Lighting-Variable AdaBoost Based-On System for Robust Face Detection

In order to detect faces in pictures presenting difficult real-world conditions such as dark background or backlighting, we propose a new method which is robust to varying illuminations and which automatically adapts itself to these lighting changes. The proposed face detection technique is based on an efficient AdaBoost super-classifier and relies on multiple features, namely, the global intensity average value and the local intensity variations. Based on tests carried out on standards datasets, our system successfully performs in indoor as well as outdoor situations with different lighting levels.