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A probabilistic approach to online eye gaze tracking without explicit personal calibration

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Final Year IEEE Projects for BE, B.Tech, ME, M.Tech,M.Sc, MCA & Diploma Students latest Java, .Net, Matlab, NS2, Android, Embedded,Mechanical, Robtics, VLSI, Power Electronics, IEEE projects are given absolutely complete working product and document providing with real time Software & Embedded training......

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A probabilistic approach to online eye gaze tracking without explicit personal calibration

  1. 1. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com A PROBABILISTIC APPROACH TO ONLINE EYE GAZE TRACKING WITHOUT EXPLICIT PERSONAL CALIBRATION By A PROJECT REPORT Submitted to the Department of electronics &communication Engineering in the FACULTY OF ENGINEERING & TECHNOLOGY In partial fulfillment of the requirements for the award of the degree Of MASTER OF TECHNOLOGY IN ELECTRONICS &COMMUNICATION ENGINEERING APRIL 2016
  2. 2. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com CERTIFICATE Certified that this project report titled “A Probabilistic Approach to Online Eye Gaze Tracking Without Explicit Personal Calibration” is the bonafide work of Mr. _____________Who carried out the research under my supervision Certified further, that to the best of my knowledge the work reported herein does not form part of any other project report or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate. Signature of the Guide Signature of the H.O.D Name Name
  3. 3. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com DECLARATION I hereby declare that the project work entitled “A Probabilistic Approach to Online Eye Gaze Tracking Without Explicit Personal Calibration” Submitted to BHARATHIDASAN UNIVERSITY in partial fulfillment of the requirement for the award of the Degree of MASTER OF APPLIED ELECTRONICS is a record of original work done by me the guidance of Prof.A.Vinayagam M.Sc., M.Phil., M.E., to the best of my knowledge, the work reported here is not a part of any other thesis or work on the basis of which a degree or award was conferred on an earlier occasion to me or any other candidate. (Student Name) (Reg.No) Place: Date:
  4. 4. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com ACKNOWLEDGEMENT I am extremely glad to present my project “A Probabilistic Approach to Online Eye Gaze Tracking Without Explicit Personal Calibration” which is a part of my curriculum of third semester Master of Science in Computer science. I take this opportunity to express my sincere gratitude to those who helped me in bringing out this project work. I would like to express my Director,Dr. K. ANANDAN, M.A.(Eco.), M.Ed., M.Phil.,(Edn.), PGDCA., CGT., M.A.(Psy.)of who had given me an opportunity to undertake this project. I am highly indebted to Co-OrdinatorProf. Muniappan Department of Physics and thank from my deep heart for her valuable comments I received through my project. I wish to express my deep sense of gratitude to my guide Prof. A.Vinayagam M.Sc., M.Phil., M.E., for her immense help and encouragement for successful completion of this project. I also express my sincere thanks to the all the staff members of Computer science for their kind advice. And last, but not the least, I express my deep gratitude to my parents and friends for their encouragement and support throughout the project.
  5. 5. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com ABSTRACT: Existing eye gaze tracking systems typically require an explicit personal calibration process in order to estimate certain person-specific eye parameters. For natural human computer interaction, such a personal calibration is often inconvenient and unnatural. In this paper, we propose a new probabilistic eye gaze tracking system without explicit personal calibration. Unlike the conventional eye gaze tracking methods, which estimate the eye parameter deterministically using known gaze points, our approach estimates the probability distributions of the eye parameter and eye gaze. Using an incremental learning framework, the subject does not need personal calibration before using the system. His/her eye parameter estimation and gaze estimation can be improved gradually when he/she is naturally interacting with the system. The experimental result shows that the proposed system can achieve <3° accuracy for different people without explicit personal calibration.
  6. 6. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com INTRODUCTION: Gaze tracking is the procedure of determining the pointof-gaze on the monitor, or the visual axis of the eye in 3D space. Gaze tracking systems are primarily used in the Human Computer Interaction (HCI) and in the analysis of visual scanning patterns. In HCI, the eye gaze can serve as an advanced computer input to replace traditional input devices such as a mouse pointer Also, the graphic display on the screen can be controlled by the eye gaze interactively. Since visual scanning patterns are closely related to the attentional focus, cognitive scientists use the gaze tracking system to study human’s cognitive processes. Various video-based gaze estimation techniques ,have been proposed (a survey of gaze estimation may be found) and the gaze estimation systems have now evolved to the point where the user is allowed free head movements while maintaining high accuracy (one degree or better). However, most of current gaze estimation systems require a personal calibration procedure for each subject in order to estimate his/her specific eye parameters.This calibration process could significantly limit the practical utility of gaze estimation. To overcome this limitation, we propose a novel gaze estimation framework without any explicit personal calibration. The main contributions of this work include: • the introduction of a probabilistic approach (versus the existing deterministic approach) for 3D eye gaze estimation without explicit personal calibration, • development of an incremental learning approach to incrementally refine the eye parameters when the subject is naturally viewing the screen without prompting, • development of a gaze estimation algorithm using Gaussian prior probability when the subject is naturally watching a video. Our system adapts automatically online to each subject to improve eye gaze tracking accuracy without user collaboration,making natural, non-intrusive and non-collaborative eye gaze tracking closer to reality. Since the video-based eye tracking is being widely used, including many commercial eye trackers. By eliminating the explicit personal calibration for these eye trackers, the proposed research hence has significant practical impact.
  7. 7. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com CONCLUSION: In this paper, we proposed a new probabilistic gaze estimation framework by combining the gaze prior with the 3D eye gaze model. Compared to the conventional gaze estimation method, our proposed approach eliminates the explicit personal calibration procedure. It changes conventional deterministic eye gaze tracking to probabilistic eye gaze tracking, allowing to combine eye gaze prior with optical axis estimates to simultaneously estimate 3D gaze point and the personal eye parameters in an incremental manner without any cooperation from the user.Compared with the most recent implicit personal calibration method , our system allows natural head movement, and achieves high gaze estimation accuracy of less than three degree (comparing to the accuracy of 3.5 degrees in, By using a novel incremental learning framework, our system doesn’t need any training data fromin the subject beforehand. It can adapt to the user quickly and improves its performance as the subject naturally uses the system. Finally, we further extend our system without computing the saliency map by assuming the prior gaze distribution follows a Gaussian distribution, with a mean located in the center of the screen. This not only improves the speed of our method (without the need of computing saliency map), but also extend its application scope. Our approach, however, is limited to free-viewing scenarios when subjects are naturally viewing images or videos. Studies in visual attention ,have already shown that if the user is performing a specific visual task, his/her gaze distribution is driven by the task. The proposed gaze prior, either saliency map or Gaussian gaze prior, may not be applicable in this case. We will study the task-dependent gaze prior in our future work.
  8. 8. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com REFERENCES: [1] Y. Sugano, Y. Matsushita, and Y. Sato, “Calibration-free gaze sensing using saliency maps,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2010, pp. 2667–2674. [2] Y. Sugano, Y. Matsushita, and Y. Sato, “Appearance-based gaze estimation using visual saliency,” IEEE Trans. Pattern Anal. Mach. Intell.,vol. 35, no. 2, pp. 329–341, Feb. 2013. [3] D. W. Hansen and Q. Ji, “In the eye of the beholder: A survey of models for eyes and gaze,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 3, pp. 478–500, Mar. 2010. [4] F. Lu, T. Okabe, Y. Sugano, and Y. Sato, “A head pose-free approach for appearance-based gaze estimation,” in Proc. Brit. Mach. Vis. Conf.,2011, pp. 126.1–126.11. [5] J. Chen and Q. Ji, “Probabilistic gaze estimation without active personal calibration,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2011, pp. 609–616. [6] Y. Sugano, Y. Matsushita, Y. Sato, and H. Koike, “An incremental learning method for unconstrained gaze estimation,” in Proc. Eur. Conf. Comput. Vis. (ECCV), 2007, pp. 656–667. [7] A. Borji and L. Itti, “Defending Yarbus: Eye movements reveal observers’ task,” J. Vis., vol. 14, no. 3, p. 29, Mar. 2014. [8] T. Judd, F. Durand, and A. Torralba, “A benchmark of computational models of saliency to predict human fixations,” Massachusetts Inst.Technol., Cambridge, MA, USA, Tech. Rep. MIT- CSAIL-TR-2012-001,2012. [9] T. Judd, K. Ehinger, F. Durand, and A. Torralba, “Learning to predict where humans look,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Sep./Oct. 2009, pp. 2106–2113.

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