The Biometric Recognition and Verification Systems Research Group is an interdisciplinary team dedicated to developing end-to-end solutions for both identity recognition (identification) and identity verification (authentication) based on physiological and behavioral biometric traits such as face, palmprint, fingerprint, iris/retina, ear, vein patterns, signature, gait, and voice. The group integrates advanced methodologies from biometric pattern recognition, computer vision, signal processing, deep representation learning (embedding learning), and metric learning to address real-world challenges including cross-device variability, illumination and pose changes, low-resolution imagery, partial occlusion, noise and motion blur, domain shift, and data imbalance. Core research topics include CNN/Transformer/Video-Transformer-based architectures, multimodal and multibiometric fusion, template security and cryptographic protection, presentation attack detection (anti-spoofing) / liveness detection, explainability and trustworthy AI, open-set recognition, and rigorous performance evaluation and error analysis using metrics such as FAR/FRR/EER/ROC/DET. The group aims to produce high-impact academic research while also building deployable prototypes that meet the robustness and reliability requirements of practical biometric systems.