Cross-model face recognition poses a major challenge due to inconsistent embeddings produced by diverse face recognition models, limiting system interoperability. Enhancing compatibility among these models is essential for scalable, maintainable face recognition systems. This paper presents the Kolmogorov-Arnold Guided Alignment (KAGA) model, optimized with Mutual Decoupled Distillation (MDD), to effectively bridge embedding discrepancies. KAGA integrates a cross-attention mechanism and a learnable Guided Alignment Embedding to unify feature spaces, while Kolmogorov-Arnold Networks ensure robustness of the transformation. MDD facilitates selective knowledge transfer without compromising class separability, and bidirectional distillation refines feature representations for improved generalization. Extensive experiments across multiple face recognition benchmarks confirm KAGA's effectiveness, demonstrating substantial gains in accuracy and cross-model compatibility compared to state-of-the-art approaches.

Palmprint recognition has emerged as a prominent biometric authentication method due to its high discriminative power, making it suitable for IoT-based security applications. However, the traditional verification paradigm—requiring identical query and registered palmprints—poses notable limitations. This approach is inconvenient if the registered palmprint is injured. To address these challenges, we draw inspiration from biological insights into the symmetrical development of structures during embryonic growth and propose a novel Cross-Chirality Palmprint Verification (CCPV) framework. CCPV enables authentication using either palm, irrespective of which palm is registered, enhancing flexibility for IoT deployments with diverse user conditions. CCPV incorporates an innovative matching rule to improve robustness and minimize variability. This rule calculates distances by flipping the gallery and query palmprints, averaging the results to produce the final matching score. Considering all potential alignments, this approach reduces variance and boosts reliability, which is critical for ensuring seamless biometric authentication in IoT systems. Complementing this is the cross-chirality (CC) loss, which fosters a robust feature space tailored to cross-chirality matching. The CC loss ensures consistency across four palmprint variants—left, right, flipped left, and flipped right—enabling the model to extract chirality-consistent features. Extensive experiments on public datasets validate our effectiveness under closed-set and open-set scenarios. Furthermore, we demonstrate that CCPV is versatile and can seamlessly integrate with existing palmprint recognition methods to achieve superior performance. This innovation advances state-of-the-art biometric authentication and paves the way for more resilient palmprint recognition systems for IoT applications.

Periocular and face are two common biometric modalities for identity management. Recently, the emergence of conditional biometrics has enabled the exploitation of the correlation between face and periocular to enhance each modality’s performance, in which we coin intra-modal matching in this paper. However, limitations arise in each modality, particularly when wearing sunglasses or helmets, causing the absence of periocular or facial occlusion. A biometric system empowered with inter-modal matching capability between periocular and face is essential to mitigate these challenges. This paper presents a novel reciprocal learning model that utilizes periocular and face conditioning to facilitate flexible intra-modal and inter-modal matching. To address the intra-modal matching challenge, we devise a lightweight Gated Convolutional Channel-wise Self-Attention Network that enables selective attention to shared salient periocular and face features. On the other hand, to bridge the modality gap without sacrificing the intra-modal matching performance, we propose a modality and augmentation-aware contrastive loss that incorporates semi-supervised positive sampling and alignment-specific logit rescaling. Extensive identification and verification experiments on five face-periocular datasets under the open-set protocol attest to the efficacy of our proposed methods.

Very low-resolution face recognition (VLRFR) poses unique challenges, such as tiny regions of interest and poor resolution due to extreme standoff distance or wide viewing angle of the acquisition devices. In this paper, we study principled approaches to elevate the recognizability of a face in the embedding space instead of the visual quality. We first formulate a robust learning-based face recognizability measure, namely recognizability index (RI), based on two criteria: (i) proximity of each face embedding against the unrecognizable faces cluster center and (ii) closeness of each face embedding against its positive and negative class prototypes. We then devise an index diversion loss to push the hard-to-recognize face embedding with low RI away from unrecognizable faces cluster to boost the RI, which reflects better recognizability. Additionally, a perceptibility attention mechanism is introduced to attend to the most recognizable face regions, which offers better explanatory and discriminative traits for embedding learning. Our proposed model is trained end-to-end and simultaneously serves recognizability-aware embedding learning and face quality estimation. To address VLRFR, our extensive evaluations on three challenging low-resolution datasets and face quality assessment demonstrate the superiority of the proposed model over the state-of-the-art methods.

Multimodal biometrics has been attributed to achieving better performance compared to unimodal biometrics, despite there being some limitations on its utilization e.g. availability, deployment cost, templates management, etc. In this paper, we revolve around a generalized multimodal biometrics notion, which we coin as Conditional Multimodal Biometrics (CMB). The CMB is substantiated by a learning model which is trained with N multimodal biometrics. During enrollment and query, the trained CMB model is utilized as a feature encoder to transform any x biometric raw input(s) yielding x reference and query instances, respectively, where 1≤x≤ N. Depending on application needs, multimodal biometrics system enjoys better performance by deploying either a single biometrics, a subset, or all N modalities. As a means of realization, we consider face and periocular biometrics and propose a deep CMB network, known as CMB-Net. The CMB-Net is composed of two predictors corresponding to face and periocular with a shared-parameter convolutional backbone. Apart from classification losses for each face and periocular, a CMB loss with regularization is devised to attract periocular-face intra-subject feature embeddings and repel periocular-face inter-subject feature embeddings, whilst each face and periocular regulates one another throughout CMB-Net training. We scrutinize three CMB configurations, namely periocular conditioned by face, face conditioned by periocular and periocular-face, under the CMB regimen. Our experimental results on five periocular-face in the wild datasets demonstrate that all three CMB configurations outperform their respective baselines under both identification and verification modes.