Pose-invariant Face Recognition via SIFT Vocabulary Tree

Face recognition is an important part of intelligent video surveillance.To improve the recognition accuracy in case of unknown facial pose, we propose a pose-invariant face recognition approach which includes two stages.In training stage, we construct a vocabulary tree using SIFT features of sampled images, and compute the feature descriptor of each face based on the vocabulary tree.We then reduce the feature dimensionality through locality preserving projections (LPP).In recognition stage, we extract the SIFT features of the test face and compute its feature descriptor through the existed vocabulary tree, then obtain the low dimensional feature by LPP algorithm, K-NN algorithm is used for face recognition.Experimental results show that the proposed approach improves recognition rate under unknown pose to a large extent.