Sex with Support Vector Machines

Ming-Hsuan Yang University of Illinois at Urbana-Champaign Urbana, IL 61801 USA mhyang avision.ai.uiuc.edu Abstract Nonlinear Support Vector Machines (SVMs) are investigated for visual sex classification with low resolution "thumbnail" faces (21-by-12 pixels) processed from 1,755 images from the FERET face database. Furthermore, the SVM performance (3.4% error) is currently the best result reported in the open literature. 1 Introduction In recent years, SVMs have been successfully applied to various tasks in computational face-processing.These include face detection [14], face pose discrimination [12] and face recognition [16]. Although facial sex classification has attracted much attention in the psychological literature [1, 4, 8, 15], relatively few computatinal learning methods have been proposed. We will briefly review and summarize the prior art in facial sex classification.