SIFT-type descriptors for sparse-representation-based classification

Sparse representation based classification (SRC) as an efficient method has high recognition rate in many pattern recognition applications. Unfortunately, the original SRC method generally requires rigid alignment in classification. In this paper, the feature-based SRC method is addressed by using the PCA-SIFT and SPP-SIFT descriptors, respectively. The presented methods are not only efficient for alignment-free in face and vehicle recognition, but also robust for the image illumination variation, rescaling and affine transform, when the image processing is moved from pixel-domain into the feature-domain and sparse-domain, i.e. PCA-SIFT and SPP-SIFT descriptors. Experimental results show the presented methods in this paper have higher recognition rate, more robustness. In addition, PCA-SIFT-SRC has lower computational complexity than MKD-SRC and SRC in the above scenarios.