Abstract
Face recognition has been a popular research topic for many years. Mostly, the appearance-based methods use multiple samples per person for training. However, most of the time, we do not have enough training samples for each person. Sometimes, we only have single sample per person, and this increases the difficulty of the appearance-based methods implementation due to the lack of training samples. Therefore, we apply the Discriminative Multi-manifold Analysis (DMMA) method and proposed an accelerative method to address the problem effectively. Our fast DMMA method has divided into three modules. First, we input the training samples of multiple persons, one person one training sample, and then use a modified of K-means method to identify the similarity of two groups people. Second, these two groups of faces have to divide into non-overlapping local patches for the DMMA. Third, we repeat the previous two steps to obtain the binary tree projection matrix of fast DMMA. This thesis has tested the AR database and FERET database to verify the face recognition mechanism. In the experiments, we prove that the method can accelerate the process of DMMA under circumstances of very limited accuracy decrement.