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.<br>
<br><pre>@inproceedings{Findling_13_RangeFaceSegmentation,
  author = {Findling, Rainhard Dieter and Wenny, Fabian and Mayrhofer, Ren\'e and Holzmann, Clemens},
  title = {Range Face Segmentation: Face Detection and Segmentation
  		  for Authentication in Mobile Device Range Images},
  booktitle = {Proc. {MoMM} 2013: 11th International Conference on
  		  Advances in Mobile Computing and Multimedia},
  year = {2013},
  pages = {260--269},
  address = {New York, NY, USA},
  month = dec,
  publisher = {ACM},
  doi = {10.1145/2536853.2536880},
  keywords = {Range images, mobile device, face detection, face
  		  segmentation, template matching, snakes},
  url = {https://dl.acm.org/citation.cfm?id=2536880}
}
</pre>