Abstract
In this study, we propose a novel two-stages mixture of experts scheme estimating gender from facial images. The first stage combines a couple of complementary gender classifiers with a third arbiter in case of decision discrepancy. Experimentally, we have verified the common thinking that one appearance-based (Haar-features cascade) classifier with another shape-based (landmarks positions metrology with SVM) classifier form a complementary couple. Subsequently, the second stage in our scheme is a Bayesian framework that is activated only when the arbiter cannot take a confident decision. We demonstrate that the proposed scheme is capable of classifying gender reliably from faces as small as 16x16 thumbnails on benchmark databases, achieving 95% gender recognition on FERET database, and 91.5% on the Labeled Faces in the Wild dataset.
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El-Din, Y.S., Moustafa, M.N., Mahdi, H. (2013). Gender Classification Using Mixture of Experts from Low Resolution Facial Images. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_5
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DOI: https://doi.org/10.1007/978-3-642-38067-9_5
Publisher Name: Springer, Berlin, Heidelberg
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