Abstract
While feature selection is very difficult for high dimensional, unstructured data such as face image, it may be much easier to do if the data can be faithfully transformed into lower dimensional space. In this paper, a new method is proposed to transform the high dimensional face images into low-dimensional SOM topological space, and then identify important local features of face images for face recognition automatically using simple statistics computed from the class distribution of the face image data. The effectiveness of the proposed method are demonstrated by the experiments on AR face databases, which reveal that up to 80% local features can be pruned with only slightly loss of the classification accuracy.
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© 2005 Springer-Verlag Berlin Heidelberg
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Tan, X., Chen, S., Zhou, ZH., Zhang, F. (2005). Feature Selection for High Dimensional Face Image Using Self-organizing Maps. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_58
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DOI: https://doi.org/10.1007/11430919_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26076-9
Online ISBN: 978-3-540-31935-1
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