Abstract
In recent years, sparse representation-based classification (SRC) has received significant attention due to its high recognition rate. However, the original SRC method requires a rigid alignment, which is crucial for its application. Therefore, features such as SIFT descriptors are introduced into the SRC method, resulting in an alignment-free method. However, a feature-based dictionary always contains considerable useful information for recognition. We explore the relationship of the similarity of the SIFT descriptors to multitask recognition and propose a clustering-weighted SIFT-based SRC method (CWS-SRC). The proposed approach is considerably more suitable for multitask recognition with sufficient samples. Using two public face databases (AR and Yale face) and a self-built car-model database, the performance of the proposed method is evaluated and compared to that of the SRC, SIFT matching, and MKD-SRC methods. Experimental results indicate that the proposed method exhibits better performance in the alignment-free scenario with sufficient samples.
Highlights
Sparse representation (SR)1,2 has become a hot topic in recent years
For handling contiguously occluded face recognition, such as disguise or expression variation, a modular weighted global sparse representation method was proposed in Ref. 13, which divided the image into modules and determined the reliability of each module based on its sparsity and residual
It seems that such a SIFT descriptor-based dictionary is far from the requirement of the restricted isometry property (RIP),26,27 which is discussed in Ref. 28
Summary
Sparse representation (SR) has become a hot topic in recent years. SR considers a query signal y as a linear representation of the columns in A, i.e., y 1⁄4 Ax þ e, where A is the dictionary (each column in A is typically referred to as an atom), x is a sparse representation coefficient vector over the dictionary A, and e denotes the noise. In Ref. 3, Wright et al presented a new method sparse representation-based classification (SRC), which achieved high recognition accuracy on face recognition. Due to this approach’s promising performance in image classification, SRC has been widely used in many pattern recognition applications, such as face recognition, gender, digit, biology data, and medical image classification. For handling contiguously occluded face recognition, such as disguise or expression variation, a modular weighted global sparse representation method was proposed in Ref. 13, which divided the image into modules and determined the reliability of each module based on its sparsity and residual. In Ref. 22, the authors presented an efficient face recognition algorithm based on the SRC using an adaptive K-means method, which clustered similar atoms of the same class and merged them into one atom while preserving the accuracy.
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