Jointly De-Biasing Face Recognition and Demographic Attribute Estimation

Gong, Sixue; Liu, Xiaoming; Jain, Anil K.

doi:10.1007/978-3-030-58526-6_20

Cited by 94 publications

(64 citation statements)

References 53 publications

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“…As a result of this work, we strongly advocate for reducing the facial expression bias in future face recognition databases, and further development of bias-reduction methods applicable to existing databases and existing models already trained on biased datasets [30,31,32]. The application of face manipulation techniques [33,34,35] could serve to enhance the facial expression availability on existing database by introducing new synthetically generated expressions.…”

Section: Discussionmentioning

confidence: 99%

Facial Expressions as a Vulnerability in Face Recognition

Peña

Serna

Morales

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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This work explores facial expression bias as a security vulnerability of face recognition systems. Despite the great performance achieved by state-of-the-art face recognition systems, the algorithms are still sensitive to a large range of covariates. We present a comprehensive analysis of how facial expression bias impacts the performance of face recognition technologies. Our study analyzes: i) facial expression biases in the most popular face recognition databases; and ii) the impact of facial expression in face recognition performances. Our experimental framework includes two face detectors, three face recognition models, and three different databases. Our results demonstrate a huge facial expression bias in the most widely used databases, as well as a related impact of face expression in the performance of state-of-the-art algorithms. This work opens the door to new research lines focused on mitigating the observed vulnerability.

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Section: Discussionmentioning

confidence: 99%

Facial Expressions as a Vulnerability in Face Recognition

Peña

Serna

Morales

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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“…The issue of fairness in data-driven computer vision systems has gained prominence, especially due to the presence of potential biases in the training data [304]. Since biometric systems rely heavily on training data, it is imperative that evaluation methodologies explicitly address the issue of fairness and bias [305], [306], [307], [308].…”

Section: Fairness and Biasmentioning

confidence: 99%

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Meden

Rot

Terhörst

et al. 2021

IEEE Trans.Inform.Forensic Secur.

126

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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.

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“…1, our final training data includes 4.5M images of 82K identities. To evaluate face recognition across races [47], we also test the proposed VPL on InsightFace Recognition Test (IFRT) [21], which contains 1.6M images of 242K identities (non-celebrity) covering four demographic groups: African, Caucasian, Indian and Asian [55,12,47,46]. For each demographic group, all pairs between gallery and probe sets are used for the 1:1 face verification.…”

Section: Implementation Detailsmentioning

confidence: 99%

Variational Prototype Learning for Deep Face Recognition

Deng

Guo

Yang

et al. 2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Deep face recognition has achieved remarkable improvements due to the introduction of margin-based softmax loss, in which the prototype stored in the last linear layer represents the center of each class. In these methods, training samples are enforced to be close to positive prototypes and far apart from negative prototypes by a clear margin. However, we argue that prototype learning only employs sample-to-prototype comparisons without considering sample-to-sample comparisons during training and the low loss value gives us an illusion of perfect feature embedding, impeding the further exploration of SGD. To this end, we propose Variational Prototype Learning (VPL), which represents every class as a distribution instead of a point in the latent space. By identifying the slow feature drift phenomenon, we directly inject memorized features into prototypes to approximate variational prototype sampling. The proposed VPL can simulate sample-to-sample comparisons within the classification framework, encour-aging the SGD solver to be more exploratory, while boosting performance. Moreover, VPL is conceptually simple, easy to implement, computationally efficient and memory saving. We present extensive experimental results on popular benchmarks, which demonstrate the superiority of the proposed VPL method over the state-of-the-art competitors.

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Jointly De-Biasing Face Recognition and Demographic Attribute Estimation

Cited by 94 publications

References 53 publications

Facial Expressions as a Vulnerability in Face Recognition

Facial Expressions as a Vulnerability in Face Recognition

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Variational Prototype Learning for Deep Face Recognition

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