Online learning from local features for video-based face recognition

This paper presents an online learning approach to video-based face recognition that does not make any assumptions about the pose, expressions or prior localization of facial landmarks. Learning is performed online while the subject is imaged and gives near realtime feedback on the learning status. Face images are automatically clustered based on the similarity of their local features. The learning process continues until the clusters have a required minimum number of faces and the distance of the farthest face from its cluster mean is below a threshold. A voting algorithm is employed to pick the representative features of each cluster. Local features are extracted from arbitrary keypoints on faces as opposed to pre-defined landmarks and the algorithm is inherently robust to large scale pose variations and occlusions. During recognition, video frames of a probe are sequentially matched to the clusters of all individuals in the gallery and its identity is decided on the basis of best temporally cohesive cluster matches. Online experiments (using live video) were performed on a database of 50 enrolled subjects and another 22 unseen impostors. The proposed algorithm achieved a recognition rate of 97.8% and a verification rate of 100% at a false accept rate of 0.0014. For comparison, experiments were also performed using the Honda/UCSD database and 99.5% recognition rate was achieved.