An Efficient Skin Illumination Compensation Model for Efficient Face Detection

Face detection (human) plays an important role in applications such as human computer interface, face recognition video surveillance and face image database management. In the human face detection applications, face(s) most frequently form an inconsequential part of the images. Consequently, preliminary segmentation of images into regions that contain "non-face" objects and regions that may contain "face" candidates can greatly accelerate the process of human face detection. Most existing face detection approaches have assumptions, which make them applicable only under some specific conditions. Existing techniques for face detection in colour images are plagued by poor performance in the presence of scale variation, variation in illumination, variation in skin colours, complex backgrounds etc. In this research work we have made a humble attempt to propose an algorithm for face detection in colour images in the presence of varying lighting conditions, for varied skin colours as well as with complex backgrounds. Based on a novel tangible skin component extraction modus operandi and detection of the valid face candidates, our method detects skin regions over the entire image and engenders face candidates based on the signatures of the detected skin patches. The algorithm constructs the boundary for each face candidate. Experimental results demonstrate successful face detection over a wide range of facial variations in colour, position, scale, varying lighting conditions, orientation, 3D pose, and expression in images from the database which is enriched with several photo collections (both indoors and outdoors with all the above mentioned extreme cases taken into consideration). In view of the capability to handle high degree of variability, that our approach can handle we articulate that our approach outperforms all the published popular methods