2022 |
Babnik, Žiga; Peer, Peter; Štruc, Vitomir FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration Proceedings Article V: IAPR International Conference on Pattern Recognition (ICPR), 2022. Povzetek | Povezava | BibTeX | Oznake: adversarial examples, adversarial noise, biometrics, face image quality assessment, face recognition, FIQA, image quality assessment @inproceedings{ICPR2022, Recent state-of-the-art face recognition (FR) approaches have achieved impressive performance, yet unconstrained face recognition still represents an open problem. Face image quality assessment (FIQA) approaches aim to estimate the quality of the input samples that can help provide information on the confidence of the recognition decision and eventually lead to improved results in challenging scenarios. While much progress has been made in face image quality assessment in recent years, computing reliable quality scores for diverse facial images and FR models remains challenging. In this paper, we propose a novel approach to face image quality assessment, called FaceQAN, that is based on adversarial examples and relies on the analysis of adversarial noise which can be calculated with any FR model learned by using some form of gradient descent. As such, the proposed approach is the first to link image quality to adversarial attacks. Comprehensive (cross-model as well as model-specific) experiments are conducted with four benchmark datasets, i.e., LFW, CFP–FP, XQLFW and IJB–C, four FR models, i.e., CosFace, ArcFace, CurricularFace and ElasticFace and in comparison to seven state-of-the-art FIQA methods to demonstrate the performance of FaceQAN. Experimental results show that FaceQAN achieves competitive results, while exhibiting several desirable characteristics. The source code for FaceQAN will be made publicly available. |
Babnik, Žiga; Štruc, Vitomir Assessing Bias in Face Image Quality Assessment Proceedings Article V: EUSIPCO 2022, 2022. Povzetek | Povezava | BibTeX | Oznake: bias, bias analysis, biometrics, face image quality assessment, face recognition, FIQA, image quality assessment @inproceedings{EUSIPCO_2022, Face image quality assessment (FIQA) attempts to improve face recognition (FR) performance by providing additional information about sample quality. Because FIQA methods attempt to estimate the utility of a sample for face recognition, it is reasonable to assume that these methods are heavily influenced by the underlying face recognition system. Although modern face recognition systems are known to perform well, several studies have found that such systems often exhibit problems with demographic bias. It is therefore likely that such problems are also present with FIQA techniques. To investigate the demographic biases associated with FIQA approaches, this paper presents a comprehensive study involving a variety of quality assessment methods (general-purpose image quality assessment, supervised face quality assessment, and unsupervised face quality assessment methods) and three diverse state-of-the-art FR models. Our analysis on the Balanced Faces in the Wild (BFW) dataset shows that all techniques considered are affected more by variations in race than sex. While the general-purpose image quality assessment methods appear to be less biased with respect to the two demographic factors considered, the supervised and unsupervised face image quality assessment methods both show strong bias with a tendency to favor white individuals (of either sex). In addition, we found that methods that are less racially biased perform worse overall. This suggests that the observed bias in FIQA methods is to a significant extent related to the underlying face recognition system. |
Objave
2022 |
FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration Proceedings Article V: IAPR International Conference on Pattern Recognition (ICPR), 2022. |
Assessing Bias in Face Image Quality Assessment Proceedings Article V: EUSIPCO 2022, 2022. |