Rapid stereo-vision enhanced face detection

This paper presents a real-time face detection algorithm. It improves state-of-the-art 2D object detection techniques by additionally evaluating a disparity map, which is estimated for the face region using a calibrated stereo camera setup. First, faces are detected in the 2D images with a rapid object classifier based on haar-like features. In a second step, falsely detected faces are removed by analyzing the disparity map. In the near field of the camera, a classifier is used, which evaluates the Eigenfaces of the normalized disparity map. Thereby, the transformation into Eigenspace is learned off-line using a principal component analysis approach. In the far field, a much simpler approach determines false-positives by evaluating the relationship between the size of the face in the image and its distance to the camera. This novel combination of algorithms runs in real-time and significantly reduces the number of false-positives compared to classical 2D face detection approaches.