Face detection under illumination variance using combined AdaBoost and gradientfaces

Abstract

Face detection is a research area in computer vision that has received much attention in recent years and a lot of commercial applications were developed using this technology. Even though several methods have been developed, there are still some particular situations that need improvements, especially those related to variations in illumination and face occlusions. In general, illumination problems are handled by using preprocessing, and model or training-based approaches. Here, we propose a face detection method that combines well-known AdaBoost with Gradientfaces technique following a model-based approach, which was not yet used for the face detection problem. We applied Gradientfaces before training an AdaBoost Haar-based cascade classifier to overcome the problem of strong variations in illumination. Quoted approaches were evaluated in two different datasets, containing first artificial and then real illumination problems. Experiments show that proposed method is stable when facing different lighting conditions, and better than others when dealing with strong and uncontrolled illumination problems. © 2012 Springer-Verlag.