Abstract
Granular computing is a popular method of attribute reduction and the granularity is divided to two kinds coarse-grain and fine-grain. However, it is hard to determine the size of granularity normally. This paper presents a method to divide the original features from the actual condition of facial feature selected in Gabor. Then we use binary encoding properly to arrange the data based on the number of the samples. When the information of more coarse grain becomes simplified it can be reduced in granular computing. The experiment shows that this method can remove redundancy accurately. It will reduce the running time and improve the operation efficiency.
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He, R., He, N. (2011). The Reduction of Facial Feature Based on Granular Computing. In: Hu, W. (eds) Electronics and Signal Processing. Lecture Notes in Electrical Engineering, vol 97. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21697-8_129
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DOI: https://doi.org/10.1007/978-3-642-21697-8_129
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21696-1
Online ISBN: 978-3-642-21697-8
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