Improvement of Face Detection Incorporating Illumination-based Robust Skin Color Measure

Abstract

Human vision system is amazing in detecting face easily but it is very challenging in computer vision and image processing as it depends on quality of image, illumination, lighting conditions, face sizes, occlusions, and face position etc. The existing face detection systems, including popular Haar feature based face detection (HFFD), very often detect a region as a face which is eventually not a face. To counteract such false detection, incorporation of human skin color property is considered a way of improving face detection accuracy in several recent studies. But these methods are found to be dependent on illumination conditions meaning that the performances of these methods degrade when applied to images with different illumination conditions. The aim of this study is to devise a robust face detection system integrating skin color matching that will perform well under different illumination conditions. In pursuit of this goal, a novel skin color matching method is proposed which is a composite of two rules to balance the high and low intensity facial images by individual rule. In the proposed method, illumination intensity of a given facial area is measured and then appropriate rule is applied based intensity value to verify the area as face or not. The proposed skin color matching is verified in face detection with HFFD on four benchmark face datasets (Put, Caltech, Bao and Muct) and a self-prepared dataset. Experimental results and analysis revealed the effectiveness of proposed composite skin color matching to improve face detection while compared with prominent existing skin color-based face detection methods.