Real-Time Human Face Detection in Noisy Images Based on Skin Color Fusion Model and Eye Detection

Abstract

Automatic human face detection is a challenging problem which has received much attention during recent years. In this paper, we propose a method that includes a denoising preprocessing step and a new face detection approach based on skin color fusion model and eye region detection. Preprocessing of the input images is concentrated on the removal of different types of noise while preserving the phase data. For the face detection process, firstly, skin pixels are modeled by using supervised training, and at the run time, the skin pixels are detected using optimal boundary conditions. Then by finding the eye location in the skin region, human face will be extracted. Experimental results obtained using images from the FEI, complex background, and noisy image database are promising in terms of detection rate and the false alarm rate in comparison with other competing methods. In addition, experimental results have demonstrated our method robust in successful detection of skin and face regions even with variant lighting conditions and poses.