Online Appearance Model Learning for Video-Based Face Recognition

In this paper, we propose a novel online learning method which can learn appearance models incrementally from a given video stream. The data of each frame in the video can be discarded as soon as it has been processed. We only need to maintain a few linear eigenspace models and a transition matrix to approximately construct face appearance manifolds. It is convenient to use these learnt models for video-based face recognition. There are mainly two contributions in this paper. First, we propose an algorithm which can learn appearance models online without using a pre-trained model. Second, we propose a method for eigenspace splitting to prevent that most samples cluster into the same eigenspace. This is useful for clustering and classification. Experimental results show that the proposed method can both learn appearance models online and achieve high recognition rate.