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. © 2012 Springer-Verlag.
CITATION STYLE
Ramón-Balmaseda, E., Lorenzo-Navarro, J., & Castrillón-Santana, M. (2012). Gender classification in large databases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7441 LNCS, pp. 74–81). https://doi.org/10.1007/978-3-642-33275-3_9
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