Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Looking at People: Sensing for Ubiquitous and Wearable Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Global Dimensionality of Face Space
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Recognizing faces with PCA and ICA
Computer Vision and Image Understanding - Special issue on Face recognition
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Robust linear dimensionality reduction
IEEE Transactions on Visualization and Computer Graphics
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Improving the interest operator for face recognition
Expert Systems with Applications: An International Journal
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Principle Component Analysis (PCA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in the traditional PCA some weaknesses. In this paper, we propose new PCA-based methods that can improve the performance of the traditional PCA and two-dimensional PCA (2DPCA) approaches. In face recognition where the training data are labeled, a projection is often required to emphasize the discrimination between the clusters. Both PCA and 2DPCA may fail to accomplish this, no matter how easy the task is, as they are unsupervised techniques. The directions that maximize the scatter of the data might not be as adequate to discriminate between clusters. So we proposed new PCA-based schemes which can straightforwardly take into consideration data labeling, and makes the performance of recognition system better. Experiment results show our method achieves better performance in comparison with the traditional PCA and 2DPCA approaches with the complexity nearly as same as that of PCA and 2DPCA methods.