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Eye gaze tracking based directional control interface for interactive applications

  • Amit LaddiEmail author
  • Neelam Rup Prakash
Article
  • 20 Downloads

Abstract

This paper proposes an unobtrusive and calibration-free framework towards eye gaze tracking based interactive directional control interface for desktop environment using simple webcam under unconstrained settings. The proposed eye gaze tracking involved hybrid approach designed by combining two different techniques based upon both supervised and unsupervised methods wherein the unsupervised image gradients method computes the iris centers over the eye regions extracted by the supervised regression based algorithm. Experiments performed by the proposed hybrid approach to detect eye regions along with iris centers over challenging face image datasets exhibited exciting results. Similar approach for eye gaze tracking worked well in real-time by using a simple web camera. Further, PC based interactive directional control interface based upon iris position has been designed that works without needing any prior calibrations unlike other Infrared illumination based eye trackers.

The proposed work may be useful to the people with full body motor disabilities, who need interactive and unobtrusive eye gaze control based applications to live independently.

Keywords

Iris center Supervised Unsupervised Hybrid Unconstrained environment Eye gaze Directional control interface Interactive applications 

Notes

References

  1. 1.
    Burgos-Artizzu XP, Perona P, Dollar P (2013) Robust Face Landmark Estimation under Occlusion. Proceedings of the 2013 IEEE International Conference on Computer Vision, IEEE Computer Society: 1513–1520Google Scholar
  2. 2.
    Chang X, Yang Y (2017) Semisupervised feature analysis by mining correlations among multiple tasks. IEEE Trans Neural Netw Learn Syst 28(10):2294–2305MathSciNetCrossRefGoogle Scholar
  3. 3.
    Chang X et al (2016) Compound rank-<inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>projections for bilinear analysis. IEEE Trans Neural Netw Learn Syst 27(7):1502–1513MathSciNetCrossRefGoogle Scholar
  4. 4.
    Database, B.F., BioID Face Database. https://www.bioid.com/About/BioID-Face-Database
  5. 5.
    Database, T.M.F., The MUCT Face Database , http://www.milbo.org/muct/
  6. 6.
    Feit A et al. (2017) Toward Everyday Gaze Input: Accuracy and Precision of Eye Tracking and Implications for Design: 1118–1130Google Scholar
  7. 7.
    Han Z et al (2014) Precise localization of eye centers with multiple cues. Multimed Tools Appl 68(3):931–945CrossRefGoogle Scholar
  8. 8.
    Hansen DW, Qiang J (2010) In the eye of the beholder: a survey of models for eyes and gaze. Pattern Anal Mach Intell IEEE Trans 32(3):478–500CrossRefGoogle Scholar
  9. 9.
    Jesorsky O, Kirchberg K, Frischholz R (2001) Robust face detection using the hausdorff distance. In: Bigun J, Smeraldi F (eds) Audio- and video-based biometric person authentication. Springer, Berlin Heidelberg, pp 90–95CrossRefGoogle Scholar
  10. 10.
    Labeled Faces in the WildGoogle Scholar
  11. 11.
    Laddi A, Prakash NR (2015) Comparative analysis of unsupervised eye center localization approaches. Signal Processing, Computing and Control (ISPCC), 2015 International Conf. IEEEGoogle Scholar
  12. 12.
    Laddi A, Prakash NR (2017) An augmented image gradients based supervised regression technique for iris center localization. Multimed Tools Appl 76(5):7129–7139CrossRefGoogle Scholar
  13. 13.
    Laddi A, Prakash NR (2017) An accurate and simple approach to detect eye centers in low resolution face images. IETE J Res: 1–6Google Scholar
  14. 14.
    Leo M et al (2013) Unsupervised approach for the accurate localization of the pupils in near-frontal facial images. J Electron Imag 22(3):033033–033033CrossRefGoogle Scholar
  15. 15.
    Leo M et al (2014) Unsupervised eye pupil localization through differential geometry and local self-similarity matching. PLoS One 9(8):e102829CrossRefGoogle Scholar
  16. 16.
    Li B, Fu H (2018) Real time eye detector with cascaded convolutional neural networks. Appl Comput Intell Soft Comput 2018:8Google Scholar
  17. 17.
    Li Z et al (2017) Beyond trace ratio: weighted harmonic mean of trace ratios for multiclass discriminant analysis. IEEE Trans Knowl Data Eng 29(10):2100–2110CrossRefGoogle Scholar
  18. 18.
    Liu L et al. (2018) From BoW to CNN: two decades of texture representation for texture classification. Int J Comput VisGoogle Scholar
  19. 19.
    Lowe DG (2004) Distinctive image features from scale-invariant Keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  20. 20.
    Matthews I, Baker S (2004) Active appearance models revisited. Int J Comput Vis 60(2):135–164CrossRefGoogle Scholar
  21. 21.
    San Agustin, J., et al., (2010) Evaluation of a low-cost open-source gaze tracker. 77–80Google Scholar
  22. 22.
    Sewell W, Komogortsev O (2010) Real-time eye gaze tracking with an unmodified commodity webcam employing a neural network, in CHI '10 Extended Abstracts on Human Factors in Computing Systems. ACM: Atlanta, Georgia, USA: 3739-3744Google Scholar
  23. 23.
    Shaoqing R et al. (2014) Face alignment at 3000 FPS via Regressing Local Binary Features. in Computer Vision and Pattern Recognition (CVPR), 2014 IEEE ConfGoogle Scholar
  24. 24.
  25. 25.
    Timm F, Barth E (2011) Accurate eye centre localisation by means of gradients. Proceedings of the International Conference on Computer Vision Theory and ApplicationsGoogle Scholar
  26. 26.
    Valenti R, Gevers T (2008) Accurate eye center location and tracking using isophote curvature. Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE ConferenceGoogle Scholar
  27. 27.
    Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. in Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society ConferenceGoogle Scholar
  28. 28.
    Xu P et al. (2015) TurkerGaze: Crowdsourcing saliency with webcam based eye tracking. CoRR. abs/1504.06755Google Scholar
  29. 29.
    Xudong C et al. (2012) Face alignment by Explicit Shape Regression. Computer Vision and Pattern Recognition (CVPR), 2012 IEEE ConferenceGoogle Scholar
  30. 30.
    Xuehan X, de la Torre F (2013) Supervised descent method and its applications to face alignment. Computer Vision and Pattern Recognition (CVPR), 2013 IEEE ConferenceGoogle Scholar
  31. 31.
    Ye L et al. (2014) Cascaded Convolutional Neural Network for Eye Detection Under Complex Scenarios. In Biometric Recognition. Cham: Springer International PublishingGoogle Scholar
  32. 32.
    Yuan-Pin L et al. (2005) Webcam Mouse Using Face and Eye Tracking in Various Illumination Environments. 2005 IEEE Engineering in Medicine and Biology 27th Annual ConferenceGoogle Scholar
  33. 33.
    Zafeiriou S, Zhang C, Zhang Z (2015) A survey on face detection in the wild. Comput Vis Image Underst 138(C):1–24CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Biomedical InstrumentationCSIR- Central Scientific Instruments Organisation (CSIO)ChandigarhIndia
  2. 2.Department of Electronics & Communication EngineeringPunjab Engineering College (Deemed to be University)ChandigarhIndia

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