Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
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
Automatic Video Indexing and Full-Video Search for Object Appearances
Proceedings of the IFIP TC2/WG 2.6 Second Working Conference on Visual Database Systems II
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Journal of Cognitive Neuroscience
Face recognition using HOG-EBGM
Pattern Recognition Letters
A Face Recognition Algorithm Decreasing the Effect of Illumination
IIH-MSP '08 Proceedings of the 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing
Centroid neural network with chi square distance measure for texture classification
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Eigenspace-based face recognition: a comparative study of different approaches
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Face Recognition by Regularized Discriminant Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Centroid neural network for unsupervised competitive learning
IEEE Transactions on Neural Networks
Weighted centroid neural network for edge preserving image compression
IEEE Transactions on Neural Networks
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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This paper presents a feature extraction method from facial images and applies it to a face recognition problem The proposed feature extraction method, called gradient-based local descriptor (GLD), first calculates the gradient information of each pixel and then forms an orientation histogram at a predetermined window for the feature vector of a facial image The extracted features are combined with a centroid neural network with the Chi square distance measure (CNN-χ2) for a face recognition problem The proposed face recognition method is evaluated using the Yale face database The results obtained in experiments imply that the CNN-χ2 algorithm accompanied with the GLD outperforms recent state-of-art algorithms including the well-known approaches KFD (Kernel Fisher Discriminant based on eigenfaces), RDA (Regularized Discriminant Analysis), and Sobel faces combined with 2DPCA (two dimensional Principle Component Analysis) in terms of recognition accuracy.