darmstadt, TU, Bachelor Thesis, 2020
Biometric face identification refers to the use of face images for the automatic identification of individuals. Due to the high performance achieved by current face search algorithms, these algorithms are useful tools, e.g. in criminal investigations. Based on the facial description of a witness, the number of suspects can be significantly reduced. However, while modern face image retrieval approaches either require an accurate verbal description or an example image of the suspect’s face, eyewitness testimonies can seldom provide this level of detail. Moreover, while eyewitness’ recall is one of the most convincing pieces of evidence, it is also one of the most unreliable. Hence, exploiting the more reliable, but vague memories about distinctive facial features directly, such as obvious tattoos, scars or birthmarks, should be considered to filter potential suspects in a first step. This might reduce the risk of wrongful convictions caused by retroactively inferred details in the witness’ recall for subsequent steps. Therefore, this thesis proposes an anomaly-based face search solution that aims at enabling a reduction of the search space solely based on locations of anomalous facial features. We developed an unsupervised image anomaly detection approach based on a cascaded image completion network that allows to roughly localize anomalous regions in face images. (1) This completion model is assumed to fill in deleted regions with probable values conditioned on all the remaining parts of the face image. (2) The reconstruction errors of this model were used as an anomaly signal to create a grid of potential anomaly locations in a given face image. (3) These grids, in the form of a thresholded matrix, were then subsequently used to search for the most relevant images. We evaluated the respective retrieval model on a preprocessed subset of 17.855 images of the VGGFace2 dataset. The three main contributions of this work are (1) a cascaded face image completion approach, (2) an unsupervised inpainting-based anomaly localization approach, and (3) a query-by-anomaly face image retrieval approach. The face inpainting achieved promising results when compared to other recent completion approaches since we didn’t leverage any adversarial component in order to simplify the entire training procedure. These inpaintings enabled to roughly localize anomalies in face images. The proposed retrieval model achieved a 60% hit rate at a penetration rate of about 20% over a gallery of 17.855 images. Despite the limitations of the proposed searching approach, the results revealed the potential benefits of using the more reliable anomaly information to reduce the search space, instead of entirely relying on the elicitation of detailed perpetrator descriptions, either in textual or in visual form.
Beyond Identity: What Information Is Stored in Biometric Face Templates ?
IJCB 2020. IEEE/IARP International Joint Conference on Biometrics
IEEE/IARP International Joint Conference on Biometrics (IJCB) <2020, online>
Deeply-learned face representations enable the success of current face recognition systems. Despite the ability of these representations to encode the identity of an individual, recent works have shown that more information is stored within, such as demographics, image characteristics, and social traits. This threatens the user's privacy, since for many applications these templates are expected to be solely used for recognition purposes. Knowing the encoded information in face templates helps to develop bias-mitigating and privacy-preserving face recognition technologies. This work aims to support the development of these two branches by analysing face templates regarding 113 attributes. Experiments were conducted on two publicly available face embeddings. For evaluating the predictability of the attributes, we trained a massive attribute classifier that is additionally able to accurately state its prediction confidence. This allows us to make more sophisticated statements about the attribute predictability. The results demonstrate that up to 74 attributes can be accurately predicted from face templates. Especially non-permanent attributes, such as age, hairstyles, haircolors, beards, and various accessories, found to be easily-predictable. Since face recognition systems aim to be robust against these variations, future research might build on this work to develop more understandable privacy preserving solutions and build robust and fair face templates.
Comparison-Level Mitigation of Ethnic Bias in Face Recognition
IWBF 2020. Proceedings
International Workshop on Biometrics and Forensics (IWBF) <8, 2020, online>
Current face recognition systems achieve high performance on several benchmark tests. Despite this progress,recent works showed that these systems are strongly biasedagainst demographic sub-groups. Previous works introducedapproaches that aim at learning less biased representations.However, applying these approaches in real applications requiresa complete replacement of the templates in the database. Thisreplacement procedure further requires that a face image ofeach enrolled individual is stored as well. In this work, wepropose the first bias-mitigating solution that works on thecomparison-level of a biometric system. We propose a fairnessdriven neural network classifier for the comparison of twobiometric templates to replace the systems similarity function.This fair classifier is trained with a novel penalization termin the loss function to introduce the criteria of group andindividual fairness to the decision process. This penalization termforces the score distributions of different ethnicities to be similar,leading to a reduction of the intra-ethnic performance differences.Experiments were conducted on two publicly available datasetsand evaluated the performance of four different ethnicities. Theresults showed that for both fairness criteria, our proposedapproach is able to significantly reduce the ethnic bias, whileit preserves a high recognition ability. Our model, build onindividual fairness, achieves bias reduction rate between 15.35%and 52.67%. In contrast to previous work, our solution is easy tointegrate into existing systems by simply replacing the systemssimilarity functions with our fair template comparison approach.
Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition
IJCB 2020. IEEE/IARP International Joint Conference on Biometrics
IEEE/IARP International Joint Conference on Biometrics (IJCB) <2020, online>
Face quality assessment aims at estimating the utility of a face image for the purpose of recognition. It is a key factor to achieve high face recognition performances. Currently, the high performance of these face recognition systems come with the cost of a strong bias against demographic and non-demographic sub-groups. Recent work has shown that face quality assessment algorithms should adapt to the deployed face recognition system, in order to achieve highly accurate and robust quality estimations. However, this could lead to a bias transfer towards the face quality assessment leading to discriminatory effects e.g. during enrolment. In this work, we present an in-depth analysis of the correlation between bias in face recognition and face quality assessment. Experiments were conducted on two publicly available datasets captured under controlled and uncontrolled circumstances with two popular face embed-dings. We evaluated four state-of-the-art solutions for face quality assessment towards biases to pose, ethnicity, and age. The experiments showed that the face quality assessment solutions assign significantly lower quality values towards subgroups affected by the recognition bias demonstrating that these approaches are biased as well. This raises ethical questions towards fairness and discrimination which future works have to address.
Learning Privacy-Enhancing Face Representations through Feature Disentanglement
15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020). Proceedings
International Conference on Automatic Face and Gesture Recognition (FG) <15, 2020, Buenos Aires, Argentina>
Convolutional Neural Networks (CNNs) are today the de-facto standard for extracting compact and discriminative face representations (templates) from images in automatic face recognition systems. Due to the characteristics of CNN models, the generated representations typically encode a multitude of information ranging from identity to soft-biometric attributes, such as age, gender or ethnicity. However, since these representations were computed for the purpose of identity recognition only, the soft-biometric information contained in the templates represents a serious privacy risk. To mitigate this problem, we present in this paper a privacy-enhancing approach capable of suppressing potentially sensitive soft-biometric information in face representations without significantly compromising identity information. Specifically, we introduce a Privacy-Enhancing Face-Representation learning Network (PFRNet) that disentangles identity from attribute information in face representations and consequently allows to efficiently suppress soft-biometrics in face templates. We demonstrate the feasibility of PFRNet on the problem of gender suppression and show through rigorous experiments on the CelebA, Labeled Faces in the Wild (LFW) and Adience datasets that the proposed disentanglement-based approach is highly effective and improves significantly on the existing state-of-the-art.
PE-MIU: A Training-Free Privacy-Enhancing Face Recognition Approach Based on Minimum Information Units
Research on soft-biometrics showed that privacy-sensitive information can be deduced from biometric data. Utilizing biometric templates only, information about a persons gender, age, ethnicity, sexual orientation, and health state can be deduced. For many applications, these templates are expected to be used for recognition purposes only. Thus, extracting this information raises major privacy issues. Previous work proposed two kinds of learning-based solutions for this problem. The first ones provide strong privacy-enhancements, but limited to pre-defined attributes. The second ones achieve more comprehensive but weaker privacy-improvements. In this work, we propose a Privacy-Enhancing face recognition approach based on Minimum Information Units (PE-MIU). PE-MIU, as we demonstrate in this work, is a privacy-enhancement approach for face recognition templates that achieves strong privacy-improvements and is not limited to pre-defined attributes. We exploit the structural differences between face recognition and facial attribute estimation by creating templates in a mixed representation of minimal information units. These representations contain pattern of privacy-sensitive attributes in a highly randomized form. Therefore, the estimation of these attributes becomes hard for function creep attacks. During verification, these units of a probe template are assigned to the units of a reference template by solving an optimal best-matching problem. This allows our approach to maintain a high recognition ability. The experiments are conducted on three publicly available datasets and with five state-of-the-art approaches. Moreover, we conduct the experiments simulating an attacker that knows and adapts to the systems privacy mechanism. The experiments demonstrate that PE-MIU is able to suppress privacy-sensitive information to a significantly higher degree than previous work in all investigated scenarios. At the same time, our solution is able to achieve a verification performance close to that of the unmodified recognition system. Unlike previous works, our approach offers a strong and comprehensive privacy-enhancement without the need of training.
Post-comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization
Pattern Recognition Letters
Current face recognition systems achieve high progress on several benchmark tests. Despite this progress, recent works showed that these systems are strongly biased against demographic sub-groups. Consequently, an easily integrable solution is needed to reduce the discriminatory effect of these biased systems. Previous work mainly focused on learning less biased face representations, which comes at the cost of a strongly degraded overall recognition performance. In this work, we propose a novel unsupervised fair score normalization approach that is specifically designed to reduce the effect of bias in face recognition and subsequently lead to a significant overall performance boost. Our hypothesis is built on the notation of individual fairness by designing a normalization approach that leads to treating “similar” individuals “similarly”. Experiments were conducted on three publicly available datasets captured under controlled and in-the-wild circumstances. Results demonstrate that our solution reduces demographic biases, e.g. by up to 82.7% in the case when gender is considered. Moreover, it mitigates the bias more consistently than existing works. In contrast to previous works, our fair normalization approach enhances the overall performance by up to 53.2% at false match rate of 10−3 and up to 82.9% at a false match rate of 10−5. Additionally, it is easily integrable into existing recognition systems and not limited to face biometrics.
Privacy Evaluation Protocols for the Evaluation of Soft-Biometric Privacy-Enhancing Technologies
Conference on Biometrics and Electronic Signatures (BIOSIG) <19, 2020, Online>
GI-Edition - Lecture Notes in Informatics (LNI), P-306
Biometric data includes privacy-sensitive information, such as soft-biometrics. Soft-biometric privacy enhancing technologies aim at limiting the possibility of deducing such information. Previous works proposed several solutions to this problem using several different evaluation processes, metrics, and attack scenarios. The absence of a standardized evaluation protocol makes a meaningful comparison of these solutions difficult. In this work, we propose privacy evaluation protocols (PEPs) for privacy-enhancing technologies (PETs) dealing with soft-biometric privacy. Our framework evaluates PETs in the most critical scenario of an attacker that knows and adapts to the systems privacy-mechanism. Moreover, our PEPs differentiate between PET of learning-based or training-free nature. To ensure that our protocol meets the highest standards in both cases, it is based on Kerckhoffs‘s principle of cryptography.
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) <2020, online>
Face image quality is an important factor to enable high-performance face recognition systems. Face quality assessment aims at estimating the suitability of a face image for the purpose of recognition. Previous work proposed supervised solutions that require artificially or human labelled quality values. However, both labelling mechanisms are error prone as they do not rely on a clear definition of quality and may not know the best characteristics for the utilized face recognition system. Avoiding the use of inaccurate quality labels, we proposed a novel concept to measure face quality based on an arbitrary face recognition model. By determining the embedding variations generated from random subnetworks of a face model, the robustness of a sample representation and thus, its quality is estimated. The experiments are conducted in a cross-database evaluation setting on three publicly available databases. We compare our proposed solution on two face embeddings against six state-of-the-art approaches from academia and industry. The results show that our unsupervised solution outperforms all other approaches in the majority of the investigated scenarios. In contrast to previous works, the proposed solution shows a stable performance over all scenarios. Utilizing the deployed face recognition model for our face quality assessment methodology avoids the training phase completely and further outperforms all baseline approaches by a large margin. Our solution can be easily integrated into current face recognition systems, and can be modified to other tasks beyond face recognition.
Detecting Face Morphing Attacks by Analyzing the Directed Distances of Facial Landmarks Shifts
German Conference on Pattern Recognition (GCPR) <40, 2018, Stuttgart, Germany>
Lecture Notes in Computer Science (LNCS), 11269
Face morphing attacks create face images that are verifiable to multiple identities. Associating such images to identity documents lead to building faulty identity links, causing attacks on operations like border crossing. Most of previously proposed morphing attack detection approaches directly classified features extracted from the investigated image. We discuss the operational opportunity of having a live face probe to support the morphing detection decision and propose a detection approach that take advantage of that. Our proposed solution considers the facial landmarks shifting patterns between reference and probe images. This is represented by the directed distances to avoid confusion with shifts caused by other variations. We validated our approach using a publicly available database, built on 549 identities. Our proposed detection concept is tested with three landmark detectors and proved to outperform the baseline concept based on handcrafted and transferable CNN features.
Enhancing the Privacy of Face Recognition and its Representations
Darmstadt, TU, Master Thesis, 2019
For these reasons, this work aims at preventing unauthorized deduction of private softbiometriccharacteristics from image representations. Latent features should be extractedfrom facial images, so that sparse feature representations are obtained. The featurerepresentations should be transformed in a way, that the predictive performance of softbiometricestimators is reduced. Biometric systems should still be able to recognize anindividual using the transformed representations.These objectives are achieved by the main contribution, the Thomson loss, that is presentedin this work. By using the Thomson loss a neural network learns a transformation that canbe applied to feature representations of facial images. After the feature representationshave been transformed, even non-binary soft-biometric estimators cannot make reliablepredictions anymore.
Exploring the Channels of Multiple Color Spaces for Age and Gender Estimation from Face Images
International Conference on Information Fusion (FUSION) <22, 2019, Ottawa, Canada>
Soft biometrics identify certain traits of individuals based on their sampled biometric characteristics. The automatic identification of traits like age and gender provides valuable information in applications ranging from forensics to service personalization. Color images are stored within a color space containing different channels. Each channel represents a different portion of the information contained in the image, including these of soft biometric patterns. The analysis of the age and gender information in the different channels and different color spaces was not previously studied. This work discusses the soft biometric performances using these channels and analyzes the sample error overlap between all possible channels to successfully prove that different information is considered in the decision making from each channel. We also present a multi-channel selection protocols and fusion solution of the selected channels. Beside the analyzes of color spaces and their channels, our proposed multi-channel fusion solution extends beyond state-of-the-art performance in age estimation on the widely used Adience dataset.
How Do Demographic Soft-Biometric Attributes Affect Kinship Verification ?
Darmstadt, TU, Bachelor Thesis, 2019
In recent years, facial kinship verification has received considerable attention due to the easy acquisition of facial images and a large potential application area. Facial kinship verification is defined as the process to determine whether two identities are kin or not by automatically comparing their facial images. Facial kinship verification may have a wide range of potential uses, including aiding in the fight against human trafficking, handling conflicts resulting from the refugee crisis, family album organization, and social media analysis. Other potential applications lie in the academic field, such as genealogical studies and in the identification of the kin of victims or suspects by law enforcement , . In Germany, from March 1951 to April 2019, a total of 1995 cases of missing children are unresolved as reported by the Bundeskriminalamt . Due to the significant change in the look of children at adult age, the high similarity of a child’s appearance to their parents, and the much easier acquisition of photos than DNA, facial kinship verification could help resolve these, and similar cases. Unfortunately, the performance of such kinship verification systems is still too underdeveloped to be used for real-world applications . One issue consists of the non-generalizability of currently available data sets to the real-world data distribution . Lopez et al. received an acceptable accuracy on two data sets by only comparing the chrominance . Inspired by this, Dawson et al. built a “From Same Photo” classifier to compete for the kinship verification task by only assigning those pictures as kin which originated from the same photo . As another trait, Guo et al. included gender and age as information in the kinship verification process by only considering this information to determine whether a person of the potential kin pair is older or the age is approximately the same . Although age and ethnicity have not yet been explicitly implemented into the kinship verification process, the present thesis analyzes the impact of gender and these attributes on the kinship verification process. Accordingly, two widely used data sets were labeled manually with gender, age, and ethnicity. The impact of the addition of these traits to the baseline model was then analyzed. These additional traits could improve the accuracy of the kinship verification process slightly. Only a softbiometric classifier, including gender, age, and ethnicity, was built. A significant fraction of the kinship verification process may be explained solely by these attributes because of an inappropriate data set composition. Moreover, an incorrect construction in the two analyzed data sets can be found, which evoked majorly from the same pictures and the same identities in different folds. Understanding the shortcomings of previously conducted research can help future researchers improve their development of the kinship verification process.
Minutiae-Based Gender Estimation for Full and Partial Fingerprints of Arbitrary Size and Shape
Computer Vision - ACCV 2018
Asian Conference on Computer Vision (ACCV) <14, 2018, Perth, Australia>
Lecture Notes in Computer Science (LNCS), 11361
Since fingerprints are one of the most widely deployed biometrics, accurate fingerprint gender estimation can positively affect several applications. For example, in criminal investigations, gender classification may significantly minimize the list of potential subjects. Previous work mainly offered solutions for the task of gender classification based on complete fingerprints. However, partial fingerprint captures are frequently occurring in many applications, including forensics and the fast growing field of consumer electronics. Due to its huge variability in size and shape, gender estimation on partial fingerprints is a challenging problem. Therefore, in this work we propose a flexible gender estimation scheme by building a gender classifier based on an ensemble of minutiae. The outputs of the single minutia gender predictions are combined by a novel adjusted score fusion approach to obtain an enhanced gender decision. Unlike classical solutions this allows to deal with unconstrained fingerprint parts of arbitrary size and shape. We performed investigations on a publicly available database and our proposed solution proved to significantly outperform state-of-the-art approaches on both full and partial fingerprints. The experiments indicate a reduction in the gender estimation error by 19.34% on full fingerprints and 28.33% on partial captures in comparison to previous work.
Mitigating Ethnic Bias in Face Recognition Models through Fair Template Comparison
Darmstadt, TU, Master Thesis, 2019
Face recognition systems find many uses in daily life. For example, they can be used to unlock your phone or automatically tag a person in a photo but they are also used in other application fields such as in security environments or surveillance. However, there is a significant problem with these systems: they are often biased. These systems make much more mistakes on women and darker-skinned people than on men and light-skinned people. This bias comes from data which is heavily skewed towards light-skinned men and the systems learn from these data, reflecting this bias. As face recognition systems become more prevalent, solving this problem increasingly gains importance, especially when these mistakes can have a large impact, such as when they are used for identifying criminals but entire groups of persons are discriminated. The important question is: How can the bias be reduced as much as possible so that the systems get fairer while maintaining a sufficient recognition performance? There are several ways to tackle bias. Previous approaches tried to introduce balanced datasets or remove features which may lead to a bias. However, often they have to deal with the challenge of providing enough data for a balanced dataset or with performance drops. This is especially true for minority groups, as it is intrinsically hard to collect more data for them. Therefore, there exists an even stronger bias against minority groups. In this thesis, the focus is on reducing the ethnic bias of facial recognition systems through a fair template comparison method: We propose applying two different fairness concepts during the training of template comparison models by adding them as penalization terms to the loss function. The first concept, group fairness, aims at equalizing groups while the second concept, individual fairness, aims at equal treatment for similar individuals. Our approach is evaluated on two different datasets. The template comparison is realized as logistic regression and neural network models. The experiments show not only the influence of the fairness terms but also that it is possible to achieve a fairer system without a significant face recognition performance drop.
Multi-algorithmic Fusion for Reliable Age and Gender Estimation from Face Images
International Conference on Information Fusion (FUSION) <22, 2019, Ottawa, Canada>
Automated estimation of demographic attributes, such as gender and age, became of great importance for many potential applications ranging from forensics to social media. Although previous works reported performances that closely match human level. These solutions lack of human intuition that allows human beings to state the confidences of their predictions. While the human intuition subconsciously considers surrounding conditions or the lack of experience in a certain task, current algorithmic solutions tend to mispredict with high confidence scores. In this work, we propose a multi-algorithmic fusion approach for age and gender estimation that is able to accurately state the model’s prediction reliability. Our solution is based on stochastic forward passes through a dropout-reduced neural network ensemble. By utilizing multiple stochastic forward passes combined from the neural network ensemble, the centrality and dispersion of these predictions are used to derive a confidence statement about the prediction. Our experiments were conducted on the Adience benchmark.We showed that the proposed solution reached and exceeded state-of-the-art performance for the age and gender estimation tasks. Further, we demonstrated that the reliability statements of the predictions of our proposed solution capture challenging conditions and underrepresented training samples.
Reliable Age and Gender Estimation from Face Images: Stating the Confidence of Model Predictions
IEEE 10th International Conference on Biometrics: Theory, Applications and Systems
IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) <10, 2019, Tampa, Florida, USA>
Automated age and gender estimation became of great importance for many potential applications ranging from forensics to social media. Although previous works reported high increased performances, these solutions tend to mispredict under challenging conditions or when the trained model faces a sample that was underrepresented in the training data. In this work, we propose an age and gender estimation model, as well as a novel reliability measure to quantify the confidence of the model’s prediction. Our solution is based on stochastic forward passes through dropout-reduced neural networks that were theoretically proven to approximate Gaussian processes. By utilizing multiple stochastic forward passes, the centrality and dispersion of these predictions are used to derive a confidence statement about the prediction. Experiments were conducted on the Adience benchmark. We showed that the proposed solution reached and exceeded state-ofthe-art performance. Further, we demonstrated that the proposed reliability measure correlates with the prediction performance and thus, is highly successful in quantifying the prediction reliability.
Suppressing Gender and Age in Face Templates Using Incremental Variable Elimination
The 12th IAPR International Conference On Biometrics
IAPR International Conference on Biometrics (ICB) <12, 2019, Crete, Greece>
Recent research on soft-biometrics showed that more information than just the person’s identity can be deduced from biometric data. Using face templates only, information about gender, age, ethnicity, health state of the person, and even the sexual orientation can be automatically obtained. Since for most applications these templates are expected to be used for recognition purposes only, this raises major privacy issues. Previous work addressed this problem purely on image level regarding function creep attackers without knowledge about the systems privacy mechanism. In this work, we propose a soft-biometric privacy enhancing approach that reduces a given biometric template by eliminating its most important variables for predicting soft-biometric attributes. Training a decision tree ensemble allows deriving a variable importance measure that is used to incrementally eliminate variables that allow predicting sensitive attributes. Unlike previous work, we consider a scenario of function creep attackers with explicit knowledge about the privacy mechanism and evaluated our approach on a publicly available database. The experiments were conducted to eight baseline solutions. The results showed that in many cases IVE is able to suppress gender and age to a high degree with a negligible loss of the templates recognition ability. Contrary to previous work, which is limited to the suppression of binary (gender) attributes, IVE is able, by design, to suppress binary, categorical, and continuous attributes.
To Detect or not to Detect: The Right Faces to Morph
The 12th IAPR International Conference On Biometrics
IAPR International Conference on Biometrics (ICB) <12, 2019, Crete, Greece>
Recent works have studied the face morphing attack detection performance generalization over variations in morphing approaches, image re-digitization, and image source variations. However, these works assumed a constant approach for selecting the images to be morphed (pairing) across their training and testing data. A realistic variation in the pairing protocol in the training data can result in challenges and opportunities for a stable attack detector. This work extensively study this issue by building a novel database with three different pairing protocols and two different morphing approaches. We study the detection generalization over these variations for single image and differential attack detection, along with handcrafted and CNNbased features. Our observations included that training an attack detection solution on attacks created from dissimilar face images, in contrary to the common practice, can result in an overall more generalized detection performance. Moreover, we found that differential attack detection is very sensitive to variations in morphing and pairing protocols.
Unsupervised Privacy-enhancement of Face Representations Using Similarity-sensitive Noise Transformations
Face images processed by a biometric system are expected to be used for recognition purposes only. However, recent work presented possibilities for automatically deducing additional information about an individual from their face data. By using soft-biometric estimators, information about gender, age, ethnicity, sexual orientation or the health state of a person can be obtained. This raises a major privacy issue. Previous works presented supervised solutions that require large amount of private data in order to suppress a single attribute. In this work, we propose a privacy-preserving solution that does not require these sensitive information and thus, works in an unsupervised manner. Further, our approach offers privacy protection that is not limited to a single known binary attribute or classifier. We do that by proposing similarity-sensitive noise transformations and investigate their effect and the effect of dimensionality reduction methods on the task of privacy preservation. Experiments are done on a publicly available database and contain analyses of the recognition performance, as well as investigations of the estimation performance of the binary attribute of gender and the continuous attribute of age. We further investigated the estimation performance of these attributes when the prior knowledge about the used privacy mechanism is explicitly utilized. The results show that using this information leads to significantly enhancement of the estimation quality. Finally, we proposed a metric to evaluate the trade-off between the privacy gain and the recognition loss for privacy-preservation techniques. Our experiments showed that the proposed cosine-sensitive noise transformation was successful in reducing the possibility of estimating the soft private information in the data, while having significantly smaller effect on the intended recognition performance.
CrazyFaces: Unassisted Circumvention of Watchlist Face Identification
IEEE 9th International Conference on Biometrics: Theory, Applications and Systems
IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) <9, 2018, Redondo Beach, CA, USA>
Once upon a time, there was a blacklisted criminal who usually avoided appearing in public. He was surfing the Web, when he noticed, what had to be a targeted advertisement announcing a concert of his favorite band. The concert was in a near town, and the only way to get there was by train. He was worried, because he heard in the news about the new face identification system installed at the train station. From his last stay with the police, he remembers that they took these special face images with the white background. He thought about what can he do to avoid being identified and an idea popped in his mind “what if I can make a crazy-face, as the kids call it, to make my face look different? What do I exactly have to do? And will it work?”. He called his childhood geeky friend and asked him if he can build him a face recognition application he can tinker with. The geeky friend was always interested in such small projects where he can use open-source resources and didn’t really care about the goal, as usual. The criminal tested the application and played around, trying to figure out how can he make a crazy-face that won’t be identified as himself. On the day of the concert, he took off to the train station with some doubt in his mind and fear in his soul. To know what happened next, you should read the rest of this paper.
Deep and Multi-algorithmic Gender Classification of Single Fingerprint Minutiae
International Conference on Information Fusion (FUSION) <21, 2018, Cambridge, UK>
Accurate fingerprint gender estimation can positively affect several applications, since fingerprints are one of the most widely deployed biometrics. For example, gender classification in criminal investigations may significantly minimize the list of potential subjects. Previous work mainly offered solutions for the task of gender classification based on complete fingerprints. However, partial fingerprint captures are frequently occurring in many applications, including forensics and the fast growing field of consumer electronics. Moreover, partial fingerprints are not well-defined. Therefore, this work improves the gender decision performance on a well-defined partition of the fingerprint. It enhances gender estimation on the level of a single minutia. Working on this level, we propose three main contributions that were evaluated on a publicly available database. First, a convolutional neural network model is offered that outperformed baseline solutions based on hand crafted features. Second, several multi-algorithmic fusion approaches were tested by combining the outputs of different gender estimators that help further increase the classification accuracy. Third, we propose including minutia detection reliability in the fusion process, which leads to enhancing the total gender decision performance. The achieved gender classification performance of a single minutia is comparable to the accuracy that previous work reported on a quarter of aligned fingerprints including more than 25 minutiae.
Fingerprint and Iris Multi-biometric Data Indexing and Retrieval
International Conference on Information Fusion (FUSION) <21, 2018, Cambridge, UK>
Indexing of multi-biometric data is required to facilitatefast search in large-scale biometric systems. Previous worksaddressing this issue in multi-biometric databases focused onmulti-instance indexing, mainly iris data. Few works addressedthe indexing in multi-modal databases, with basic candidate listfusion solutions limited to joining face and fingerprint data. Irisand fingerprint are widely used in large-scale biometric systemswhere fast retrieval is a significant issue. This work proposes jointmulti-biometric retrieval solution based on fingerprint and irisdata. This solution is evaluated under eight different candidatelist fusion approaches with variable complexity on a databaseof 10,000 reference and probe records of irises and fingerprints.Our proposed multi-biometric retrieval of fingerprint and irisdata resulted in a reduction of the miss rate (1- hit rate) at 0.1%penetration rate by 93% compared to fingerprint indexing and88% compared to iris indexing.
P-score: Performance Aligned Normalization and an Evaluation in Score-level Multi-biometric Fusion
2018 Proceedings of the 26th European Signal Processing Conference (EUSIPCO)
European Signal Processing Conference (EUSIPCO) <26, 2018, Rome, Italy>
Normalization is an important step for different fusion, classification, and decision making applications. Previous normalization approaches considered bringing values from different sources into a common range or distribution characteristics. In this work we propose a new normalization approach that transfers values into a normalized space where their relative performance in binary decision making is aligned across their whole range. Multi-biometric verification is a typical problem where information from different sources are normalized and fused to make a binary decision and therefore a good platform to evaluate the proposed normalization.We conducted an evaluation on two publicly available databases and showed that the normalization solution we are proposing consistently outperformed state-of-the-art and best practice approaches, e.g. by reducing the false rejection rate at 0.01% false acceptance rate by 60- 75% compared to the widely used z-score normalization under the sum-rule fusion.
What Can a Single Minutia Tell about Gender?
2018 International Workshop on Biometrics and Forensics (IWBF)
International Workshop on Biometrics and Forensics (IWBF) <2018, Sassari, Italy>
Since fingerprints are one of the most widely deployed biometrics, several applications can benefit from an accurate fingerprint gender estimation. Previous work mainly tackled the task of gender estimation based on complete fingerprints. However, partial fingerprint captures are frequently occurring in many applications including forensics and consumer electronics, with the considered ratio of the fingerprint is variable. Therefore, this work investigates gender estimation on a small, detectable, and well-defined partition of a fingerprint. It investigates gender estimation on the level of a single minutia. Working on this level, we propose a feature extraction process that is able to deal with the rotation and translation invariance problems of fingerprints. This is evaluated on a publicly available database and with five different binary classifiers. As a result, the information of a single minutia achieves a comparable accuracy on the gender classification task as previous work using quarters of aligned fingerprints with an average of more than 25 minutiae.
Efficient, Accurate, and Rotation-Invariant Iris Code
IEEE Signal Processing Letters
The large scale of the recently demanded biometric systems has put a pressure on creating a more efficient, accurate, and private biometric solutions. Iris biometrics is one of the most distinctive and widely used biometric characteristics. High-performing iris representations suffer from the curse of rotation inconsistency. This is usually solved by assuming a range of rotational errors and performing a number of comparisons over this range, which results in a high computational effort and limits indexing and template protection. This work presents a generic and parameter-free transformation of binary iris representation into a rotation-invariant space. The goal is to perform accurate and efficient comparison and enable further indexing and template protection deployment. The proposed approach was tested on a database of 10 000 subjects of the ISYN1 iris database generated by CASIA. Besides providing a compact and rotational-invariant representation, the proposed approach reduced the equal error rate by more than 55% and the computational time by a factor of up to 44 compared to the original representation.
General Borda Count for Multi-biometric Retrieval
2017 International Joint Conference on Biometrics
IEEE International Joint Conference on Biometrics (IJCB) <2017, Denver, CO, USA>
Indexing of multi-biometric data is required to facilitate fast search in large-scale biometric systems. Previous works addressing this issue were challenged by including biometric sources of different nature, utilizing the knowledge about the biometric sources, and optimizing and tuning the retrieval performance. This work presents a generalized multi-biometric retrieval approach that adapts the Borda count algorithm within an optimizable structure. The approach was tested on a database of 10k reference and probe instances of the left and the right irises. The experiments and comparisons to five baseline solutions proved to achieve advances in terms of general indexing performance, tunability to certain operating points, and response to missing data. A clear advantage of the proposed solution was noticed when faced by candidate lists of low quality.
Indexing of Multi-biometric Databases: Fast and Accurate Biometric Search
Darmstadt, TU, Master Thesis, 2017
Biometrics is a rapidly developing field of research and biometric-based identification systems experience a massive growth all around the world caused by the gaining industrial, government and citizen acceptance. The US-VISIT program uses biometric systems to enforce homeland and border security, whereas in the United Arab Emirates (UAE), biometric systems play a major role in the border control process. Similar, in India, biometrics have gained a great deal of attention, as the Unique Identification Authority of India (UIDAI) have already registered over one billion Indian citizens in the last 7 years (uidai.gov.in). Despite the rapid propagation of large-scale databases, the majority of researchers are still focusing on the matching accuracy of small databases, while neglecting scalability and speed issues. Identity association is usually determined by comparing input data against every entry in the database, which causes computational problems when it comes to large-scale databases. Biometric indexing aims to reduce the number of candidate identities to be considered by an identification system when searching for a match in large biometric databases. However, this is a challenging task since biometric data is fuzzy and does not exhibit any natural sorting order. Current indexing methods are mainly based on tree traversal (using kd-trees, B-trees, R-trees) which suffer from the curse of dimensionality, while other indexing methods are based on hashing, which suffer from pure key generation. The goal of this thesis is to develop an indexing scheme based on multiple biometric modalities. It aims to present the main results of research focusing on iris and fingerprint indexing. Fingerprints are undisputedly the most studied biometric modality that are extensive used in civil and forensic recognition systems. Together with the potential rise of iris recognition accurateness along with enhanced robustness, indexing of this modalities becomes a promising field of research. Different unimodal and multimodal identification approaches have already been proposed in past years. However, most of them trade fast identification rates at the cost of accuracy, while the remaining make use of complex indexing structures, which results in a complete restructuring if insertions or deletions are necessary. This work offers a framework for fast and accurate iris indexing as well as effective indexing schemes to combine multiple modalities. To achieve that, three main contributions are made: First, a new rotation invariant iris representation was developed, reducing the equal error rate by more than 55% and the computation time by a factor up to 44 compared to the original representation. Second, this representation was used to construct an indexing scheme, which reaches a hit rate of 99.7% at 0.1% penetration rate, outperforming state of the art algorithms. And third, a general rank-level indexing fusion scheme was developed to effectively combine multiple sources, achieving over 99.98% hit rate at same penetration rate of 0.1%.
Indexing of Single and Multi-instance Iris Data Based on LSH-Forest and Rotation Invariant Representation
Computer Analysis of Images and Patterns
International Conference on Computer Analysis of Images and Patterns (CAIP) <17, 2017, Ystad, Sweden>
Indexing of iris data is required to facilitate fast search in large-scale biometric systems. Previous works addressing this issue were challenged by the tradeoffs between accuracy, computational efficacy, storage costs, and maintainability. This work presents an iris indexing approach based on rotation invariant iris representation and LSH-Forest to produce an accurate and easily maintainable indexing structure. The complexity of insertion or deletion in the proposed method is limited to the same logarithmic complexity of a query and the required storage grows linearly with the database size. The proposed approach was extended into a multi-instance iris indexing scheme resulting in a clear performance improvement. Single iris indexing scored a hit rate of 99.7% at a 0.1% penetration rate while multi-instance indexing scored a 99.98% hit rate at the same penetration rate. The evaluation of the proposed approach was conducted on a large database of 50k references and 50k probes of the left and the right irises. The advantage of the proposed solution was put into prospective by comparing the achieved performance to the reported results in previous works.