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A Novel Fast Face Recognition Method of Two-Dimensional Principal Component Analysis Based on BP Neural Networks

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3 Author(s)
Wenjing Han ; Coll. of Inf. & Electr. Eng., Shandong Univ. of Sci. & Technol., Qingdao ; Jing Li ; Nongliang Sun

Two-dimensional principal component analysis technique is an important and well-developed area of image recognition and to date this method has been put forward. A new face recognition method two-dimensional principal component analysis (2DPCA) based on BP neural networks, named 2DPCA-BP method, was proposed. 2DPCA was used to obtain a family of projected feature vectors, in which face image was projected into this family of projected feature vectors to get the feature matrix. BP-based neural network was used as classifier for its good learning capability. Experiment proved that 2DPCA-BP is better than 2DPCA-SVMs in velocity and its recognition accuracy is 98.246%. The CVL database showed that the system achieved excellent performance.

Published in:

Computational Intelligence and Industrial Application, 2008. PACIIA '08. Pacific-Asia Workshop on  (Volume:1 )

Date of Conference:

19-20 Dec. 2008

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