Accelerating Face Detection in Static Images with Fusion of RGB and Depth Data

Face detection is an important part of many computer vision systems and has several applications in areas, such as face tracking, visual surveillance, video conferencing, face recognition, intelligent human-computer interfaces and content-based information retrieval. For use of face detection in this applications, need a fast and precise face detection algorithm. But Detection speed of traditional face detection method based on AdaBoost algorithm is slow since an exhaustive search in image. Over the past few years, the availability of color images with corresponding depth data has increased due to the popularity of low-cost RGB-Depth cameras, notably Kinect. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in face detection with intelligently constraining search over the image. In this paper, utilize additional depth data to reduce the computational cost of face detection. Leveraging the additional depth images from a Kinect camera, and use of Recurring in nature idea, we are able to accelerate the Viola-Jones face detector by 270%.