Support Vector Learning for Gender Classification
Computer vision systems for people monitoring will eventually play an important role in our lives by means of automated human (face) detection, body tracking, action (gesture) recognition, person identification and estimation of age and gender. We have developed a facial gender classifier using Support Vector Machine (SVM) learning with performance superior to existing gender classifiers. This technology can, for example, be used for passive surveillance and control in "smart buildings" as well as gender-mediated HCI.
Background & Objective: This project addresses the problem of classifying gender from low-resolution images in which only the main facial regions appear (i.e., without hair information). We wanted to investigate the minimal amount of face information (resolution) required to learn male and female faces by various pattern classifiers. Previous studies on gender classification have relied on high-resolution images with hair information and used relatively small datasets for their experiments. In our study, we demonstrate that SVM classifiers are able to learn and classify gender from a large set of hairless low-resolution images with the highest accuracy.
Technical Discussion: A Support Vector Machine is a learning algorithm for pattern classification and regression. The basic principle behind SVMs is finding the optimal linear (or nonlinear) hyperplane (see above figure) such that the expected classification error for unseen test samples is minimized (i.e., good generalization performance). We investigated the utility of SVMs for visual gender classification with low-resolution "thumbnail" faces (21-by-12 pixels) processed from 1,755 images from the FERET 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.
| Technical Reports: | |
| Learning Gender with Support Faces | |
| Gender Classification with Support Vector Machines | |
Technology Areas:
Computer Vision
Artificial Intelligence
Modification Date: September 12, 2007