Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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
Quality modeling of chemical product based on a new chaotic Elman neural network
ICNC'09 Proceedings of the 5th international conference on Natural computation
PPCA-based license plate detection algorithm
ACM SIGSOFT Software Engineering Notes
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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 a new PCA-based method that can overcome one drawback existed in the traditional PCA method. In face recognition where the training data are labeled, a projection is often required to emphasize the discrimination between the clusters. PCA 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 a new PCA-based scheme 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 method.