Periocular and face are complementary biometrics for identity management, albeit with inherent limitations, notably in scenarios involving occlusion due to sunglasses or masks. In response to these challenges, we introduce Flexible Biometric Recognition (FBR), a novel framework designed to advance conventional face, periocular, and multimodal face-periocular biometrics across both intra- and cross-modality recognition tasks. FBR strategically utilizes the Multimodal Fusion Attention (MFA) and Multimodal Prompt Tuning (MPT) mechanisms within the Vision Transformer architecture. MFA facilitates the fusion of modalities, ensuring cohesive alignment between facial and periocular embeddings while incorporating soft-biometrics to enhance the model's ability to discriminate between individuals. The fusion of three modalities is pivotal in exploring interrelationships between different modalities. Additionally, MPT serves as a unifying bridge, intertwining inputs and promoting cross-modality interactions while preserving their distinctive characteristics. The collaborative synergy of MFA and MPT enhances the shared features of the face and periocular, with a specific emphasis on the ocular region, yielding exceptional performance in both intra-and cross-modality recognition tasks. Rigorous experimentation across four benchmark datasets validates the note-worthy performance of the FBR model. The source code is available at https://github.com/MIS-DevWorks/FBR.

Traditional biometrics identification performs matching between probe and gallery that may involve the same single or multiple biometric modalities. This paper presents a cross-matching scenario where the probe and gallery are from two distinctive biometrics, i.e., face and periocular, coined as face-periocular cross-identification (FPCI). We propose a novel contrastive loss tailored for face-periocular cross-matching to learn a joint embedding, which can be used as a gallery or a probe regardless of the biometric modality. On the other hand, a hybrid attention vision transformer is devised. The hybrid attention module performs depth-wise convolution and conv-based multi-head self-attention in parallel to aggregate global and local features of the face and periocular biometrics. Extensive experiments on three benchmark datasets demonstrate that our model sufficiently improves the performance of FPCI. Besides that, a new face-periocular dataset in the wild, the Cross-modal Face-periocular dataset, is developed for the FPCI models training.

This paper describes biometric liveness establishment in support of server-side authentication computation on user face data, as captured on client-side apparatus, via a bidirectional user-to-server challenge-response interaction. We propose for server-side issue of a random challenge, with the user undertaking verbal delivery of the response into aforesaid capture apparatus. This spoken input is subject to speaker-independent speech decoding, on the client-side platform, for extraction of the response numeric or symbolic valuation, which is then transmitted to the authentication server. Computation undertaken on this challenge-response pair, as specific to user interaction of interest, would then result in a test authenticator, which is then verified against some user-specific reference valuation as previously established during initial user enrolment. This user-specific authenticator, as presumed secret and exclusive to particular user, can furthermore be used in support of biometric salting as used to generate a secure biometric template. These templates would constitute a cancellable biometric, allowing for credential management processes equivalent to those undertaken on user-specific passwords or physical tokens, and furthermore preserving the privacy of the underlying biometric data.