Learning to detect multi-view faces in real-time

In this paper, we present a system which learns to detect multi-view faces. The system uses a coarse-to-fine, simple-to-complex architecture called detector-pyramid. A new boosting algorithm, called FloatBoost, is proposed to construct a strong face-nonface classifier from weak classifiers for the component detectors in the pyramid. FloatBoost incorporates the idea of Floating Search into AdaBoost, and yields similar or higher classification accuracy than AdaBoost with a smaller number of weak classifiers. This work leads to the first real-time multi-view face detection system in the world. It runs at 200 ms per image of size 320×240 pixels on a Pentium-III CPU of 700 MHz.