Biometric recognition using deep learning is a rapidly growing research area in computer vision and pattern recognition that focuses on identifying individuals based on unique physiological or behavioral traits, such as fingerprints, face, iris, voice, gait, and palmprints. Early deep learning approaches employed convolutional neural networks (CNNs) to extract discriminative features, while later research incorporated metric learning, triplet loss, and contrastive learning to improve inter-class separability and intra-class compactness. Recent studies leverage attention mechanisms, residual and dense architectures, generative adversarial networks (GANs) for data augmentation, and transformer-based models to handle variations in pose, illumination, occlusion, and temporal dynamics. Applications span security and access control, forensics, healthcare, banking, and surveillance, where reliable and robust biometric authentication is critical. Current research also explores multi-modal biometric recognition, lightweight models for edge deployment, privacy-preserving methods, and explainable AI techniques to enhance transparency, robustness, and adoption in real-world systems.