Hybrid face detection with skin segmentation and edge detection

Face detection for low quality images and different face positions is a very challenging task. This paper presents a hybrid method for face detection to these problems. The algorithm starts with image resizing process followed by the Gaussian Mixture Model to calculate the skin likelihood value of pixel in an image. Then, the skin regions are extracted from the background with a proper threshold value obtained adaptively based on image information. This study developed an algorithm to performed face location and detection. This study used face database from University of Ljubljana (Slovenia) Computer Vision Laboratory (CVL), which contains seven 2D images corresponding to 114 different individuals, to evaluate the proposed system. The resolution of the images is 640*480 pixels. Another database, the Bao database which consists of 157 images with image resolutions within 57×85 pixels and 300 × 300 pixels is chosen. The detection accuracy for frontal face and side face images on CVL database is 94.4% and 84.7% respectively. The detection accuracy on Bao database is 93.6%.