Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Improved-LDA based face recognition using both facial global and local information
Pattern Recognition Letters
On transforming statistical models for non-frontal face verification
Pattern Recognition
Journal of Cognitive Neuroscience
Matching pursuit filters applied to face identification
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Rotation-invariant multiresolution texture analysis using Radon and wavelet transforms
IEEE Transactions on Image Processing
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
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This paper presents an in-plane rotation (tilt), illumination invariant pattern recognition framework based on the combination of the features extracted using Radon and discrete cosine transforms and kernel based learning for face recognition. The use of Radon transform enhances the low frequency components, which are useful for face recognition and that of DCT yields low dimensional feature vector. The proposed technique computes Radon projections in different orientations and captures the directional features of the face images. DCT applied on Radon projections provides frequency features. Further, polynomial kernel Fisher discriminant analysis implemented on these features enhances discrimination capability of these features. The technique is also robust to zero mean white noise. The feasibility of the proposed technique has been evaluated using FERET, ORL, and Yale databases.