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

  • Laurindo de Sousa Britto NetoEmail author
  • Vanessa Regina Margareth Lima Maike
  • Fernando Luiz Koch
  • Maria Cecília Calani Baranauskas
  • Anderson de Rezende Rocha
  • Siome Klein Goldenstein
Conference paper
Part of the Lecture Notes in Business Information Processing book series (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.

Keywords

Human-Computer interaction Assistive technology Computer vision Accessibility Wearable device Wearable system 

Notes

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].

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Laurindo de Sousa Britto Neto
    • 1
    • 2
    Email author
  • Vanessa Regina Margareth Lima Maike
    • 2
  • Fernando Luiz Koch
    • 3
  • Maria Cecília Calani Baranauskas
    • 2
  • Anderson de Rezende Rocha
    • 2
  • Siome Klein Goldenstein
    • 2
  1. 1.Department of ComputingFederal University of Piauí (UFPI)TeresinaBrazil
  2. 2.Institute of ComputingUniversity of Campinas (UNICAMP)CampinasBrazil
  3. 3.Samsung Research InstituteCampinasBrazil

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