Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Journal of Cognitive Neuroscience
Recent advances in ear biometrics
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Null space-based kernel fisher discriminant analysis for face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
IEEE Transactions on Image Processing
On guided model-based analysis for ear biometrics
Computer Vision and Image Understanding
A review of recent advances in 3D ear- and expression-invariant face biometrics
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
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Many natural objects such as face and ear manifest symmetry. The mirror images of symmetrical objects also encode significant discriminative information, which is of benefit to recognition performance. In this paper, a novel symmetrical null space method with the even-odd decomposition principle is proposed for face and ear recognition. By introducing mirror images, the two orthogonal even/odd eigenspaces are constructed. Then the discriminative features are, respectively, extracted from the two eigenspaces under the most suitable situation of the null space. Finally, all the features are combined for classification. Experimental results on both face database and ear database demonstrate the performance of the proposed method.