M.A.Sc. Thesis Presentation Automated Reading Assistance System Using Point-of-Gaze Estimation

1. M.A.Sc. Thesis PresentationAutomated Reading Assistance System Using Point-of-Gaze Estimation Jeffrey J. Kang Supervisor: Dr. Moshe Eizenman Department of Electrical and Computer Engineering Institute of Biomaterials and Biomedical Engineering January 24, 2006

2. Introduction • Reading • Visual examination of text • Convert words to sounds to activate word recognition • We learn appropriate conversions through repetitive exposure to word-to-sound mappings • Insufficient reader skill or irregular spelling can lead to failed conversion: assistance is required Objective: Develop an automated reading assistance system that automatically vocalizes unknown words in real-time on the reader’s behalf. The system should operate within a natural reading setting.

3. What We Need To Do — Step 1 • Identify the word being read, in real-time • Detect when the word being read is an unknown word • Vocalization of the unknown word

4. Identifying the Word Being Read • Identify the viewed word using point-of-gaze estimation • Point-of-gaze is: • Where we are looking with the highest visual acuity region of the retina • Intersection of the visual axis of each eye within the 3D scene • Intersection of the visual axis one eye with a 2D plane

5. Point-of-Gaze Estimation Methodologies 1. Head-mounted 2. Remote (no head-worn components)

6. eye camera scene camera IR LEDs hot mirror Head-mounted Point-of-Gaze Estimation pupil centre corneal reflections • Based on principle of tracking the pupil centre, and corneal reflections to measure eye position • Point-of-gaze is estimated with respect to a coordinate system attached to the head

7. Point-of-Gaze in Head Coordinate System • Point-of-gaze is measured in the head coordinate system, and placed on the scene camera image

8. Locating the Reading Object • The position of the reading object is determined by tracking markers

9. Mapping the Point-of-Gaze • Establish point correspondences from • the estimated positions of the markers in the scene image • the known positions of the markers on the reading object • Homographic mapping of point-of-gaze from scene camera image to reading object coordinate system

10. Identify the Reading Object • Extract the barcode from the scene camera image to identify the reading object (e.g. page number) • Match barcode to database of reading objects to determine what text is being read

11. Identifying the Word Being Read • Using the mapped point-of-gaze, identify the word being read by table lookup

12. Sample Reading Video

13. Sample Reading Video

14. Mapping Accuracy

15. Point-of-Gaze Estimation Methodologies 1. Head-mounted 2. Remote (no head-worn components)

16. Y visual axis P O X C 2D scene object Z Remote Point-of-Gaze Estimation • Point-of-gaze is estimated to a fixed coordinate system • C – centre of corneal curvature • P – point-of-gaze computer screen IR LEDs eye camera

17. true position of 2D scene object Y assumed position of 2D scene object P’ visual axis P O X C Z Moving Reading Card • How can point-of-gaze be estimated to a coordinate system attached to a moving reading object?

18. true position of 2D scene object Y assumed position of 2D scene object P’ visual axis P O X C Z Estimate Motion t1 t0 R, T

19. true position of 2D scene object Y assumed position of 2D scene object P’ visual axis P O X C Z Use a Scene Camera and Targets t1 t0 Scene Camera

20. true position of 2D scene object Y assumed position of 2D scene object P’ visual axis P O X C Z Calculate Two Homographies t1 t0 H0 H1 Scene Camera

21. true position of 2D scene object Y assumed position of 2D scene object P’ visual axis P O X C Z Decompose Homography Matrices t1 t0 R0, T0 R1, T1 Scene Camera

22. true position of 2D scene object Y assumed position of 2D scene object P’ visual axis P O X C Z Calculate Motion of 2D Scene Object t1 t0 R, T R0, T0 R1, T1 Scene Camera

23. Point-of-Gaze Accuracy

24. What We Need To Do: Step 2 • Identify the word being read, in real-time • Detect when the word being read is an unknown word • Vocalization of the unknown word

25. Dual Route Reading Model Coltheart, M. et al. (2001)

26. Dual Route Reading Model • Each word’s graphemes are processed in parallel

27. Dual Route Reading Model Each word’s graphemes are individually converted into phonemes based on mapping rules

28. Detecting Unknown Words • For unknown words, the lexical route fails and the slower non-lexical route is used Hypothesis: we can differentiate between known and unknown words by the duration of the processing time

29. Processing Time

30. Setting a Threshold Curve

31. Setting the Threshold • Threshold curve is a function of word length • Model processing time for known words (length k) as a Gaussian random variable • (μk, σk2) • Estimate μk, σk2from a short training set for each subject • Each point on threshold curve is given by • α is the constrained probability of false alarm

32. Experiment: Detecting Unknown Words • Remote point-of-gaze estimation system • Reading material presented on computer screen • Head position stabilized using a chinrest • Four subjects read from 40 passages of text • 20 passages aloud and 20 passages silently • Divided into training set to “learn” μk, σk2and set detection threshold curves • Set false alarm probability α = 0.10 • Evaluate detection performance

33. Experiment: Detecting Unknown Words

34. Experiment: Natural Setting Reading Assistance • Natural reading pose • Unrestricted head movement • Reading material is hand-held • Head-mounted eye-tracker • Identify viewed word in real-time • Measure per-word processing time • Detecting unknown words • Processing time threshold curves established in previous experiment • Assistance • Detection of unknown word activates vocalization

35. Experiment: Natural Setting Reading Assistance • Results • Point-of-gaze mapping method accommodated head and reading material movement without reducing detection performance

36. Conclusions • Developed methods to map point-of-gaze estimates to an object coordinate system attached to a moving 2D scene object (e.g. reading card) • Head-mounted system • Remote system • Developed method to detect when a reader encounters an unknown word • Demonstrated principle of operation for an automated reading assistance system

37. Future Work • Implement reading assistant using remote-gaze estimation methodology • Validate efficacy of system as a teaching tool for unskilled English readers, in collaboration with an audiologist • Evaluate other forms of assistive intervention • e.g. translation, definition

38. Questions?