AdaBoost Face Detection Based on Haar-Like Intensity Features and Multi-threshold Features

Effected by illumination and complex background, Haar-like feature values have a large change, and cannot sufficiently represent the face image texture information. By analyzing the distribution of Haar-like feature values, we propose a new type of classifiers called Haar-like intensity feature. Experimental results on some hand-labeled examples and MIT-CMU test dataset illustrate that the AdaBoost algorithm using the extensive features can reduce detection time and make higher face detection rate with fewer simple classifiers.