A Multi-Face Recognition Framework for Real Time Monitoring

Abstract

From the past few years, face recognition has become critical for security and surveillance applications, and is now necessary in many different settings, including offices, educational institutions, airports, corporations, and social spaces. In this paper, we present a framework of multi-face recognition for real time monitoring, resulting in simultaneous face tracking and recognition. First, the faces are detected in the video frames by using viola-jones algorithm. To remove the outliers from the detected face region, we design a face skeleton based on YCBCR color space for further feature points detection and extraction. Then harris corner feature points and SURF feature points are detected from each face, where harris points are used to track the faces in the video and the SURF feature points are used to extract facial features from the cropped faces. As the face tracking is going on, faces are simultaneously recognized by the trained classifier (support vector machine). The experiments conducted on publicly available dataset suggest that our method is reliable, accurate, and robust that can be deployed for real-world multi-face recognition systems.