A Two Fold Expert System for Yawning Detection

One of the prominent indicators of drowsiness is yawning. The main intention for a real-time application such as detecting the driver's yawning is that the response of the detector must be as quick as possible. A novel yawning detection system is proposed which is based on a two agent expert system. The features of the face have to be extracted to detect yawning in the driver's face. In the proposed system, as the first part of detection the authors use the face detection algorithm's skin detection part. The skin region is extracted. For all the skin region blocks detected, their boundaries are defined. Then segmented face is divided into two halves. The lower half of the face is considered for the mouth region extraction. The presence of yawning would be indicated by a black blob in the mouth region of the binary image. But, there may be multiple blobs present in the image which may be due to the presence non-skin like regions around the driver's face. So, identifying the exact position of the mouth and checking for its containment inside the face is necessary. The features extracted for yawning detection are the histogram values taken from the vertical projection of the lower part of the face.

• Record URL:
  https://doi.org/10.1016/j.procs.2016.07.324
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S1877050916315708
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/18770509
• Supplemental Notes:
  • © 2016 C. Anitha et al. Published by Elsevier B.V.
• Authors:
  • Anitha, C
  • Venkatesha, M K
  • Adiga, B Suryanarayana
• Conference:
  • 2nd International Conference on Intelligent Computing, Communication & Convergence (ICCC-2016)
  • Location: Bhubaneswar Odisha, India
  • Date: 2016-1-24 to 2016-1-25
• Publication Date: 2016

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Photos; References;
• Pagination: pp 63-71
• Serial:
  • Procedia Computer Science
  • Volume: 92
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1877-0509
  • Serial URL: http://www.sciencedirect.com/science/journal/18770509
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Detectors; Drivers; Drowsiness; Expert systems; Face
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01610927
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 15 2016 10:31AM