Abstract
In this paper, we focus on gender recognition in challenging large scale scenarios. Firstly, we review the literature results achieved for the problem in large datasets, and select the currently hardest dataset: The Images of Groups. Secondly, we study the extraction of features from the face and its local context to improve the recognition accuracy. Different descriptors, resolutions and classifiers are studied, overcoming previous literature results, reaching an accuracy of 89.8%.
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Castrillón-Santana, M., Lorenzo-Navarro, J., Ramón-Balmaseda, E. (2013). Improving Gender Classification Accuracy in the Wild. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41827-3_34
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DOI: https://doi.org/10.1007/978-3-642-41827-3_34
Publisher Name: Springer, Berlin, Heidelberg
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