Skip to main content
SpringerLink
Log in
Book cover

International Conference on Enterprise Information Systems

ICEIS 2015: Enterprise Information Systems pp 515–528Cite as

A Wearable Face Recognition System Built into a Smartwatch and the Blind and Low Vision Users

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 241))

Abstract

Assistive technologies need to be affordable, ergonomic and easy to use. In this work we argue that smartwatches could be assistive devices for the visually impaired, if they have the potential to run complex applications. Hence, in this paper we propose a face recognition system to show that it’s technically possible to develop a real-time computer vision system in a wearable device with limited hardware, since such systems generally require powerful hardware. A case study is presented using the first generation Samsung Galaxy Gear smartwatch. The system runs only on the watch’s hardware and consists in a face detection and recognition software that emits an audio feedback so that visually impaired users know who is around them. The case study includes an evaluation of the proposal with users. Results are shown and discussed validating the technological aspects of the proposal and pointing out room for improving the aspects of interaction.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    http://www.seeingmachines.com/.

  2. 2.

    http://opencv.org/platforms/android.html.

References

  1. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)

    Article  Google Scholar 

  2. Astler, D., Chau, H., Hsu, K., Hua, A., Kannan, A., Lei, L., Nathanson, M., Paryavi, E., Rosen, M., Unno, H., Wang, C., Zaidi, K., Zhang, X., Tang, C.: Increased accessibility to nonverbal communication through facial and expression recognition technologies for blind/visually impaired subjects. In: 13th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 259–260 (2011)

    Google Scholar 

  3. Britto Neto, L.S., Maike, V.R.M.L., Koch, F.L., Baranauskas, M.C.C., Rocha, A.R., Goldenstein, S.K.: A wearable face recognition system built into smartwatch and the visually impaired user. In: 17th International Conference on Enterprise Information Systems, vol. 3, pp. 5–12. Scitepress (2015)

    Google Scholar 

  4. Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)

    Article  Google Scholar 

  5. Chen, X., Flynn, P., Bowyer, K.: PCA-based face recognition in infrared imagery: baseline and comparative studies. In: IEEE International Workshop on Analysis and Modeling of Faces and Gestures, pp. 127–134 (2003)

    Google Scholar 

  6. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)

    Article  MATH  Google Scholar 

  7. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)

    Google Scholar 

  8. Freevoxtouch. the only smart watch in the world for the visually impaired. http://myfreevox.com/en/

  9. Fusco, G., Noceti, N., Odone, F.: Combining retrieval and classification for real-time face recognition. In: 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 276–281 (2012)

    Google Scholar 

  10. Gordon, G.: Face recognition based on depth maps and surface curvature. In: SPIE1570, Geometric methods in Computer Vision, pp. 234–247 (1991)

    Google Scholar 

  11. Hadid, A., Li, S.Z., Pietikäinen, M.: Learning Personal Specific Facial Dynamics for Face Recognition from Videos. In: Zhou, S., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 1–15. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  12. Jain, A. K., Chandrasekaran, B.: Dimensionality and sample size considerations in pattern recognition practice. In Handbook of Statistics, pp. 835–855 (1982)

    Google Scholar 

  13. Kistler, D., Wightman, F.: A model of head-related transfer functions based on principal components analysis and minimum-phase reconstruction. J. Acoust. Soc. Am. 91(3), 1637–1647 (1992)

    Article  Google Scholar 

  14. Kramer, K., Hedin, D., Rolkosky, D.: Smartphone based face recognition tool for the blind. In: 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4538–4541 (2010)

    Google Scholar 

  15. Krishna, S., Little, G., Black, J., Panchanathan, S.: A Wearable face recognition system for individuals with visual impairments. In: 7th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 106–113 (2005)

    Google Scholar 

  16. Li, B., Mian, A., L., W., and Krishna, A.: Using kinect for face recognition under varying poses, expressions, illumination and disguise. In: IEEE Workshop on Applications of Computer Vision, pp. 186–192 (2013)

    Google Scholar 

  17. Luxand, Inc.: Detect and Recognize Faces with Luxand FaceSDK. https://www.luxand.com/facesdk/. Accessed 27 August 2013

  18. Manduchi, R., Coughlan, J.: Computer vision without sight. Commun. ACM 55(1), 96–104 (2012)

    Article  Google Scholar 

  19. Martínez, A.M.: Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 748–762 (2002)

    Article  Google Scholar 

  20. NEUROtechnology.: VeriLook SDK: Face Identification for Stand-Alone or Web Applications. http://www.neurotechnology.com/verilook.html. Accessed on 15 April 2014

  21. Porzi, L., Messelodi, S., Modena, C. M., Ricci, E.: A smart watch-based gesture recognition system for assisting people with visual impairments. In: 3rd ACM International Workshop on Interactive Multimedia on Mobile, pp. 19–24 (2013)

    Google Scholar 

  22. Pun, T., Roth, P., Bologna, G., Moustakas, K., Tzovaras, D.: Image and video processing for visually handicapped people. EURASIP J. Image Video Process. 025214(5), 4:1–4:12 (2007)

    Google Scholar 

  23. Schwartz, W., Guo, H., Choi, J., Davis, L.: Face Identification Using Large Feature Sets. IEEE Trans. Image Process. 21(4), 2245–2255 (2012)

    Article  MathSciNet  Google Scholar 

  24. Tan, X., Chen, S., Zhou, Z., Zhang, F.: Face recognition from a single image per person: a survey. J. Pattern Recogn. 39(9), 1725–1745 (2006). Elsevier, New York

    Article  MATH  Google Scholar 

  25. Tanveer, M., Anam, A., Rahman, A., Ghosh, S., Yeasin, M.: FEPS: A sensory substitution system for the blind to perceive facial expressions. In: 14th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 207–208 (2012)

    Google Scholar 

  26. Tistarelli, M., Grosso, E.: Human face analysis: from identity to emotion and intention recognition. In: Kumar, A., Zhang, D. (eds.) ICEB 2010. LNCS, vol. 6005, pp. 76–88. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  27. Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    Article  Google Scholar 

  28. Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137–154 (2004)

    Article  Google Scholar 

  29. Watanabe, H., Terada, T., Tsukamoto, M.: A sound-based lifelog system using ultrasound. In: 5th Augmented Human International Conference, pp. 59:1–59:2 (2014)

    Google Scholar 

  30. Wilder, J., Phillips, P.J., Jiang, C., Wiener, S.: Comparison of visible and infra-red imagery for face recognition. In: 2nd International Conference on Automatic Face and Gesture Recognition, pp. 182–187 (1996)

    Google Scholar 

  31. Wiscott, L., Fellous, J., Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 19(1), 775–779 (1997)

    Article  Google Scholar 

  32. World Health Organization: Visual impairment and blindness: Fact sheet N. 282 (2013). http://www.who.int/mediacentre/factsheets/fs282/en/

  33. Yan, H., Lu, J., Zhou, X., Shang, Y.: Multi-feature multi-manifold learning for single-sample face recognition. NeuroComputing 143, 134–143 (2014). Elsevier

    Article  Google Scholar 

  34. Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)

    Article  Google Scholar 

  35. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)

    Article  Google Scholar 

Download references

Acknowledgements

The authors wish to express their gratitude to all the volunteers who participated in the experiments in this study, and also for Samsung Research that loaned the hardware equipment. Laurindo de Sousa Britto Neto receives a Ph.D. fellowship from CNPq (grant #141254/2014-9). Vanessa Regina Margareth Lima Maike receives a Ph.D. fellowship from CAPES (grant #01-P-04554/2013). Maria Cecília Calani Baranauskas, Anderson de Rezende Rocha and Siome Klein Goldenstein receive a Productivity Research Fellowship from CNPq (grants #308618/2014-9, #304352/2012-8 and #308882/2013-0, respectively). This work is part of a project that was approved by Unicamp Institutional Review Board CAAE 31818014.0.0000.5404. This paper is an extended version of work published in [3].

Author information

Authors and Affiliations

  1. Department of Computing, Federal University of Piauí (UFPI), Teresina, Brazil

    Laurindo de Sousa Britto Neto

  2. Institute of Computing, University of Campinas (UNICAMP), Campinas, Brazil

    Laurindo de Sousa Britto Neto, Vanessa Regina Margareth Lima Maike, Maria Cecília Calani Baranauskas, Anderson de Rezende Rocha & Siome Klein Goldenstein

  3. Samsung Research Institute, Campinas, Brazil

    Fernando Luiz Koch

Authors
  1. Laurindo de Sousa Britto Neto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vanessa Regina Margareth Lima Maike
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fernando Luiz Koch
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maria Cecília Calani Baranauskas
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anderson de Rezende Rocha
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Siome Klein Goldenstein
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Laurindo de Sousa Britto Neto .

Editor information

Editors and Affiliations

  1. MODESTE/ESEO, Angers, France

    Slimane Hammoudi

  2. Macquarie University, Sydney, Wroclaw University of Economics, Poland, Wroclaw, Poland

    Leszek Maciaszek

  3. Despatx Omega-132, Polytechnic University of Catalonia, Barcelona, Spain

    Ernest Teniente

  4. MODESTE/ESEO, Angers, France

    Olivier Camp

  5. INSTICC, Instituto Politécnico de Setúbal, Setúbal, Portugal

    José Cordeiro

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

de Sousa Britto Neto, L., Maike, V.R.M.L., Koch, F.L., Baranauskas, M.C.C., de Rezende Rocha, A., Goldenstein, S.K. (2015). A Wearable Face Recognition System Built into a Smartwatch and the Blind and Low Vision Users. In: Hammoudi, S., Maciaszek, L., Teniente, E., Camp, O., Cordeiro, J. (eds) Enterprise Information Systems. ICEIS 2015. Lecture Notes in Business Information Processing, vol 241. Springer, Cham. https://doi.org/10.1007/978-3-319-29133-8_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-29133-8_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29132-1

  • Online ISBN: 978-3-319-29133-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation