Real-time face detection using AdaBoot algorithm
Cheng Han, Kwee-Bo Sim · 2008
In this paper, we propose to use the AdaBoost algorithm for face detection. AdaBoost is a kind of large margin classifiers and is efficient for on-line learning. In order to adapt the AdaBoost algorithm to fast face detection, use the original AdaBoost algorithm, the original AdaBoost which uses a given features is compared with the boosting along feature dimensions. The comparable results assure the use of the latter, which is faster for classification. The AdaBoost is typically a classification between two classes. This face detection system operates without the aid of initializing stage and realizes automatic face detection system. The overall structure adopts window scanning and image pyramid structure so that various size of face is allowed to be detected. In addition, real-time performance rate can be achieved through constituting strong classifier with extracting a few but efficient weak classifiers by the AdaBoost learning.