Active shape models—their training and application
Computer Vision and Image Understanding
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A visual approach for driver inattention detection
Pattern Recognition
Active Shape Models with Invariant Optimal Features: Application to Facial Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Haar-like features with optimally weighted rectangles for rapid object detection
Pattern Recognition
Determining driver visual attention with one camera
IEEE Transactions on Intelligent Transportation Systems
Detecting driver drowsiness using feature-level fusion and user-specific classification
Expert Systems with Applications: An International Journal
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Several studies have related the alertness of an individual to their eye-blinking patterns. Accurate and automatic quantification of eye-blinks can be of much use in monitoring people at jobs that require high degree of alertness, such as that of a driver of a vehicle. This paper presents a non-intrusive system based on facial biometrics techniques, to accurately detect and quantify eye-blinks. Given a video sequence from a standard camera, the proposed procedure can output blink frequencies and durations, as well as the PERCLOS metric, which is the percentage of the time the eyes are at least 80% closed. The proposed algorithm was tested on 360 videos of the AV@CAR database, which amount to approximately 95,000 frames of 20 different people. Validation of the results against manual annotations yielded very high accuracy in the estimation of blink frequency with encouraging results in the estimation of PERCLOS (average error of 0.39%) and blink duration (average error within 2 frames).