Enhancing Law Enforcement Through Pose-Based Facial Recognition and Image Normalization Techniques

Artificial intelligence (AI) systems have made significant strides in image processing, yet encounter challenges like noise, occlusion, and fast video processing. Metric learning, a subset of AI, utilizes distance metrics to assess image similarity, while Deep Metric Learning employs Neural Networks to learn discriminative features. This work proposes an innovative method for normalizing face images using metric learning techniques, crucial for applications such as face recognition and expression analysis. Traditional normalization methods often struggle with pose, illumination, and expression variations. Drawing from image processing sectors like automotive and medical, this study employs weakly supervised data and face orientation to identify faces for law enforcement. It focuses on pose-oriented face recognition using metric learning to identify similar pairings between images. The optimization process involves different face directions, enhancing law enforcement capabilities by removing noise through a Diffusion model. Experimental results on benchmark datasets illustrate the efficacy of our approach in improving face image normalization compared to conventional methods. The system's accuracy hinges on its ability to adapt to varying conditions during experimentation.