Directional two-dimensional principal component analysis for face recognition

Authors:
Lijun Yan;Jeng-Shyang Pan
Affiliations:
Harbin Institute of technology, Harbin, China;National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
Venue:
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
Year:
2010

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Abstract

In this paper, two novel face recognition frames are proposed, called single directional two dimensional principal component analysis (SD2DPCA) and multi-directional two dimensional principal component analysis (MD2DPCA). Compared with other popular algorithms, SD2DPCA needs less running time while achieves almost the same correct recognition rate. MD2DPCA can extract the directional feature of face images more efficiently, so it gets a higher recognition rate, and experimental results demonstrate that the SD2DPCA and MD2DPCA have their advantages.