Robust sparse coding for face recognition

Abstract

Recently the sparse representation (or coding) based classification (SRC)

Fast and robust face recognition via coding residual map learning based adaptive masking

“…The best results of competing methods, including the nearest neighbor (NN) classifier, SRC [7], CRC_RLS [12], RSC [24] and the proposed method, are presented in Table 1. In addition, to more clearly show the advantage brought by the learned residual map, we also present the result of CRC_RLS coupled with an outlier detector by global thresholding.…”

“…In particular, the RSC method [24] has shown excellent results in FR with various occlusions. From the viewpoint of maximal likelihood estimation, the l 1 -norm or l 2 -norm characterization of the representation residual is only optimal when the residual follows Laplacian or Gaussian distribution, thus Yang et al [24] used a robust regression function to measure the representation residual. Although the RSC method has high recognition accuracy, it is also very time-consuming, like SRC.…”

“…Many related works have been developed, such as the l 1 -graph for image classification [8], kernel based SRC [9], Gabor feature based SRC [10,48], robust sparse coding (RSC) [24], robust alignment with sparse and low rank decomposition [25], joint dimension reduction and dictionary learning [49], face and ear multimodal biometric system [50], etc. In particular, the RSC method [24] has shown excellent results in FR with various occlusions. From the viewpoint of maximal likelihood estimation, the l 1 -norm or l 2 -norm characterization of the representation residual is only optimal when the residual follows Laplacian or Gaussian distribution, thus Yang et al [24] used a robust regression function to measure the representation residual.…”

Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary

“…Such a big occlusion matrix makes the sparse coding process very computationally expensive, and even prohibitive. These two issues are not fully solved by the sparsity based FR improvers [28][29][30][31][32][33][34] [40][41][42]. For instance, only holistic features are considered in [29][30][31][32][33][34][ [40][41][42], FR with occlusion is ignored in [28][32] [33], and no occlusion dictionary is learned in [40][41][42].…”

“…Elhamifar et al [30] discussed classification using structure sparse representation to exploit the block structure of the dictionary, and Yang et al [31] proposed a robust sparse coding model with a maximum likelihood estimator like fidelity term. Moreover, learning a discriminative dictionary under the sparse representation framework for classification has also attracted much attention.…”