Recognition from a single sample per person with multiple SOM fusion

Authors:
Xiaoyang Tan;Jun Liu;Songcan Chen
Affiliations:
Department of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, China;Department of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, China;Department of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 6
Cited 1

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Face recognition: A literature survey

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Enhanced (PC)2 A for face recognition with one training image per person

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Using the original and 'symmetrical face' training samples to perform representation based two-step face recognition

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Abstract

One of the main challenges faced by the current face recognition techniques lies in the difficulties of collecting samples, and many existing face recognition techniques rely heavily on the size and representative of training set. Those algorithms may suffer serious performance drop or even fail to work if only one training sample per person is available to the systems. In this paper, we present a multiple-SOMs-based fusion method to address this problem. Based on the localization of the face, multiple Self-Organizing Maps are constructed in different manners, and then fused to obtain a more compact and robust representation of the face, through which the discrimination and class-specific information can be easily explored from the single training image among a large number of classes. Experiments on the FERET face database show that the proposed fusion method can significantly improve the performance of the recognition system, achieving a top 1 matching rate of 90.0%.