Abstract
In this paper we have proposed a gender recognition system through facial images. We have used three different techniques that involve Bandlet Trans-form (a multi-resolution technique), LBP (Local Binary Pattern) and mean to create the feature vectors of the images. To classify the images for gender, we have used fuzzy c mean clustering. SUMS and FERET databases were used for testing. Experimental results have shown that the maximum average accuracy was achieved using SUMS, 97.1% has been achieved using Band-lets and mean technique, Bandlets and whole image LBP has shown 85.13% and Bandlets with blocked based LBP has shown 87.02% average accuracy.
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Shabbir, Z., Khan, A.U., Irtaza, A., Mahmood, M.T. (2015). A Fuzzy Logic Approach for Gender Recognition from Face Images with Embedded Bandlets. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_56
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DOI: https://doi.org/10.1007/978-3-319-19324-3_56
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