2014 International Conference on Informatics, Electronics &Amp; Vision (ICIEV) 2014
DOI: 10.1109/iciev.2014.6850713
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Robust driver fatigue recognition using image processing

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Cited by 25 publications
(22 citation statements)
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“…Face Detection Face detection is the first stage. There are numerous challenges that are belongs to the face detection such as position, orientation, lighting conditions, variability of size, shape and color [1]. Generally, camera is fixed at the front of the driver so it is easy for detection of face and head tilt determination.…”
Section: Proposed Systemmentioning
confidence: 99%
See 1 more Smart Citation
“…Face Detection Face detection is the first stage. There are numerous challenges that are belongs to the face detection such as position, orientation, lighting conditions, variability of size, shape and color [1]. Generally, camera is fixed at the front of the driver so it is easy for detection of face and head tilt determination.…”
Section: Proposed Systemmentioning
confidence: 99%
“…Another one method is driver performance analysis which is used for monitoring the vehicles on road and lane detection. Non-intrusive methods are helpful to detect facial expressions for fatigue recognition Eye closure and mouth gaping are popular symptoms in the driver fatigue recognition [1]. This is real time system which will consider all fatigue symptoms collectively such as eye closure, head tilting (where head is moving to the left or right) yawning, lanedetection.…”
Section: Introductionmentioning
confidence: 99%
“…To track the gaze direction of the driver, they traced the center of the pupil using CDF analysis and estimated the frequency of eye-movement. Rafi Ahmed et al [4] described a modern approach which detects driver's fatigue symptoms, eye closure, yawning, and head tilting are included. M. Omidyeganeh et al [5] presented a robust and intelligent scheme for driver drowsiness detection employing the fusion of eye closure and yawning detection methods.…”
Section: A Fatigue Behaviormentioning
confidence: 99%
“…The driver fatigue is determined by the frequency of eye blinking and head tilting and the drowsiness level is calculated by Raspberry Pi3 along with a Pi camera [8]. The experiment is carried out on ten volunteers under different lighting conditions and they obtain face and eye detection rate up to 99.59% [7]. Real-time video-based vision processing method is recommended to estimate fatigue of the driver and a buzzer is used to alert the driver [11].…”
Section: Introductionmentioning
confidence: 99%