The nature of statistical learning theory
The nature of statistical learning theory
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
A review on Gabor wavelets for face recognition
Pattern Analysis & Applications
Gabor wavelets and General Discriminant Analysis for face identification and verification
Image and Vision Computing
Gabor wavelets and General Discriminant Analysis for face identification and verification
Image and Vision Computing
Journal of Cognitive Neuroscience
Support Vector Machines for Pattern Classification
Support Vector Machines for Pattern Classification
Geometrically invariant image watermarking using SVR correction in NSCT domain
Computers and Electrical Engineering
Computers and Electrical Engineering
A non-reference image fusion metric based on mutual information of image features
Computers and Electrical Engineering
Multi-focus image fusion for visual sensor networks in DCT domain
Computers and Electrical Engineering
Computers and Electrical Engineering
Using Support Vector Machines to Enhance the Performance of Bayesian Face Recognition
IEEE Transactions on Information Forensics and Security
Scene categorization based on integrated feature description and local weighted feature mapping
Computers and Electrical Engineering
IEEE Transactions on Image Processing
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Face Recognition Using Total Margin-Based Adaptive Fuzzy Support Vector Machines
IEEE Transactions on Neural Networks
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This paper presents a novel and uniform framework for face recognition. This framework is based on a combination of Gabor wavelets, direct linear discriminant analysis (DLDA) and support vector machine (SVM). First, feature vectors are extracted from raw face images using Gabor wavelets. These Gabor-based features are robust against local distortions caused by the variance of illumination, expression and pose. Next, the extracted feature vectors are projected to a low-dimensional subspace using DLDA technique. The Gabor-based DLDA feature vectors are then applied to SVM classifier. A new kernel function for SVM called hyperhemispherically normalized polynomial (HNP) is also proposed in this paper and its validity on the improvement of classification accuracy is theoretically proved and experimentally tested for face recognition. The proposed algorithm was evaluated using the FERET database. Experimental results show that the proposed face recognition system outperforms other related approaches in terms of recognition rate.