Abstract：Sparse representation based classification (SRC) algorithm loses much discriminative information hidden in the training samples when constructing dictionary and the L1-minimization approach to solving the coding coefficient is computationally expensive. Aiming at these problems,a face recognition algorithm via compressive sensing based on Fisher discrimination dictionary learning and least square method is proposed. The training samples are trained by Fisher discrimination criterion and thus the structured dictionary is acquired. Then, the coding coefficients are obtained by solving L2-minimization problem through regularized least square method. Finally, the face is identified through the coding coefficient and reconstruction error. The experimental results clearly show that the proposed method has a better accuracy rate and improves the recognition speed compared with the existing sparse representation classification methods.
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2014, Vol. 27 
