Effective and precise face detection based on color and depth data

In this work an effective face detector based on the well-known Viola–Jones algorithm is proposed. A common issue in face detection is that for maximizing the face detection rate a low threshold is used for classifying as face an input image, but at the same time using a low threshold drastically increases the number of false positives. In this paper several criteria are proposed for reducing false positives: (i) a skin detection step is used to reject a candidate face region that does not contain the skin color, (ii) the size of the candidate face region is calculated according to the depth data, removing the too small or the too large faces, (iii) images of flat objects (e.g. candidate face found in a wall) or uneven objects (e.g. candidate face found in the leaves of a tree) are removed using the depth map and a segmentation approach based both on color and depth data. The above criteria permit to drastically reduce the number of false positives without decreasing the detection rate. The proposed approach has been validated on three datasets composed of 180 samples including both 2D and depth images. The face position inside samples has been manually labeled for testing. A Matlab version of the system for face detection and the full testing dataset will be freely available from http://www.dei.unipd.it/node/2357.