Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Support vector machines applied to face recognition
Proceedings of the 1998 conference on Advances in neural information processing systems II
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Face Similarity Space as Perceived by Humans and Artificial Systems
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Face Recognition Based on Multiple Facial Features
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Gender Classification with Support Vector Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
An Experimental Evaluation of Linear and Kernel-Based Methods for Face Recognition
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Optimal Gabor Filters for High Speed Face Identification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
A Comparison of Gabor Filter Methods for Automatic Detection of Facial Landmarks
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
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
Face Recognition Using Kernel Based Fisher Discriminant Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
An embedded HMM-based approach for face detection and recognition
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Journal of Cognitive Neuroscience
IEEE Transactions on Image Processing
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Information theory for Gabor feature selection for face recognition
EURASIP Journal on Applied Signal Processing
A two-stage head pose estimation framework and evaluation
Pattern Recognition
Combining Global and Local Classifiers for Lipreading
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Facial recognition using multisensor images based on localized kernel eigen spaces
IEEE Transactions on Image Processing
Fusion of support vector classifiers for parallel gabor methods applied to face verification
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
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A novel Gabor-Kernel face recognition method is proposed in this paper. This involves convolving a face image with a series of Gabor wavelets at different scales, locations, and orientations. Kernel methods such as Kernel Principal Component Analysis (KPCA) and Kernel Discriminant Analysis (KDA) are then applied to the feature vectors for dimension reduction as well as class separability enhancement. A database of 600 frontal-view face images from the FERET face database is used to test the method. Experimental results demonstrate the advantage of Kernel methods over classical Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Significant improvements are also observed when the Gabor filtered images are used for feature extraction instead of the original images. The Gabor + KDA method achieves 92% recognition accuracy using only 35 features of a face image.