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.
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.
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.
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.