Research on the Application and Comparison of PCA & 2DPCA in Face Recognition

Yi-lin YANG, Guang-yan WANG, Yan-xiang GENG

Abstract


Face recognition technology considers one of the hot spots in the field of pattern recognition, and it is also one of the difficulties in the field of pattern recognition. In this paper, the principal component analysis (PCA) algorithm and its application in the face recognition technology has been studied primarily. Firstly, discusses the traditional 1DPCA algorithm. Secondly, based on the previously mentioned 1DPCA algorithm, the 2DPCA algorithm, which improves algorithm, is put forward. Simulation results showed that the 2DPCA method is superior to the traditional PCA method for its avoidance of the transformation from image array to one-dimensional vector. The programmable procedures of 1DPCA and 2DPCA designed in the thesis have high recognition rate, and pose theoretically important influence and application value.

Keywords


Face recognition, Data dimension reduction, Principle component analysis, 2DPCA


DOI
10.12783/dtetr/amma2017/13363

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