Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Principal Component Analysis
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Local Linear Discriminant Analysis Framework Using Sample Neighbors
IEEE Transactions on Neural Networks
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Sparse representation method, especially the Two-Phase Test Sample Representation (TPTSR) method is regarded as a powerful algorithm for face recognition. The TPTSR method is a two-phase process in which finds out the M nearest neighbors to the testing sample in the first phase, and classifies the testing sample into the class with the most representative linear combination in the second phase. However, this method is limited by the overwhelming computational load, especially for a large training set and big number of classes. This paper studies different nearest neighbor selection approaches for the first phase of TPTSR in order to reduce the computational expenses of face recognition. Experimental results and theoretical analysis show that computational efficiency can be significantly increased by using relatively more straightforward criterions while maintaining a comparable classification performance with the original TPTSR method.