IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decomposition and virtualization of eigenface for face recognition under various lighting conditions
Systems and Computers in Japan
Using Signal/Residual Information of Eigenfaces for PCA Face Space Dimensionality Characteristics
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
A PCA Based Visual DCT Feature Extraction Method for Lip-Reading
IIH-MSP '06 Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia
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Face Recognition is an important topic in the field of pattern recognition. This technology has a variety of applications including entrance guard control, personal service system, criminal verification, and security verification of finance. Our research focuses on the development of a human face recognition system. It is a challenge to correctly identify a human in an image under various possible situations including difference of lighting conditions, change of hairstyles, variation of facial expression, and different aspects of the face. We have analyzed several existing face recognition techniques and found that each of them is performed well over some specific sets of testing samples but poorly over some other sets. This motivates us to combine different techniques to construct a better face recognition system. First, we propose a new module E-2DPCA applying DCT for image enhancement and 2DPCA for feature extraction. The experimental results show that the recognition accuracy of E-2DPCA is better than all the modules we have analyzed. We choose the best two from those analyzed and compared them with our proposed E-2DPCA module, and found that although the E-2DPCA module outperforms the other two modules, each of the three modules behaves better than others over some specific set of samples. Thus we combine the three modules and apply weighted voting scheme to choose the recognition result from those given by the three modules. Experimental results show that the integrated system can further improve the recognition rate.