학술논문
Sparse models for gender classification
Document Type
Conference
Author
Source
Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings. Automatic face gesture recognition Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on. :201-206 2004
Subject
Language
Abstract
A class of sparse regularization functions is 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.