Towards absolute invariants of images under translation, rotation, and dilation
Pattern Recognition Letters
A complete invariant description for gray-level images by the harmonic analysis approach
Pattern Recognition Letters
Representations that uniquely characterize images modulo translation, rotation, and scaling
Pattern Recognition Letters
Using Discriminant Eigenfeatures for Image Retrieval
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition with one training image per person
Pattern Recognition Letters
Membership authentication in the dynamic group by face classification using SVM ensemble
Pattern Recognition Letters
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
A new matching strategy for content based image retrieval system
Applied Soft Computing
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At present there are many methods that could deal well with frontal view face recognition. However, most of them cannot work well when there is only single example image per person. In order to deal with this problem of single example image per person is stored in the system in the real-world application. In this paper, a comparative study of the AFMT, Fourier-AFMT and Taylor-AFMT are identified for face recognition. And then, we present hybrid Fourier-AFMT framework based face recognition for feature extraction. Firstly, both directionality of edges and intensity facial features are extracted and secondly fuse two kinds of features and classify with correlation coefficient method (CCM). Experiments are implemented on YALE and ORL face databases to demonstrate the efficient of proposed methods. The experimental results show that the average recognition accuracy rates of our proposed fuse multiple feature domains much higher than that of single feature domain.