Personal mobile devices hold a vast amount of private and
		  sensitive data and can e. g. be used to access services
		  with associated cost. For security reasons, most mobile
		  platforms therefore implement automatic device locking
		  after a period of inactivity. Unlocking them using
		  approaches like PIN, password or an unlock pattern is both
		  problematic in terms of usability and potentially insecure,
		  as it is prone to the shoulder surfing attack: an attacker
		  watching the display during user authentication. Hence,
		  face unlock – using biometric face information for
		  authentication – was developed as a more secure as well
		  as more usable personal device unlock. Unfortunately, when
		  using frontal face information only, authentication can
		  still be circumvented by a photo attack: presenting a
		  photo/video of the authorized person to the camera. In this
		  work we present a variant of face unlock which is harder to
		  circumvent than with using frontal face information only by
		  using more facial information, available during a 180∘
		  pan shot around the user’s head. We develop and evaluate
		  our mobile device pan shot face unlock in four different
		  stages in order to identify conceptual weaknesses and do
		  improvements within the next stage. In the first stage we
		  present a proof-of-concept prototype based on Android,
		  which uses different Viola and Jones Haar-cascades for face
		  detection and Eigenfaces for face recognition. We identify
		  Eigenfaces as being insufficient for usage in a mobile
		  device unlocking scenario. Therefore, we utilize neural
		  networks and support vector machines for face recognition
		  in the next stage, with which we identify using Viola and
		  Jones based face detection as being insufficient for usage
		  in a mobile device pan shot unlocking scenario based on
		  multiple perspectives. Hence, we develop a novel face
		  detection and segmentation approach based on stereo vision
		  and range template matching in the next stage, which we
		  find to deliver promising results and consequently focus on
		  improving details of the range template generation and
		  matching within the fourth and last stage. Parallel to
		  developing and evaluating our approach we build up the
		  u’smile face database containing grayscale and stereo
		  vision pan shot test data. Concluding, our results indicate
		  that a mobile device pan shot face unlock is a viable
		  approach to unlocking mobile devices and that using range
		  information might in general be an effective approach for
		  incorporated face detection and segmentation.<br>
<br><pre>@thesis{Findling_13_PanShotFace,
  author = {Findling, Rainhard Dieter},
  title = {Pan Shot Face Unlock: Towards Unlocking Personal Mobile
  		  Devices using Stereo Vision and Biometric Face Information
  		  from multiple Perspectives},
  month = sep,
  year = {2013},
  note = { {Winner of the OCG Incentive Award FH 2014 and the
  		  IFAC Fred Margulies Award 2015}},
  institution = {Department of Mobile Computing, School of Informatics,
  		  Communication and Media, University of Applied Sciences
  		  Upper Austria},
  location = {Softwarepark 11, 4232 Hagenberg, Austria},
  type = {Master's Thesis}
}
</pre>