A Design of New Face/Non-face Classifier Based on Face Boundary Training

摘要

In this paper, we propose a new face detector that is less affected with background. To reduce the affect of various backgrounds, we apply more strong constraints to face. In previous works, classier in face detector determine that the input image is more like face or more like non-face, so the training set for non-face has more affect face detection. But to apply more strong constraints for face, the detector determines only whether the input image is like face or not, i.e. background has less affect in decision process. Constraints that used in this paper for face is how the image is look like face (image based), and that the image satisfies structural features of face, especially edge of face. The experimental result for proposed face/non-face classifier showed 95.8% classification rate of face and 96.5% classification rate of non-face with a small quantity of face image for a set of training.