Face Recognition based on scale invariant feature transform and Spatial Pyramid Representation

For Face Recognition (FR) task, local feature based approaches have proven to be more robust to variations than the holistic approaches. As one of the state-of-the-art local descriptors with excellent performance for matching different images of an object or a scene, Scale Invariant Feature Transform (SIFT) has sparingly been used in FR, and whether it is a good descriptor for face images needs to be analyzed more. In this paper we propose a face representation approach based on SIFT and Spatial Pyramid Representation (SPR), to enhance the recognition performance of SIFT based method. Firstly it employs SIFT to extract discriminative local features and then constructs Spatial Pyramid to form a local-holistic representation of a face image. Finally Classifiers such as Nearest Neighbor (NN) and Support Vector Machine (SVM) could be performed on representation vectors of equal length. The comparative experimental results on ORL and Yale databases indicate that our approach achieves better performance than other SIFT based methods. In addition, it shows great robustness against environmental variations such as pose mismatches, imperfect face alignment, various facial expressions and accessory configuration variations, demonstrating a new alternative method for FR task.