Abstract:
Sparse signal representation showed promising results in the field of face
recognition in the last few years. In this work, an algorithm based on
sparsifying transform is considered. It mainly learns a dictionary that can
transforms the image into sparse vectors. In the transformation domain,
the images of the same class should have similar nonzero coefficients
pattern that can be used for identification. The classification process
of this method only require to transform the image and make norm
comparisons to determine the class of the image. The proposed method
shows a comparable performance with the other known methods in the
literature by means of accuracy. This paper proposes a novel method in
sparsity based image identification that uses analysis dictionaries unlike
the conventional sparsity based methods. one advantage of the proposed
algorithm is the low computational cost of the classification process.
