Combined AdaBoost and gradientfaces for face detection under illumination problems

Regardless of several different methods for face detection have been developed in the last years, there are still situations that requires more improvements especially in issues related to variations in illumination and face occlusion. Illumination problems are normally handled by using preprocessing, and model or training-based approaches. We propose here a face detection method combining the well-known AdaBoost with Gradientfaces following a model-based approach, which was not yet used for the face detection problem. We have applied Gradientfaces before training an AdaBoost Haar-based cascade classifier to overcome the problem of strong variations in illumination. Cited approaches were evaluated first in a data set containing 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.