Evaluation of gender classification methods with automatically detected and aligned faces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Erno Mäkinen, Roope Raisamo

Abstract

We present a systematic study on gender classification with automatically detected and aligned faces. We experimented with 120 combinations of automatic face detection, face alignment and gender classification. One of the findings was that the automatic face alignment methods did not increase the gender classification rates. However, manual alignment increased classification rates a little, which suggests that automatic alignment would be useful when the alignment methods are further improved. We also found that the gender classification methods performed almost equally well with different input image sizes. In any case, the best classification rate was achieved with a support vector machine. A neural network and Adaboost achieved almost as good classification rates as the support vector machine and could be used in applications where classification speed is considered more important than the best possible classification accuracy.

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Citations

Jun 4, 2014·TheScientificWorldJournal·Ming Wu, Yubo Yuan
May 12, 2010·Neural Networks : the Official Journal of the International Neural Network Society·Liang ChenNaoyuki Tokuda
Jun 24, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Fahad Shahbaz KhanCarlo Gatta
Jun 1, 2014·IEEE Transactions on Pattern Analysis and Machine Intelligence· Weihong Deng Jun Guo
Jun 13, 2013·IEEE Transactions on Cybernetics·Bruno José Torres FernandesTsang Ing Ren
Jul 4, 2009·IEEE Transactions on Pattern Analysis and Machine Intelligence·Matthew Toews, Tal Arbel
Dec 8, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·Juan Bekios-CalfaLuis Baumela
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Mar 15, 2016·Journal of the Optical Society of America. A, Optics, Image Science, and Vision·Wenhao ZhangAbdul Farooq

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