Face detection using large margin classifiers

Ming-Hsuan Yang, Dan Roth, Narendra Ahuja

Abstract: Large margin classifiers have demonstrated their advantages in many visual learning tasks, and have attracted much attention in vision and image processing communities. We apply and compare two large margin classifiers, support vector machines and sparse network of winnows, to detect faces in still gray scale images Furthermore, we study the theoretical frameworks of these classifiers and analyze the empirical results. Experiments on a test set of 24,045 images exhibit good generalization and robustness, and conform to theoretical analysis.

External IDs:dblp:conf/icip/YangRA01