Comparing the Performance of Single-Layer and Two-Layer Support Vector Machines on Face Detection

Face detection is a vibrant research branch of computer vision. Methods of detecting faces fall into two categories: global and component-based. In this paper, we compare these two approaches by applying a single-layer and a dual-layer support vector machine classifier to detect faces from images. Experiments suggest that the single-layer classifier has better performance on detecting faces with big attitude extremity. But the dual-layer classifier has equivalent performance on detecting frontal faces and has more generality on different databases.