The field of biometric recognition and person re-identification is rapidly advancing with a focus on developing innovative solutions to address the challenges of multimodal recognition, domain adaptation, and lifelong learning. Researchers are exploring new approaches to integrate multiple biometric modalities, such as face, gait, and body, to improve recognition performance. Additionally, there is a growing interest in developing methods that can adapt to new domains and datasets without requiring extensive retraining. The use of ensemble fusion, graph neural networks, and attention mechanisms are becoming increasingly popular in this field. Noteworthy papers in this area include: CORE-ReID, which introduces a novel framework for unsupervised domain adaptation in person re-identification, achieving state-of-the-art results on multiple datasets. GaitAdapt, which proposes a continual learning approach for gait recognition, enabling the progressive enhancement of recognition capabilities over time. DepthGait, which presents a novel framework that incorporates RGB-derived depth maps and silhouettes for enhanced gait recognition, achieving state-of-the-art performance on standard benchmarks.