A Robust Multi-Model Approach for Face Detection in Crowd

Abstract

The estimation of the number of people in surveillance areas is essential for monitoring crowded scenes. When density of a zone increases to a certain approximated level, people's safety can be endangered. Detection of human is a prerequisite for density estimation, tracking, activity recognition and anomaly detection even in non congested areas. This paper presents a robust hybrid approach for face detection in crowd by combining the skin color segmentation and a Histogram of Oriented Gradients(HOG) with Support Vector Machine(SVM) architecture. Initially, image enhancement is performed to improve the detection rate. An edge preserving pyramidal approach is applied for multiscale representation of an image. Skin color segmentation is done with combination of YCbCr and RGB color model, and HOG features are extracted from the segmented skin region. We trained the SVM classifier by Muct and FEI databases which consist 751 and 2800 face images respectively. The accuracy of this approach is evaluated by testing it on BAO multiple face database and on various manually collected images captured in surveillance areas. Experimental results demonstrate that the supplementary skin color segmentation with HOG is more potent for increasing the detection rate than using HOG features only. The proposed approach achieves 98.02% accuracy which is higher in comparison to Viola Jones and fast face detection method.