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Detection of
Driver Drowsiness from Video |
Vural, Cetin, Ercil,
Movellan, Bartlett
Automatic facial expression recognition has advanced to the point
that we can develop applications that respond to spontaneous expressions in
real time. This work explores the real-time measurement of drowsiness. Drowsiness
detection has crucial implications for safety in situations involving heavy
machinery or control towers, as well as application in fields such as adaptive
tutoring systems. The US National Highway Traffic Safety Administration (NHTSA)
has concluded that drowsy driving is just as dangerous as drunk driving. Thus
methods to automatically detect drowsiness may help save many lives. Other drowsiness detection systems
focus on blink rate, yawning, and head nods. Here, we apply automated
measurement of the face during actual drowsiness to discover new signals of
drowsiness in facial expression and head motion.
Vural, E., Bartlett, M.S., Littlewort,
G., Cetin, M. Ercil, E., and Movellan,
J. (2010). Discrimination of Moderate and Acute Drowsiness Based on Spontaneous
Facial Expressions. IEEE International Conference on Pattern
Recognition. Download pdf
Vural, E., Cetin, M., Ercil, A., Littlewort, G.,
Bartlett, M., and Movellan, J. (2007). Drowsy driver detection through facial movement analysis.
ICCV Workshop on Human Computer Interaction.
Download pdf
Vural, E., Cetin, M., Ercil, A., Littlewort, G.,
Bartlett, M., and Movellan, J. (2007). Machine learning systems for detecting driver drowsiness.
Proc. Digital Signal Processing for in-Vehicle and Mobile Systems, Istanbul,
Turkey. p. 97-110. Best paper award.