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Pupil detection and gaze tracking using a deformable template

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Abstract

This paper suggests a method for tracking gaze of a person at a distance around 2 m, using a single pan-tilt-zoom (PTZ) camera. In the suggested method, images are acquired for gaze tracking by turning the camera to the wide angle mode, or the narrow angle mode, depending on the location of the person. The face that is present in the field of view (FOV) of a camera, is detected in the wide angle mode. Once the location of the face is calculated, the camera turns to the narrow angle mode. The images, which have been acquired in the narrow angle mode, contain information on the direction of gaze of the person, who is at a distance. The method for calculating the direction of gaze is comprised of the head pose estimation and gaze direction calculation steps. The head pose estimation is performed using the location information on the eyes and nose in the face. The direction of gaze is generated using the process of partitioning the pupil through a deformable template, and extracting the center of an eye using the end points of both eyes and head pose information. This paper shows that the proposed gaze tracking algorithm can effectively track the direction of a person’s gaze, at varying distances.

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References

  1. Alnajar F, Gevers T, Valenti R, Ghebreab S (2017) Auto-calibrated gaze estimation using human gaze patterns. Int J Comput Vis 124(2):223–236

    Article  MathSciNet  Google Scholar 

  2. Arar NM, Gao H, Thrian JP (2017) A regression-based user calibration framework for real-time gaze estimation. IEEE Transactions on Circuites and Systems for Video Technology 27(12):2623–2638

    Article  Google Scholar 

  3. Baker BW, Woods MG (2001) The role of the divine proportion in the esthetic improvement of patients undergoing combined orthodontic/orthognathic surgical treatment. International Journal of Adult Orthodontics and Orthognathic Surgery 16(2):108–120

    Google Scholar 

  4. Chan CN, Oe S, Lin CS (2007) Active eye-tracking system by using quad ptz cameras,” In Proc. of the 33rd Annual Conference of the IEEE Industrial Electronics Society, pp. 2389–2394

  5. Cheng H, Liu Y, Fu W, Ji Y, Yang L, Zhao Y, Yang J (2017) Gazing Point dependent eye gaze estimation. Pattern Recogn 71:36–44

    Article  Google Scholar 

  6. Cho DC, Kim WY (2013) Long-range gaze tracking system for large movements. IEEE Trans Biomed Eng 60(12):3432–3440

    Article  Google Scholar 

  7. Corcoran PM, Nanu F, Petrescu S, Bigioi P (2012) Real-time eye gaze tracking for gaming design and consumer electronics systems. IEEE Trans Consum Electron 58(2):347–355

    Article  Google Scholar 

  8. Dhanachandra N, Manglem K, Chanu YJ (2015) Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Computer Science 54:764–771

    Article  Google Scholar 

  9. Duchowski AT (2002) A breadth-first survey of eye-tracking applications. Behavior Research Methods, Instruments, and Computers 34(4):455–470

    Article  Google Scholar 

  10. Fadda G, Marcialis GL, Roli F, Ghiani L (2013) Exploiting the golden ratio on human faces for head-pose estimation. Lect Notes Comput Sci 8156:280–289

  11. Hallinan PW (1991) Recognizing human eyes. In Proc of the SPIE Geometric Methods in Computer Vision 1570:214–226

    Article  Google Scholar 

  12. Hansen D, Ji Q (2009) In the eye of the beholder: a survey of models for eyes and gaze. IEEE Trans Pattern Anal Mach Intell 32(3):478–500

    Article  Google Scholar 

  13. Hennessey C, Fiset J (2012) Long range eye tracking: bringing eye tracking into the living room. In Proc. of the International Symposium on Eye Tracking Research and Applications:249–252

  14. Horsley M, Eliot M, Knight BA, Reilly R (2014) Current trends in eye tracking research. Springer

  15. Iqbal N, Lee H, Lee SY (2013) Smart user interface for mobile consumer devices using model-based eye-gaze estimation. IEEE Trans Consum Electron 59(1):161–166

    Article  Google Scholar 

  16. Jairath S, Bharadwaj S, Vatsa M (2015) Adaptive skin color model to improve video face detection. Machine Intelligence and Signal Processing:131–142

  17. Ju F, Chen X, Sato Y (2017) Appearance-based gaze estimation via Uncalibrated gaze pattern recovery. IEEE Trans Image Process 26(4):1543–1553

    Article  MathSciNet  Google Scholar 

  18. Kar A, Corcoran P (2017) A review and analysis of eye-gaze estimation systems, algorithms and performance evaluation methods in consumer platforms. IEEE Access 5:16495–16519

    Article  Google Scholar 

  19. Khade BS, Haikwad HM, Aher AS, Patil KK (2016) Face Recognition Techniques: A Survey. International Journal of Computer Science and Information Technology 5(11):65–72

    Google Scholar 

  20. Kublbeck C, Ernst A (2006) Face detection and tracking in video sequences using the odified census transformation. Image Vis Comput 24(6):564–572

    Article  Google Scholar 

  21. Lee HC, Luong DT, Cho CW, Lee EC, Park KR (2010) Gaze tracking system at a distance for controlling IPTV. IEEE Trans Consum Electron 56(4):2577–2583

    Article  Google Scholar 

  22. Magee JJ, Betke M, Gips J, Scott MR, Waber BN (2008) A human-computer interface using symmetry between eyes to detect gaze direction. IEEE Trans Syst Man Cybern Syst Hum 38(6):1248–1261

    Article  Google Scholar 

  23. Ramdane-Cherif Z, Nait-Ali A, Motsch JF, Krebs MO (2004) Performance of computer system for recording and analyzing eye gaze position using an infrared light device. J Clin Monit Comput 18(1):39–44

    Article  Google Scholar 

  24. Reale M, Hung T, Yin L (2010) Pointing with the eyes: gaze estimation using a static/active camera system and 3d iris disk model. IEEE International Conference on Multimedia and Expo:280–285

  25. Tamura K, Choi R, Aoki Y (2018) Unconstrained and calibration-free gaze estimation in a room-scale area using a monocular camera. IEEE Access 6:10896–10908

    Article  Google Scholar 

  26. Tunhua W, Baogang B, Changle Z, Shaozi L, Kunhui L (2010) Real-time non-intrusive eye tracking for human computer interaction. In Proc. of the 5th International Conference on Computer Science and Education, pp. 1092–1096

  27. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In Proc of the IEEE Conference on Computer Vision and Pattern Recognition 1:511–518

    Google Scholar 

  28. Wood E, Bulling A (2004) EyeTab: model-based gaze estimation in unmodified tablet computers. In Proc of the International Symposium on Eye Tracking Research and Applications:207–210

  29. Yan S, Wang H, Fang Z, Wang C A Face Detection Method Combining Improved AdaBoost Algorithm and Template Matching in Video Sequence,” Intelligent Human-Machine Systems and Cybernetics, 8th International Conference on, 2016

  30. Yoo BI, Han JJ, Choi CK, Yi KY, Suh SJ, Park DS, Kim CY (2010) 3D user Interface combining gaze and hand gestures for large-scale display. In Proc of the ACM Conference on Human Factors in Computing Systems:3709–3714

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Acknowledgements

This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2018-0-01419) supervised by the IITP(Institute for Information & communications Technology Promotion).

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Authors and Affiliations

  1. School of Software, Soongsil University, 369, Sangdo-Ro, Dongjak-Gu, Seoul, 156-743, South Korea

    Gyung-Ju Lee & Gye-Young Kim

  2. Department of Software, Anyang University, 708-113, Anyang 5-Dong, Manan-Gu, Anyang, 430-714, South Korea

    Seok-Woo Jang

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  1. Gyung-Ju Lee
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  3. Gye-Young Kim
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Correspondence to Gye-Young Kim.

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Lee, GJ., Jang, SW. & Kim, GY. Pupil detection and gaze tracking using a deformable template. Multimed Tools Appl 79, 12939–12958 (2020). https://doi.org/10.1007/s11042-020-08638-7

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