Abstract
Face recognition has been a challenging task in computer vision. In this paper, we propose a new method for face recognition. Firstly, we extract HOG (Histogram of Orientated Gradient) features of each class face images in used Face databases. Then, we select the so-called eigenfaces from HOG features corresponding to each class face images and finally use them to build a overcomplete dictionary for ESRC (the Eigenface-based Sparse Representation Classification ). Experiments show that our method receives better results by comparison.
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Hou, YF., Pei, WJ., Chong, YW., Zheng, CH. (2013). Eigenface-Based Sparse Representation for Face Recognition. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_53
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DOI: https://doi.org/10.1007/978-3-642-39482-9_53
Publisher Name: Springer, Berlin, Heidelberg
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