Template-Driven Knowledge Distillation for Compact and Accurate Periocular Biometrics Deep-Learning Models.

This work addresses the challenge of building an accurate and generalizable periocular recognition model with a small number of learnable parameters. Deeper (larger) models are typically more capable of learning complex information. For this reason, knowledge distillation (kd) was previously proposed to carry this knowledge from a large model (teacher) into a small model (student). Conventional KD optimizes the student output to be similar to the teacher output (commonly classification output). In biometrics, comparison (verification) and storage operations are conducted on biometric templates, extracted from pre-classification layers. In this work, we propose a novel template-driven KD approach that optimizes the distillation process so that the student model learns to produce templates similar to those produced by the teacher model. We demonstrate our approach on intra- and cross-device periocular verification. Our results demonstrate the superiority of our proposed approach over a network trained without KD and networks trained with conventional (vanilla) KD. For example, the targeted small model achieved an equal error rate (EER) value of 22.2% on cross-device verification without KD. The same model achieved an EER of 21.9% with the conventional KD, and only 14.7% EER when using our proposed template-driven KD.

Self-restrained triplet loss for accurate masked face recognition.

Using the face as a biometric identity trait is motivated by the contactless nature of the capture process and the high accuracy of the recognition algorithms. After the current COVID-19 pandemic, wearing a face mask has been imposed in public places to keep the pandemic under control. However, face occlusion due to wearing a mask presents an emerging challenge for face recognition systems. In this paper, we present a solution to improve masked face recognition performance. Specifically, we propose the Embedding Unmasking Model (EUM) operated on top of existing face recognition models. We also propose a novel loss function, the Self-restrained Triplet (SRT), which enabled the EUM to produce embeddings similar to these of unmasked faces of the same identities. The achieved evaluation results on three face recognition models, two real masked datasets, and two synthetically generated masked face datasets proved that our proposed approach significantly improves the performance in most experimental settings.

Real masks and spoof faces: On the masked face presentation attack detection.

Face masks have become one of the main methods for reducing the transmission of COVID-19. This makes face recognition (FR) a challenging task because masks hide several discriminative features of faces. Moreover, face presentation attack detection (PAD) is crucial to ensure the security of FR systems. In contrast to the growing number of masked FR studies, the impact of face masked attacks on PAD has not been explored. Therefore, we present novel attacks with real face masks placed on presentations and attacks with subjects wearing masks to reflect the current real-world situation. Furthermore, this study investigates the effect of masked attacks on PAD performance by using seven state-of-the-art PAD algorithms under different experimental settings. We also evaluate the vulnerability of FR systems to masked attacks. The experiments show that real masked attacks pose a serious threat to the operation and security of FR systems.

Extended evaluation of the effect of real and simulated masks on face recognition performance.

Face recognition is an essential technology in our daily lives as a contactless and convenient method of accurate identity verification. Processes such as secure login to electronic devices or identity verification at automatic border control gates are increasingly dependent on such technologies. The recent COVID-19 pandemic has increased the focus on hygienic and contactless identity verification methods. The pandemic has led to the wide use of face masks, essential to keep the pandemic under control. The effect of mask-wearing on face recognition in a collaborative environment is currently a sensitive yet understudied issue. Recent reports have tackled this by using face images with synthetic mask-like face occlusions without exclusively assessing how representative they are of real face masks. These issues are addressed by presenting a specifically collected database containing three sessions, each with three different capture instructions, to simulate real use cases. The data are augmented to include previously used synthetic mask occlusions. Further studied is the effect of masked face probes on the behaviour of four face recognition systems-three academic and one commercial. This study evaluates both masked-to-non-masked and masked-to-masked face comparisons. In addition, real masks in the database are compared with simulated masks to determine their comparative effects on face recognition performance.