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%.
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© 2006 Springer-Verlag Berlin Heidelberg
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Tan, X., Liu, J., Chen, S. (2006). Recognition from a Single Sample per Person with Multiple SOM Fusion. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_20
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DOI: https://doi.org/10.1007/11760023_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
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