AbstractÐNonlinear Support Vector Machines (SVMs) are investigated for appearance-based gender classification with low-resolution ªthumbnailº faces processed from 1,755 images from the FERET face database. The performance of SVMs (3.4 percent 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 ªthumbnailsº (21-by-12 pixels) and the corresponding higher resolution images (84-by-48 pixels) was found to be only 1 percent, thus demonstrating robustness and stability with respect to scale and degree of facial detail.
In REF , non-linear support vector machine (SVM) is used to classify faces from thumbnail images with low resolutions.
Learning gender with support faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Science