Automated video-based measurement of eye closure using a remote camera for detecting drowsiness and behavioural microsleeps

• Main Author: Malla, Amol Man
• Language: en
• Published: University of Canterbury. Electrical and Computer Engineering 2009
• Subjects: Video-based Lapse drowsiness and microsleep detection; Eyelid detection; Face detection; Eye detection; Remote camera and infrared illumination; Integral projection; Template matching; Anthropometric; Haar-object detection.
• Online Access: http://hdl.handle.net/10092/2111

A device capable of continuously monitoring an individual’s levels of alertness in real-time is highly desirable for preventing drowsiness and lapse related accidents. This thesis presents the development of a non-intrusive and light-insensitive video-based system that uses computer-vision methods to localize face, eyes, and eyelids positions to measure level of eye closure within an image, which, in turn, can be used to identify visible facial signs associated with drowsiness and behavioural microsleeps. The system was developed to be non-intrusive and light-insensitive to make it practical and end-user compliant. To non-intrusively monitor the subject without constraining their movement, the video was collected by placing a camera, a near-infrared (NIR) illumination source, and an NIR-pass optical filter at an eye-to-camera distance of 60 cm from the subject. The NIR-illumination source and filter make the system insensitive to lighting conditions, allowing it to operate in both ambient light and complete darkness without visually distracting the subject. To determine the image characteristics and to quantitatively evaluate the developed methods, reference videos of nine subjects were recorded under four different lighting conditions with the subjects exhibiting several levels of eye closure, head orientations, and eye gaze. For each subject, a set of 66 frontal face reference images was selected and manually annotated with multiple face and eye features. The eye-closure measurement system was developed using a top-down passive feature-detection approach, in which the face region of interest (fROI), eye regions of interests (eROIs), eyes, and eyelid positions were sequentially localized. The fROI was localized using an existing Haar-object detection algorithm. In addition, a Kalman filter was used to stabilize and track the fROI in the video. The left and the right eROIs were localized by scaling the fROI with corresponding proportional anthropometric constants. The position of an eye within each eROI was detected by applying a template-matching method in which a pre-formed eye-template image was cross-correlated with the sub-images derived from the eROI. Once the eye position was determined, the positions of the upper and lower eyelids were detected using a vertical integral-projection of the eROI. The detected positions of the eyelids were then used to measure eye closure. The detection of fROI and eROI was very reliable for frontal-face images, which was considered sufficient for an alertness monitoring system as subjects are most likely facing straight ahead when they are drowsy or about to have microsleep. Estimation of the y- coordinates of the eye, upper eyelid, and lower eyelid positions showed average median errors of 1.7, 1.4, and 2.1 pixels and average 90th percentile (worst-case) errors of 3.2, 2.7, and 6.9 pixels, respectively (1 pixel 1.3 mm in reference images). The average height of a fully open eye in the reference database was 14.2 pixels. The average median and 90th percentile errors of the eye and eyelid detection methods were reasonably low except for the 90th percentile error of the lower eyelid detection method. Poor estimation of the lower eyelid was the primary limitation for accurate eye-closure measurement. The median error of fractional eye-closure (EC) estimation (i.e., the ratio of closed portions of an eye to average height when the eye is fully open) was 0.15, which was sufficient to distinguish between the eyes being fully open, half closed, or fully closed. However, compounding errors in the facial-feature detection methods resulted in a 90th percentile EC estimation error of 0.42, which was too high to reliably determine extent of eye-closure. The eye-closure measurement system was relatively robust to variation in facial-features except for spectacles, for which reflections can saturate much of the eye-image. Therefore, in its current state, the eye-closure measurement system requires further development before it could be used with confidence for monitoring drowsiness and detecting microsleeps.