Multi-Face Detection and Recognition System

Abstract

The use of face recognition technology in modern intelligent systems has emerged as one of the main features owing to its extensive application across several security, surveillance, access control and automated attendance systems. Although classical face recognition techniques have been found sufficient in controlled settings in the presence of a single target, they fail in real world situations where there are multiple faces present with differences in illumination, pose, expression, occlusion, and complexity of the background. This project tackles these issues by suggesting an effective real-time multi-face detection and recognition system that uses deep learning methods.
The suggested system combines a progressive convolutional neural network (CNN)-based face detection models and a deep embedding-based face recognition method like FaceNet and ArcFace. The system can detect several faces on both fixed-image and live video feeds, extract discriminative facial features and positively identify individuals by comparing feature embeddings with a stored facial database. The structure is a modular one composed of image acquisition, preprocessing, face detection, feature extraction, face recognition, and visualization modules, which allows it to be scaled and operate in real-time.
The system also uses preprocessing methods like face alignment, normalization, and patch extraction to make the system robust to recognize faces in difficult conditions. Experiments on publicly available data including Labeled Faces in the Wild (LFW), and a dataset collected by the authors show high recognition accuracy, resistance to variations, and can be run in real-time. The findings confirm the efficiency of the suggested strategy on managing dense and uncontrolled spaces.
Altogether, this project illustrates that the systems of multi-face detection and recognition based on deep learning will be highly superior to traditional ones and will offer a viable and scalable solution to the real-life presence in surveillance, smart classrooms, and access control systems.