Statistical Learning of Multi-view Face Detection

A new boosting algorithm, called FloatBoost, is proposed to overcome the monotonicity problem of the sequential AdaBoost learning. AdaBoost [1, 2] is a sequential forward search procedure using the greedy selection strategy. The premise oÿered by the sequential procedure can be broken-down when the monotonicity assumption, i.e. that when adding a new feature to the current set, the value of the performance criterion does not decrease, is violated. FloatBoost incorporates the idea of Floating Search [3] into AdaBoost to solve the non-monotonicity problem encountered in the sequential search of AdaBoost. We then present a system which learns to detect multi-view faces using FloatBoost. The system uses a coarse-to-þne, simple-to-complex architecture called detector-pyramid. FloatBoost learns the component detectors in the pyramid and yields similar or higher classiþcation accuracy than AdaBoost with a smaller number of weak classiþers. This work leads to the þrst real-time multi-view face detection system in the world. It runs at 200 ms per image of size 320x240 pixels on a Pentium-III CPU of 700 MHz. A live demo will be shown at the conference.