Here, we present a computational photography-based biometric scanner to capture deep wrist vascular arcades using multi-zone NIR LED banks and multi-exposure HDR per each illumination zone, achieving exceptional pseudo-color imaging of the wrist volume, in addition to other anatomical features such as high-resolution skin textures. Using NIR illumination on dorsal, lateral, and ventral positions, deeper vascular structures are encoded in red and green, and superficial textures and veins in blue. As an internal biometric, this modality inherently provides stronger security and privacy. Implemented on an NVIDIA Jetson platform, we further leverage secure computation using ARM TrustZone isolation, encrypted data storage, and secure boot functionalities. Experimental results validate the effectiveness and computational efficiency of the proposed system, demonstrating its potential for secure, real-time, and privacy-preserving biometric applications.
Optical coherence tomography (OCT) is a non-invasive imaging technique capable of acquiring depth information up to 1-3mm beneath the skin surface, including the stratum corneum and viable epidermis regions. This technique allows for the reconstruction of internal and external fingerprint images from grayscale data. However, existing fingerprint extraction methods heavily rely on contour features and current 2D approaches overlook the spatial continuity of biometric features in OCT images. Therefore, this paper proposes a novel iterative algorithm for internal and external fingerprint extraction from OCT images. This algorithm incorporates the spatial continuity of OCT slice images and an iterative feature enhancement module during the prediction phase to improve segmentation continuity. Additionally, a soft label technique is employed to reduce contour dependence and mitigate interference from noise and anomaly interference. Qualitative and quantitative experiments demonstrate significant improvements in segmentation accuracy with higher fingerprint quality, validating the effectiveness of the proposed approach.
This paper proposes facial recognition approach on variations in outdoor lighting conditions using short wavelength infrared (SWIR) images. Conventional visible light (VIS) systems struggle under challenging lighting, such as backlit condition, due to their sensitivity to ambient light. Recently, SWIR imaging has emerged as a promising alternative, offering robustness against such conditions. Despite its advantages, SWIR imaging faces challenges, including higher noise levels, limited high-intensity illumination, and differences in image characteristics compared to VIS images. These challenges hinder the integration of SWIR-based systems with existing VIS databases. To address these issues, this study proposes a SWIR-to-VIS image translation method to improve VIS-SWIR face recognition performance. The proposed method relies solely on recognition loss for training the image translator, eliminating the need for other loss functions. This novel approach ensures compatibility with existing VIS-based databases while taking advantage of SWIR’s robustness to ambient light. Experimental evaluations demonstrate the efficacy of the proposed method in achieving accurate face recognition under severe outdoor lighting conditions.
The adoption of facial recognition technology for identity verification has seen significant growth in recent years. While state-of-the-art (SOTA) facial recognition systems demonstrate high accuracy, the biometric features extracted and stored in system databases are inherently privacy-sensitive. If compromised, this data poses a serious threat to user privacy. To address this challenge, we propose a novel federated learning-based cancelable template protection framework that combines secure, user-specific transformations with collaborative training. In our approach, each user’s face features are first converted into a cancelable template, revocable representation, and then used to train a shared model through federated learning, which enables multiple clients to jointly update a global model without sharing their raw or transformed biometric data. We evaluate our method across key security properties, including unlinkability, revocability, and resilience to inversion attacks, in compliance with the ISO/IEC 30136 standard for biometric performance and protection. Experiments conducted on the large-scale CelebA and LFW dataset using modern facial recognition architectures demonstrate that our approach achieves competitive recognition performance while significantly enhancing security, while preserving recognition performance.
Adversarial attacks pose a significant threat to the reliability of biometric systems, particularly in security-critical applications such as identity verification and access control. Ensuring robustness against such attacks is essential for the safe deployment of face recognition technologies in real-world scenarios. To advance this goal, the 2025 Adversarial Attack Challenge for Secure Face Recognition was organized as part of the International Joint Conference on Biometrics (IJCB) 2025.The competition focused on two main tracks: Detection, where the objective was to determine whether a given face image is clean or adversarial, and Resilience, which aimed to evaluate recognition systems under adversarial perturbations. Participants were provided with a standardized dataset derived from CelebA and LFW, encompassing both clean samples and adversarial images crafted using ten diverse attack methods targeting evasion and impersonation scenarios. To ensure fairness and reproducibility, all models were trained solely on the data provided, with support from a custom open source adversarial attack package tailored for face recognition.In addition to benchmarking adversarial robustness, the challenge contributes to the research community by releasing the data set and the extensible attack package, allowing further investigation of secure and reliable face recognition systems.
