A Novel Fast Face Recognition Method of Two-Dimensional Principal Component Analysis Based on BP Neural Networks
Computational Intelligence and Industrial Application, 2008. PACIIA '08. Pacific-Asia Workshop（2008）
摘要
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.
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关键词
bp neural network,recognition accuracy,face reconstruction,support vector machines (svms),face recognition,projected feature vector,two-dimensional component analysis (2dpca),bp-based neural network,bp-based neural networks,backpropagation,image recognition,cvl database,two-dimensional principal component analysis,excellent performance,two-dimensional principal,new face recognition method,image classification,feature matrix,component analysis,bp neural networks,novel fast face recognition,principal component analysis,neural nets,fast face recognition method,feature extraction,support vector machine,face,neural network,artificial neural networks,feature vector,image reconstruction
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