Eyes wide open? eyelid location and eye aperture estimation for pervasive eye tracking in real-world scenarios

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera

“…Using video recordings, eyelid movement is visible in the images and can be assessed using image processing methods. Different algorithms for that purpose are based on either the motion detection derived from differencing two consecutive images (e.g., Bhaskar, Keat, Ranganath, & Venkatesh, 2003;Chau & Betke, 2005;Fogelton & Benesova, 2016;Jiang, Tien, Huang, Zheng, & Atkins, 2013), a second-order derivative method of image differentiations (Gorodnichy, 2003), a state classification (e.g., Choi, Han, & Kim, 2011;Missimer & Betke, 2010;Pan, Sun, & Wu, 2008;Pan, Sun, Wu, & Lao, 2007), an evaluation of the color contrast or amount of visible color of specific eye regions (Cohn, Xiao, Moriyama, Ambadar, & Kanade, 2003;Danisman, Bilasco, Djeraba, & Ihaddadene, 2010;Lee, Lee, & Park, 2010), the distance between landmarks or arcs representing the upper and lower eyelid (Fuhl et al, 2016;Ito, Mita, Kozuka, Nakano, & Yamamoto, 2002;Miyakawa, Takano, & Nakamura, 2004;Moriyama et al, 2002;Sukno, Pavani, Butakoff, & Frangi, 2009), the missing regions of the open eye like the iris or pupil due to their occlusion by the upper and lower eyelid (Hansen & Pece, 2005;Pedrotti, Lei, Dzaack, & Rötting, 2011), or a combination of the described methods (Sirohey, Rosenfeld, & Duric, 2002). Instead of measuring the real distance between the upper and lower eyelid, most of these algorithms use an indirect measure (motion detection, classification, color contrast, missing eye regions) to conclude whether the eye is closed.…”

“…For the eye blink detection with the head-mounted eyetracker, an image processing algorithm developed by Fuhl et al (2016) was used in combination with an algorithm for the pupil detection (Fuhl, Kübler, Sippel, Rosenstiel, & Kasneci, 2015). The algorithm of Fuhl et al (2016) was chosen because the generated signal represents a direct measurement of the eyelid distance instead of an indirect measure based on motion detection, classification, color contrast or missing eye regions. Further, in contrast to the indirect measures by EOG, this algorithm can be later used in drowsiness detection algorithms to estimate the drowsiness level based on the direct distance of the eyelids.…”

“…Further, in contrast to the indirect measures by EOG, this algorithm can be later used in drowsiness detection algorithms to estimate the drowsiness level based on the direct distance of the eyelids. Since the algorithm developed by Fuhl et al (2016) used data recorded with the same head-mounted eyetracker as in the presented manual and CAD studies, good signal estimations for the eyelid distances in the experiments were expected. Refer to the studies by Fuhl and colleagues (Fuhl et al, 2016;Fuhl et al, 2015) for more details of the image-processing algorithms.…”

“…Since the algorithm developed by Fuhl et al (2016) used data recorded with the same head-mounted eyetracker as in the presented manual and CAD studies, good signal estimations for the eyelid distances in the experiments were expected. Refer to the studies by Fuhl and colleagues (Fuhl et al, 2016;Fuhl et al, 2015) for more details of the image-processing algorithms. The output of the algorithm Fuhl et al (2016) described the palpebral aperture in pixels according to the recorded video.…”

“…Refer to the studies by Fuhl and colleagues (Fuhl et al, 2016;Fuhl et al, 2015) for more details of the image-processing algorithms. The output of the algorithm Fuhl et al (2016) described the palpebral aperture in pixels according to the recorded video. An open eye was estimated with a value of approximately 144 pixels.…”