Abstract
In this paper, a kernel-based SOM-face method is proposed to recognize expression variant faces under the situation of only one training image per person. Based on the localization of the face, an unsupervised kernel-SOM learning procedure is carried out to capture the common local features and the non-Euclidean structure of the image data, so that a compact and robust representation of the face can be obtained. Experiments on the FERET face database show that the Kernel-based SOM-face method can obtain higher recognition performance than the regular SOM-face method.
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Tan, X., Chen, S., Zhou, ZH., Zhang, F. (2004). Robust Face Recognition from a Single Training Image per Person with Kernel-Based SOM-Face. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_141
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DOI: https://doi.org/10.1007/978-3-540-28647-9_141
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
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