Person re-identification (PRId) is critical in computer vision with significant surveillance, security, and public safety applications. This thesis explores the latest advancements in person re-identification techniques to address the challenges of matching individuals across different camera views and under varying conditions. The primary goal of this research is to enhance the accuracy and efficiency of PRId systems, ultimately contributing to improving security and surveillance systems. The thesis begins by providing an in-depth review of existing PRId methodologies, highlighting their strengths and limitations. It delves into the importance of feature extraction, metric learning, and deep neural networks in Re-ID systems. Furthermore, it discusses the challenges posed by  variations in lighting, pose, occlusions, and camera viewpoints, which are inherent to real-world surveillance scenarios.