Zipeng Yu
ABSTRACT
As the theory of Compressive Sensing being put forward, people began to study how to apply CS for face recognition. Allen Y. Yang and John Wright proposed a new method based on CS, named Sparse Representation-based Classification (SRC). SRC cast the recognition problem as one of finding a sparse representation of the test image in terms of the training set as a whole. SRC addressed the recognition problem with illumination invariant images successfully. However, SRC didn't consider the variable illumination and the complexity of calculation. In this paper, we propose an illumination robust algorithm based on SRC, named G_SRC. Moreover, our algorithm is faster than SRC. G_SRC applies the Gradientfaces to preprocess face images and extract illumination invariant. Then, G_SRC applies PCA to extract face feature and reduce the image dimensions. At last, we use SRC to face recognition. G_SRC can weaken the influence of light on the human face and improve the recognition. Experimental results on the ORL and the actual face databases show that G_SRC has better generalization ability than SRC for face recognition.
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Index Terms
- An Illumination Robust Algorithm for Face Recognition Via SRC and Gradientfaces
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