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%. © Springer-Verlag 2013.
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CITATION STYLE
Castrilloń-Santana, M., Lorenzo-Navarro, J., & Ramoń-Balmaseda, E. (2013). Improving gender classification accuracy in the wild. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8259 LNCS, pp. 270–277). https://doi.org/10.1007/978-3-642-41827-3_34
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