Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Principal Component Analysis of Gabor Filter Responses
RATFG-RTS '99 Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems
Unified Subspace Analysis for Face Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
IEEE Transactions on Image Processing
LDA/SVM driven nearest neighbor classification
IEEE Transactions on Neural Networks
Scene segmentation based on IPCA for visual surveillance
Neurocomputing
Review of existing algorithms for face detection and recognition
CIMMACS'09 Proceedings of the 8th WSEAS International Conference on Computational intelligence, man-machine systems and cybernetics
An improvement in feature feedback using R-LDA with application to Yale database
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Pixel selection based on discriminant features with application to face recognition
Pattern Recognition Letters
Fast multi-scale local phase quantization histogram for face recognition
Pattern Recognition Letters
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In this paper, a novel face recognition method based on Gabor-wavelet and linear discriminant analysis (LDA) is proposed. Given training face images, discriminant vectors are computed using LDA. The function of the discriminant vectors is two-fold. First, discriminant vectors are used as a transform matrix, and LDA features are extracted by projecting original intensity images onto discriminant vectors. Second, discriminant vectors are used to select discriminant pixels, the number of which is much less than that of a whole image. Gabor features are extracted only on these discriminant pixels. Then, applying LDA on the Gabor features, one can obtain the Gabor-LDA features. Finally, a combined classifier is formed based on these two types of LDA features. Experimental results show that the proposed method performs better than traditional approaches in terms of both efficiency and accuracy.