Sparse Models for Gender Classification

A class of sparse regularization functions are considered for the developing sparse classifiers for determining facial gender. The sparse classification method aims to both select the most important features and maximize the classification margin, in a manner similar to support vector machines. An efficient process for directly calculating the complete set of optimal, sparse classifiers is developed. A single classification hyper-plane which maximizes posterior probability of describing training data is then efficiently selected. The classifier is tested on a Japanese gender-divided ensemble, described via a collection of appearance models. Performance is comparable with a linear SVM, and allows effective manipulation of apparent gender.

• Type: Conference
• Year: 2004
• Where: FGR
• Authors: Nicholas Costen, Martin Brown, Shigeru Akamatsu
• Keywords: Biometrics, FGR 2004, Sparse Classification Method, Sparse Classifiers, Sparse Regularization Functions