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Facial Expression Recognition for Motor Impaired Users

  • Krishna SehgalEmail author
  • Sanchit Goel
  • Rachna Jain
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 799)

Abstract

In today’s world touch screen devices are trending as people are dependent on their smartphones and tablets for much of their work, making it simple and convenient to store and access data anytime and anywhere. In such a bustling framework, some people are not able to access touch screen devices. These users are diseased by motor impairment because of which they find it difficult or nearly impossible to access touch screen devices resulting in a digital divide. This research work revolves around a technology that can be used to aid problems faced by motor impaired users. It provides an alternative solution by using an algorithm that detects emotions and performs action on touch screen devices. Facial expression recognition can support access to touch screen devices with minimal physical interaction. In this proposed work facial expressions of a user are detected.

Keywords

Assistive technology Emotion analysis Facial expression Motor impairment Touch screen 

References

  1. 1.
    Li, Y.: Gesture search: a tool for fast mobile data access. In: Proceedings of UIST, pp. 87–96. ACM (2010)Google Scholar
  2. 2.
    Lü, H., Li, Y.: Gesture avatar: a technique for operating mobile user interfaces using gestures. In: Proceedings of CHI, pp. 207–216. ACM (2011)Google Scholar
  3. 3.
    Poppinga, B., Sahami Shirazi, A., Henze, N., Heuten, W., Boll, S.: Understanding shortcut gestures on mobile touch devicesGoogle Scholar
  4. 4.
    Kong, L.Y.: Gesture recognition on smartphone devicesGoogle Scholar
  5. 5.
    Bartlett, M.S., Littlewort, G., Fasel, I., Chenu, J., Kanda, T., Ishiguro, H., Movellan J.R.: Towards social robots: automatic evaluation of human-robot interaction by face detection and expression classificationGoogle Scholar
  6. 6.
    Sobecki, J., Szymański, J.M., Cichoń, K.: Gesture tracking and recognition in touchscreens usability testingGoogle Scholar
  7. 7.
  8. 8.
    Zhong, Y., Weber, A., Burkhardt, C., Weaver, P., Bigham, J.P.: Enhancing android accessibility for users with hand tremor by reducing fine pointing and steady tapping. In: Proceedings of 12th Web for All Conference, p. 29. ACM (2015)Google Scholar
  9. 9.
    Anthony, L., Brown, Q., Nias, J., Tate, B.: Examining the need for visual feedback during gesture interaction on mobile touchscreen devices for kids. In: Proceedings of 12th International Conference on Interaction Design and Children, IDC 2013, pp. 157–164. ACM (2013)Google Scholar
  10. 10.
    Samal, A., Iyengar, P.A.: Automatic recognition and analysis of human faces and facial expressions: a survey. Pattern Recogn. 25(1), 65–77 (1992)CrossRefGoogle Scholar
  11. 11.
    Yang, M.H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 24(1), 34–58 (2002)CrossRefGoogle Scholar
  12. 12.
    Bourbakis, N.: A face detection and facial expression recognition methodGoogle Scholar
  13. 13.
    Duff, S.N., Irwin, C.B. Skye, J.L., Sesto, M.E., Wiegmann, D. A.: The effect of disability and approach on touch screen performance during a number entry task. In: Proceedings of Human Factors and Ergonomics Society Annual Meeting (2010)Google Scholar
  14. 14.
    Adolphs, R.: Neural systems for recognizing emotion. Curr. Opin. Neurobiol. 12(2), 169–177 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceBharati Vidyapeeth’s College of EngineeringDelhiIndia

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