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Published in:
2012 | OriginalPaper | Chapter
Gender Classification in Large Databases
Authors : Enrique Ramón-Balmaseda, Javier Lorenzo-Navarro, Modesto Castrillón-Santana
Published in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Publisher: Springer Berlin Heidelberg
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In this paper, we address the challenge of gender classification using large databases of images with two goals. The first objective is to evaluate whether the error rate decreases compared to smaller databases. The second goal is to determine if the classifier that provides the best classification rate for one database, improves the classification results for other databases, that is, the cross-database performance.