Face recognition is a rapidly advancing research topic due to the large number of applications that can benefit from it. Face recognition consists of determining whether a known face is present in an image and is typically composed of four distinct steps. These steps are face detection, face alignment, feature extraction, and face classification [1]. The leading application for face recognition is video surveillance. The majority of current research in face recognition has focused on determining if a face is present in an image, and if so, which subject in a known database is the closest match. This Thesis deals with face matching, which is a subset of face recognition, focusing on face identification, yet it is an area where little research has been done. The objective of face matching is to determine, in real-time, the degree of confidence to which a live subject matches a facial image. Applications for face matching include video surveillance, determination of identification credentials, computer-human interfaces, and communications security.
The method proposed here employs principal component analysis [16] to create a method of face matching which is both computationally efficient and accurate. This method is integrated into a real time system that is based upon a two camera setup. It is able to scan the room, detect faces, and zoom in for a high quality capture of the facial features. The image capture is used in a face matching process to determine if the person found is the desired target. The performance of the system is analyzed based upon the matching accuracy for 10 unique subjects
