Advancements in Person Re-Identification Through Artificial Intelligence Techniques

Abstract

Person re-identification (Re-ID) has advanced significantly through the integration of deep learning techniques, with face recognition serving as a foundational component. This study presents a comprehensive analysis of state-of-the-art approaches spanning face detection, alignment, recognition, and cross-camera person Re-ID. Deep Convolutional Neural Networks (CNNs), attention mechanisms, and generative models (GANs) drive progress in robust feature extraction, occlusion handling, and domain adaptation. Landmark techniques such as DeepFace, DeepID2+, and center loss have achieved near-human face verification accuracy, while cascaded CNNs and Kalman-filter-based tracking enhance detection and temporal consistency in video surveillance. Emerging trends include transformer-based models, multi-modal biometric fusion, and edge-cloud optimization for scalable deployment. However, challenges remain in cross-domain generalization, fairness, and full-body Re-ID integration. This synthesis identifies critical research gaps and underscores the need for holistic, real-time, and ethically sound Re-ID systems capable of operating under diverse real-world conditions.