Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Pose Invariant Face Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Nonparametric discriminant analysis and nearest neighbor classification
Pattern Recognition Letters
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Journal of Cognitive Neuroscience
Robust face-tracking using skin color and facial shape
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Removing Pose from Face Images
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Cross-pose face recognition based on partial least squares
Pattern Recognition Letters
Video-to-video face authentication system robust to pose variations
Expert Systems with Applications: An International Journal
Local Linear Regression on Hybrid Eigenfaces for Pose Invariant Face Recognition
International Journal of Computer Vision and Image Processing
Robust frontal view search using extended manifold learning
Journal of Visual Communication and Image Representation
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Recognizing human faces is one of the most important areas of research in biometrics. However, drastic change of facial poses is a big challenge for its practical application. This paper proposes generating frontal view face image using linear transformation in feature space for face recognition. We extract features from a posed face image using the kernel PCA. Then, we transform the posed face image into its corresponding frontal face image using the transformation matrix predetermined by learning. Then, the generated frontal face image is identified by three different discrimination methods such as LDA, NDA, or GDA. Experimental results show that the recognition rate with the pose transformation outperforms that without pose transformation greatly.