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.
What Can a Single Minutia Tell about Gender?
2018 International Workshop on Biometrics and Forensics (IWBF)
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.