Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ordinal Measures for Image Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ordinal-measure based shape correspondence
EURASIP Journal on Applied Signal Processing - Image analysis for multimedia interactive services - part I
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Face recognition using multi-feature and radial basis function network
VIP '02 Selected papers from the 2002 Pan-Sydney workshop on Visualisation - Volume 22
Journal of Cognitive Neuroscience
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Face recognition using LDA-based algorithms
IEEE Transactions on Neural Networks
Independent component analysis of Gabor features for face recognition
IEEE Transactions on Neural Networks
Face recognition by applying wavelet subband representation and kernel associative memory
IEEE Transactions on Neural Networks
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In this paper, we propose a new face recognition system based on the ordinal correlation principle. First, we explain the ordinal similarity measure for any two images and then propose a systematic approach for face recognition based on this ordinal measure. In addition, we design an algorithm for selecting a suitable classification threshold via using the information obtained from the training database. Experimentation is conducted on the Yale datasets and the results show that the proposed face recognition approach outperforms the Eigenface and 2DPCA approaches significantly and also the threshold selection algorithm works effectively. Further, we carry out experimentation with various noise algorithms and the results show that the ordinal approach outperforms the Eigenface and 2DPCA approaches under different noise algorithms.