Range Face Segmentation: Face Detection and Segmentation for Authentication in Mobile Device Range Images

Face detection (finding faces of different perspectives in images) is an important task as prerequisite to face recognition. This is especially difficult in the mobile domain, as bad image quality and illumination conditions lead to overall reduced face detection rates. Background information still present in segmented faces and unequally normalized faces further decrease face recognition rates. We present a novel approach to robust single upright face detection and segmentation from different perspectives based on range information (pixel values corresponding to the camera-object distance). We use range template matching for finding the face's coarse position and gradient vector flow (GVF) snakes for precisely segmenting faces. We further evaluate our approach on range faces from the u'smile face database, then perform face recognition using the segmented faces to evaluate and compare our approach with previous research. Results indicate that range template matching might be a good approach to finding a single face; in our tests we achieved an error free detection rate and average recognition rates above 98%/96% for color/range images.