Using Gradient Features from Scale-Invariant Keypoints on Face Recognition
In this paper, an algorithm which combines Principal Component Analysis (PCA), Scale Invariant Feature Transform (SIFT) and gradient features to face recognition is proposed. The feature vectors invariant to image scaling and rotation are firstly extracted by SIFT with a different local gradient descriptor. And PCA is applied to the dimension reduction of the local descriptors for saving the computation time. Then the K-means algorithm is introduced to cluster the local descriptors, and the local and global information of images are combined to classify human faces. Simulation results demonstrate that PCA-SIFT local descriptors are robust to accessory and expression variations and that these descriptors have better performance than other comparative methods.