Driver drowsiness detection using multi-channel second order blind identifications

Chao Zhang, Xiaopei Wu*, Xi Zheng, Shui Yu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)
242 Downloads (Pure)


It is well known that blink, yawn, and heart rate changes give clue about a human's mental state, such as drowsiness and fatigue. In this paper, image sequences, as the raw data, are captured from smart phones which serve as non-contact optical sensors. Video streams containing subject's facial region are analyzed to identify the physiological sources that are mixed in each image. We then propose a method to extract blood volume pulse and eye blink and yawn signals as multiple independent sources simultaneously by multi-channel second-order blind identification (SOBI) without any other sophisticated processing, such as eye and mouth localizations. An overall decision is made by analyzing the separated source signals in parallel to determine the driver's driving state. The robustness of the proposed method is tested under various illumination contexts and a variety of head motion modes. Experiments on 15 subjects show that the multi-channel SOBI presents a promising framework to accurately detect drowsiness by merging multi-physiological information in a less complex way.

Original languageEnglish
Pages (from-to)11829-11843
Number of pages15
JournalIEEE Access
Publication statusPublished - 2019

Bibliographical note

Copyright the Publisher 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • Yawn
  • blink
  • blood volume pulse (BVP)
  • drowsiness detection
  • second-order blind identification (SOBI)


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