Online Face Recognition System Based on Local Binary Patterns and Facial Landmark Tracking

This paper presents a system for real-time face recognition. The system learns and recognizes faces from a video on the fly and it doesn't need already trained database. The system consists of the following sub-methods: face detection and tracking, face alignment, key frame selection, face description and face matching. The system detects face tracks from a video, which are used in learning and recognition. Facial landmark tracking is utilized to detect changes in facial pose and expression in order to select key frames from a face track. Faces in key frames are represented using local binary patterns (LBP) histograms. These histograms are stored into the database. Nearest neighbor classifier is used in face matching. The system achieved recognition rate of 98.6% in offline test and 95.9% in online test.