This paper investigates a face recognition approach based on Scale Invariant Feature Transform (SIFT) feature and sparse representation. The approach takes advantage of SIFT which is local feature other than holistic feature in classical Sparse Representation based Classification (SRC) algorithm and possesses strong robustness to expression, pose and illumination variations. Since hexagonal image has more inherit merits than square image to make recognition process more efficient, we extract SIFT keypoint in hexagonal-sampling image. Instead of matching SIFT feature, firstly the sparse representation of each SIFT keypoint is given according the constructed dictionary; secondly these sparse vectors are quantized according dictionary; finally each face image is represented by a histogram and these so-called Bag-of-Words vectors are classified by SVM. Due to use of local feature, the proposed method achieves better result even when the number of training sample is small. In the experiments, the proposed method gave higher face recognition rather than other methods in ORL and Yale B face databases; also, the effectiveness of the hexagonal-sampling in the proposed method is verified.
Graph-based dimensionality reduction methods are popular in pattern recognition and machine learning. In contrast to the manifold learning approaches, the dot product representation of graphs (DPRG) seeks a solution to dimensionality reduction by assigning vectors to each node of a graph such that the dot product of every pair of nodes approximates the similarity between them. The DPRG has many potential applications, for the reason that there is no prior assumption of the data distribution. It has been found, however, that the DPRG tends to reduce the distances of the graph nodes represented in a low-dimensional space, which in turn degrades the performance of data clustering. Motivated by this observation, we propose an extended DPRG (EDPRG) model by simply employing negative similarity values. The theoretical analysis and experiments on synthetic data show that the modification is effective in increasing between-class distances. We demonstrate the effectiveness of the EDPRG model by experiments on synthetic aperture radar (SAR) image segmentation. The proposed image segmentation method has two steps. The first one presegments the image by the mean shift algorithm. The second merges the resulting regions by means of the EDPRG model.