Video-oculography (VOG) is a tool providing diagnostic information about the progress of the diseases that cause regression of the vergence eye movements, such as Parkinson's disease (PD). The majority of the existing systems are based on sophisticated infra-red (IR) devices. In this study, the authors show that a webcam-based VOG system can provide similar accuracy to that of a head-mounted IR-based VOG system. They also prove that the authors’ iris localisation algorithm outperforms current state-of-the-art methods on the popular BioID dataset in terms of accuracy. The proposed system consists of a set of image processing algorithms: face detection, facial features localisation and iris localisation. They have performed examinations on patients suffering from PD using their system and a JAZZ-novo head-mounted device with IR sensor as reference. In the experiments, they have obtained a mean correlation of 0.841 between the results from their method and those from the JAZZ-novo. They have shown that the accuracy of their visual system is similar to the accuracy of IR head-mounted devices. In the future, they plan to extend their experiments to inexpensive high frame rate cameras which can potentially provide more diagnostic parameters.

References

1. 1)
  - 14. George, A., Routray, A.: ‘Fast and accurate algorithm for eye localization for gaze tracking in low resolution images’, IET Comput. Vis., 2016, abs/1605.05272.
2. 2)
  - 4. Sakatani, T., Isa, T.: ‘Pc-based high-speed video-oculography for measuring rapid eye movements in mice’, Neurosci. Res., 2004, 49, (1), pp. 123–131.
3. 3)
  - 7. Migliaccio, A.A., MacDougall, H.G., Minor, L.B., et al: ‘Inexpensive system for real-time 3-dimensional video-oculography using a fluorescent marker array’, J. Neurosci. Methods, 2005, 143, (2), pp. 141–150.
4. 4)
  - 5. Sesma-Sanchez, L., Villanueva, A., Cabeza, R.: ‘Gaze estimation interpolation methods based on binocular data’, IEEE Trans. Biomed. Eng., 2012, 59, (8), pp. 2235–2243.
5. 5)
  - 3. Schneider, E., Dera, T., Bard, K., et al: ‘Eye movement driven head-mounted camera: it looks where the eyes look’. IEEE Int. Conf. on Systems, Man and Cybernetics, 2005, 2005, vol. 3, pp. 2437–2442.
6. 6)
  - 11. Oommen, B.S., Stahl, J.S.: ‘Amplitudes of head movements during putative eye-only saccades’, Brain Res., 2005, 1065, (12), pp. 68–78.
7. 7)
  - 20. Naruniec, J., Kowalski, M., Daniluk, M.: ‘3d face data acquisition and modelling based on an rgbd camera matrix’. IEEE Eighth Int. Conf. on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2015, 2015, vol. 1, pp. 157–160.
8. 8)
  - 12. Zhou, Z., Yao, P., Zhuang, Z., et al: ‘A robust algorithm for iris localization based on radial symmetry and circular integro differential operator’. 2011 Sixth IEEE Conf. on Industrial Electronics and Applications, 2011, pp. 1742–1745.
9. 9)
  - 19. Xiao, J., Moriyama, T., Kanade, T., et al: ‘Robust full-motion recovery of head by dynamic templates and re-registration techniques’, Int. J. Imaging Syst. Technol., 2003, 13, pp. 85–94.
10. 10)
  - 10. Wierts, R., Janssen, M., Kingma, H.: ‘Measuring saccade peak velocity using a low-frequency sampling rate of 50 Hz’, IEEE Trans. Biomed. Eng., 2008, 55, (12), pp. 2840–2842.
11. 11)
  - 9. Yagi, T., Koizumi, Y., Aoyagi, M., et al: ‘Three-dimensional analysis of eye movements using four times high-speed video camera’, Auris Nasus Larynx, 2005, 32, (2), pp. 107–112.
12. 12)
  - 13. Markus, N., Frljak, M., Pandzic, I.S., et al: ‘Eye pupil localization with an ensemble of randomized trees’, Pattern Recognit., 2014, 47, pp. 578–587.
13. 13)
  - 6. van der Geest, J., Frens, M.: ‘Recording eye movements with video-oculography and sclera search coils: a direct comparison of two methods’, J. Neurosci. Methods, 2002, 114, (2), pp. 185–195.
14. 14)
  - 15. Timm, F., Barth, E.: ‘Accurate eye centre localisation by means of gradients’, in Mestetskiy, L., Braz, J. (Eds.): ‘VISAPP’ (SciTePress, 2011), pp. 125–130.
15. 15)
  - 8. Haslwanter, T., Clarke, A.H.: ‘Chapter 5 – eye movement measurement: electro-oculography and video-oculography’, in Zee, D.S., Eggers, S.D. (Eds.): ‘Vertigo and Imbalance: Clinical Neurophysiology of the Vestibular System, volume 9 of Handbook of Clinical Neurophysiology’ (Elsevier, 2010), pp. 61–79.
16. 16)
  - 1. Zhang, Y.-F., Gao, X.-Y., Zhu, J.-Y., et al: ‘A novel approach to driving fatigue detection using forehead eog’. Seventh Int. IEEE/EMBS Conf. on Neural Engineering (NER), 2015, 2015, pp. 707–710.
17. 17)
  - 16. Valenti, R., Gevers, T.: ‘Accurate eye center location through invariant isocentric patterns’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (9), pp. 1785–1798.
18. 18)
  - 18. Dalal, N., Triggs, B.: ‘Histograms of oriented gradients for human detection’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2005. CVPR 2005, 2005, vol. 1, pp. 886–893.
19. 19)
  - 2. Inoue, T., Kato, Y., Ozawa, J.: ‘Evaluating visual fatigue by sensing eye movement during viewing of 3d images’. IEEE First Global Conf. on Consumer Electronics (GCCE), 2012, 2012, pp. 486–490.
20. 20)
  - 22. Jesorsky, O., Kirchberg, K.J., Frischholz, R.: ‘Robust face detection using the Hausdorff distance’. Proc. of the Third Int. Conf. on Audio- and Video-Based Biometric Person Authentication, AVBPA ‘01, London, UK, 2001, pp. 90–95, Available at: http://dl.acm.org/citation.cfm?id=646073.677460.
21. 21)
  - 17. Lienhart, R., Maydt, J.: ‘An extended set of Haar-like features for rapid object detection’. Proc. 2002 Int. Conf. on Image Processing, 2002, vol. 1, pp. I–900–I–903.
22. 22)
  - 21. Ren, S., Cao, X., Wei, Y., et al: ‘Face alignment at 3000 fps via regressing local binary features’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2014, 2014, pp. 1685–1692.

Webcam-based system for video-oculography

References

Related content