Multi-Pose Face Recognition And Tracking System

We propose a real time system for person detection, recognition and tracking using frontal and profile faces. The system integrates face detection, face recognition and tracking techniques. The face detection algorithm uses both frontal face and profile face detectors by extracting the Haar’ features and uses them in a cascade of boosted classifiers. The pose is determined from the face detection algorithm which uses a combination of profile and frontal face cascades and, depending on the pose, the face is compared with a particular set of faces having the same range for classification. The detected faces are recognized by projecting them onto the Eigenspace obtained from the training phase using modular weighted PCA and then, are tracked using the Kalman filter multiple face tracker. In this proposed system, the pose range is divided into three bins onto which the faces are sorted and each bin is trained separately to have its own Eigenspace. This system has the advantage of recognizing and tracking an individual with minimum false positives due to pose variations.