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Gender Classification via Gradientfaces

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Computer and Information Sciences

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 62))

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Abstract

In this paper illumination invariant, pose and facial expression tolerant gender classification method is proposed. A recently introduced feature extraction method, namely Gradientfaces, is utilized together with Support Vector Machine (SVM) as a classifier. Image regions obtained from cascaded Adaboost based face detector is used at the feature extraction step and faster classification is achieved by using only 20-by-20 pixel region during feature extraction. For performance evaluation, two well-known face databases, FERET and Yale B are tested and the algorithm is compared against a pixelbased algorithm on these datasets. The results indicate that Gradientfaces significantly outperform the pixel-based methods under severe illumination, pose and facial expression variances.

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Correspondence to K. Berker Loğoğlu .

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Loğoğlu, K.B., Saracoğlu, A., Esen, E., Alatan, A.A. (2011). Gender Classification via Gradientfaces. In: Gelenbe, E., Lent, R., Sakellari, G., Sacan, A., Toroslu, H., Yazici, A. (eds) Computer and Information Sciences. Lecture Notes in Electrical Engineering, vol 62. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9794-1_48

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  • DOI: https://doi.org/10.1007/978-90-481-9794-1_48

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-9793-4

  • Online ISBN: 978-90-481-9794-1

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