Robust face detection using individual face parts classifiers based on AdaBoost
Kiyoto Ichikawa, Takeshi Mita, Osamu Hori · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
We present a robust frontal face detection method that enables the identification of face positions in images by combining the results of a low-resolution whole face and individual face parts classifiers. Our approach is to use face parts information and change the identification strategy based on the results from individual face parts classifiers. Faces are detected by scanning the classifiers into an input image. The classifiers for whole face and individual face parts detection were implemented based on an AdaBoost algorithm. We propose a novel method based on a decision tree to improve performance of face detectors for occluded faces. The proposed decision tree method distinguishes partially occluded faces based on the results from the individual classifies. Preliminarily experiments on a test sample set containing non-occluded faces and occluded faces indicated that our method achieved better results than conventional methods. Actual experimental results containing real images also showed better results.