Abstract

Face recognition technology plays a vital role in modern educational systems by enabling efficient and accurate student identification. The growing demand for efficient and accurate student identification systems has highlighted the limitations of conventional face recognition methods, particularly in handling variations in pose, lighting, and occlusions. To address this, our Precision-Optimized Human Recognition Model builds an Adaptive Information Retrieval System utilizing a Histogram of Oriented Gradients (HOG)-based detector for face detection and a ResNet-34-based Deep Metric Learning Model for face recognition. The system encodes facial features and performs identity verification using Euclidean distance for precise and reliable student identification. By integrating these techniques, the model ensures real-time data retrieval with high accuracy and adaptability to diverse conditions. The proposed approach enhances computational efficiency while maintaining robust recognition performance, making it a scalable and practical solution for identity verification in educational institutions.