A novel approach for face detection using hybrid skin color model

Abstract

Human face detection has become an area of interest in various biometric applications such as crowd surveillance, human–computer interaction, and many security related areas. It is a major field of current research because there is no deterministic algorithm to find the face(s) in a given image. Face detection is challenging due to varying illumination conditions, pose variations, the complexity of noises and image backgrounds. In this paper, a localized approach for face detection based on skin color segmentation and facial features is proposed. Algorithm proficiently analyzes various skin color models such as RGB, YCbCr, HSV and their combinations for the skin color detection because skin segmentation decreases the computational complexity. The segmented face regions are further classified using a set of facial features such as eye-mouth hole detection, bounding box, and eccentricity ratio. This method is tested on four databases: Bao database contains 157 images, Muct database contains 751 images, FEI database contains 2800 images and GTAV database contains 1188 images. The algorithm achieves an average accuracy of 97.85%. Comparison with Viola Jones, face detection using skin color model, and fast face detection based on skin segmentation and facial features methods has also been done. Further, we apply this method to cartoon characters. Our method works with images with some conditions. First, input images are not monochrome images but color images. Second, the characters skin color has to be near real people skin color. Skin color region is extracted from the image and after this apply the facial features to locate faces in an image. The proposed approach for cartoon characters faces detection performed well and achieves a good accuracy of 76.00%.