A Wearable Face Recognition System Built into a Smartwatch and the Visually Impaired User

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

2015

Abstract

Practitioners usually expect that real-time computer vision systems such as face recognition systems will require hardware components with high processing power. In this paper, we present a concept to show that it is technically possible to develop a simple real-time face recognition system in a wearable device with low processing power – in this case an assistive device for the visually impaired. Our platform of choice here is the first generation Samsung Galaxy Gear smartwatch. Running solely in the watch, without pairing to a phone or tablet, the system detects a face in the image captured by the camera, and then performs face recognition (on a limited dictionary), emitting an audio feedback that either identifies the recognized person or indicates that s/he is unknown. For the face recognition approach we use a variation of the K-NN algorithm which accomplished the task with high accuracy rates. This paper presents the proposed system and preliminary results on its evaluation.

References

  1. Ahonen, T., Hadid, A., and Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12):2037- 2041.
  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., and Tang, C. (2011). Increased accessibility to nonverbal communication through facial and expression recognition technologies for blind/visually impaired subjects. In The Proceedings of the 13th International ACM SIGACCESS Conference on Computers and Accessibility, pages 259-260.
  3. Belhumeur, P., Hespanha, J., and Kriegman, D. (1997). Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):711-720.
  4. Chen, X., Flynn, P., and Bowyer, K. (2003). PCA-based face recognition in infrared imagery: baseline and comparative studies. In Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures, pages 127-134.
  5. Cover, T., and Hart, P.: Nearest neighbor pattern classification. (1967). IEEE Transactions on Information Theory, 13(1):21-27.
  6. Dalal, N. and Triggs, B. (2005). Histograms of Oriented Gradients for Human Detection. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 886-893.
  7. FreevoxTouch (2014). The only smart watch in the world for the visually impaired. Available: http://myfreevox.com/en/.
  8. Fusco, G., Noceti, N., and Odone, F. (2012). Combining retrieval and classification for real-time face recognition. In 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, pages 276-281.
  9. Gordon, G. (1991). Face recognition based on depth maps and surface curvature. In SPIE1570, Geometric methods in Computer Vision, pages 234-247.
  10. Hadid, A., Pietikainen, M., and Li, S. (2007). Learning personal specific facial dynamics for face recognition from videos. In Analysis and Modeling of Faces and Gestures: Lecture Notes in Computer Science 4778, pages 1-15.
  11. Kistler, D. and Wightman, F. (1992). A model of headrelated transfer functions based on principal components analysis and minimum-phase reconstruction. Journal of the Acoustical Society of America, 91(3):1637-1647.
  12. Kramer, K., Hedin, D., and Rolkosky, D. (2010). Smartphone based face recognition tool for the blind. In 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 4538-4541.
  13. Krishna, S., Little, G., Black, J., and Panchanathan, S. (2005). A wearable face recognition system for individuals with visual impairments. In The Proceedings of the 7th International ACM SIGACCESS Conference on Computers and Accessibility, pages 106-113.
  14. Li, B., Mian, A., L., W., and Krishna, A. (2013). Using kinect for face recognition under varying poses, expressions, illumination and disguise. In IEEE Workshop on Applications of Computer Vision, pages 186-192.
  15. Luxand, Inc. (2013). Detect and Recognize Faces with Luxand FaceSDK. Updated on August 27, 2013. Available: https://www.luxand.com/facesdk/ Manduchi, R. and Coughlan, J. (2012). (Computer) vision without sight. In Communications of the ACM, 55(1):96-104.
  16. NEUROtechnology. (2014). VeriLook SDK: face identification for stand-alone or web applications. Updated on April 15, 2014. Available: http://www. neurotechnology.com/verilook.html.
  17. Organization, W. H. (2013). Visual impairment and blindness: Fact sheet n.282. Available: http://www.who.int/mediacentre/factsheets/fs282/en/ Porzi, L., Messelodi, S., Modena, C. M., and Ricci, E. (2013). A smart watch-based gesture recognition system for assisting people with visual impairments. In Proceedings of the 3rd ACM International Workshop on Interactive Multimedia on Mobile, pages 19-24.
  18. Pun, T., Roth, P., Bologna, G., Moustakas, K., and Tzovaras, D. (2007). Image and video processing for visually handicapped people. EURASIP Journal on Image and Video Processing, 2007:025214(5):4:1- 4:12.
  19. Schwartz, W., Guo, H., Choi, J., and Davis, L. (2012). Face identification using large feature sets. IEEE Transactions on Image Processing, 21(4):2245-2255.
  20. Tanveer, M., Anam, A., Rahman, A., Ghosh, S., and Yeasin, M. (2012). Feps: A sensory substitution system for the blind to perceive facial expressions. In Proceedings of the 14th International ACM SIGACCESS Conference on Computers and Accessibility, pages 207-208.
  21. Tistarelli, M. and Grosso, E. (2010). Human face analysis: From identity to emotion and intention recognition. In Ethics and Policy of Biometrics: Lectures Notes in Computer Science 6005, pages 76-88.
  22. Turk, M. and Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71-86.
  23. Viola, P. and Jones, M. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2):137-154.
  24. Watanabe, H., Terada, T., and Tsukamoto, M. (2014). A sound-based lifelog system using ultrasound. In Proceedings of the 5th Augmented Human International Conference, 59:1-59:2.
  25. Wilder, J., Phillips, P. J., Jiang, C. and Wiener, S. (1996). Comparison of visible and infra-red imagery for face recognition. In Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition, pages 182-187.
  26. Wiscott, L., Fellous, J., and Malsburg, C. (1997). Face recognition by elastic buncg graph matching. In IEEE Transactions on pattern analysis and machine intelligence, pages 775-779.
  27. Zhao, G. and Pietikainen, M. (2007). Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6):915-928.
  28. Zhao, W., Chellappa, R., Phillips, P. J., and Rosenfeld, A. (2003). Face recognition: A literature survey. Acm Computing Surveys, 35(4):399-458.
Download


Paper Citation


in Harvard Style

Britto Neto L., Maike V., Koch F., Baranauskas M., Rocha A. and Goldenstein S. (2015). A Wearable Face Recognition System Built into a Smartwatch and the Visually Impaired User . In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 3: ICEIS, ISBN 978-989-758-098-7, pages 5-12. DOI: 10.5220/0005370200050012


in Bibtex Style

@conference{iceis15,
author={Laurindo de Sousa Britto Neto and Vanessa Regina Margareth Lima Maike and Fernando Luiz Koch and Maria Cecília Calani Baranauskas and Anderson de Rezende Rocha and Siome Klein Goldenstein},
title={A Wearable Face Recognition System Built into a Smartwatch and the Visually Impaired User},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 3: ICEIS,},
year={2015},
pages={5-12},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005370200050012},
isbn={978-989-758-098-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 3: ICEIS,
TI - A Wearable Face Recognition System Built into a Smartwatch and the Visually Impaired User
SN - 978-989-758-098-7
AU - Britto Neto L.
AU - Maike V.
AU - Koch F.
AU - Baranauskas M.
AU - Rocha A.
AU - Goldenstein S.
PY - 2015
SP - 5
EP - 12
DO - 10.5220/0005370200050012