Title :
Learning gender with support faces
Author :
Moghaddam, Baback ; Yang, Ming-Husan
Author_Institution :
Mitsubishi Electr. Res. Labs., Cambridge, MA, USA
fDate :
5/1/2002 12:00:00 AM
Abstract :
Nonlinear support vector machines (SVMs) are investigated for appearance-based gender classification with low-resolution "thumbnail" faces processed from 1,755 images from the FERET (FacE REcognition Technology) face database. The performance of SVMs (3.4% error) is shown to be superior to traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, nearest-neighbor) as well as more modern techniques, such as radial basis function (RBF) classifiers and large ensemble-RBF networks. Furthermore, the difference in classification performance with low-resolution "thumbnails" (21×12 pixels) and the corresponding higher-resolution images (84×48 pixels) was found to be only 1%, thus demonstrating robustness and stability with respect to scale and the degree of facial detail
Keywords :
face recognition; gender issues; image classification; image resolution; learning (artificial intelligence); learning automata; radial basis function networks; FERET face database; Fisher linear discriminant classifiers; appearance-based gender classification; classification performance; ensemble-RBF networks; face recognition technology; facial detail; high-resolution images; learning; linear classifiers; low-resolution thumbnail images; nearest-neighbor classifiers; nonlinear support vector machines; pattern classifiers; quadratic classifiers; radial basis function networks; robustness; scale; stability; Face recognition; Image databases; Pixel; Robust stability; Support vector machine classification; Support vector machines;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
