Computer Vision for the Blind: A Comparison of Face Detectors in a Relevant Scenario

  • Marco De Marco
  • Gianfranco Fenu
  • Eric Medvet
  • Felice Andrea PellegrinoEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 195)


Motivated by the aim of developing a vision-based system to assist the social interaction of blind persons, the performance of some face detectors are evaluated. The detectors are applied to manually annotated video sequences acquired by blind persons with a glass-mounted camera and a necklace-mounted one. The sequences are relevant to the specific application and demonstrate to be challenging for all the considered detectors. A further analysis is performed to reveal how the performance is affected by some features such as occlusion, rotations, size and position of the face within the frame.


Face detection Video sequences Blindness Comparison 



This work has been supported by the University of Trieste - Finanziamento di Ateneo per progetti di ricerca scientifica - FRA 2014, and by a private donation in memory of Angelo Soranzo (1939–2012).


  1. 1.
    Online: Be my eyes.
  2. 2.
    Jin, Y., Kim, J., Kim, B., Mallipeddi, R., Lee, M.: Smart cane: face recognition system for blind. In: Proceedings of 3rd International Conference on Human-Agent Interaction, HAI 2015, pp. 145–148. ACM, New York (2015)Google Scholar
  3. 3.
    Chaudhry, S., Chandra, R.: Design of a mobile face recognition system for visually impaired persons. CoRR abs/1502.00756 (2015)Google Scholar
  4. 4.
    Carrato, S., Fenu, G., Medvet, E., Mumolo, E., Pellegrino, F.A., Ramponi, G.: Towards more natural social interactions of visually impaired persons. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2015. LNCS, vol. 9386, pp. 729–740. Springer, Cham (2015). doi: 10.1007/978-3-319-25903-1_63 CrossRefGoogle Scholar
  5. 5.
    Zafeiriou, S., Zhang, C., Zhang, Z.: A survey on face detection in the wild: past, present and future. Comput. Vis. Image Underst. 138, 1–24 (2015)CrossRefGoogle Scholar
  6. 6.
    Hsu, G.S., Chu, T.Y.: A framework for making face detection benchmark databases. IEEE Trans. Circuits Syst. Video Technol. 24(2), 230–241 (2014)CrossRefGoogle Scholar
  7. 7.
    Carrato, S., Marsi, S., Medvet, E., Pellegrino, F.A., Ramponi, G., Vittori, M.: Computer vision for the blind: a dataset for experiments on face detection and recognition. In: Proceedings of 39th International Convention on Information and Communication Technology, Electronics and Microelectronics, pp. 1479–1484. Mipro Croatian Society, Opatija (2016)Google Scholar
  8. 8.
    Fazzi, E., Lanners, J., Danova, S., Ferrarri-Ginevra, O., Gheza, C., Luparia, A., Balottin, U., Lanzi, G.: Stereotyped behaviours in blind children. Brain Dev. 21(8), 522–528 (1999)CrossRefGoogle Scholar
  9. 9.
    Bonetto, M., Carrato, S., Fenu, G., Medvet, E., Mumolo, E., Pellegrino, F.A., Ramponi, G.: Image processing issues in a social assistive system for the blind. In: 2015 9th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 216–221. IEEE (2015)Google Scholar
  10. 10.
    Frazor, R.A., Geisler, W.S.: Local luminance and contrast in natural images. Vis. Res. 46(10), 1585–1598 (2006)CrossRefGoogle Scholar
  11. 11.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  12. 12.
    Liao, S., Jain, A.K., Li, S.Z.: A fast and accurate unconstrained face detector. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 211–223 (2016)CrossRefGoogle Scholar
  13. 13.
    Markuš, N., Frljak, M., Pandžić, I.S., Ahlberg, J., Forchheimer, R.: Object detection with pixel intensity comparisons organized in decision trees (2013). arXiv preprint arXiv:1305.4537
  14. 14.
    Dundar, A., Jin, J., Martini, B., Culurciello, E.: Embedded streaming deep neural networks accelerator with applications. IEEE Trans. Neural Netw. Learn. Syst. (2016, to appear)Google Scholar
  15. 15.
    Kuhn, H.W.: The Hungarian method for the assignment problem. Nav. Res. Logist. Q. 2(1–2), 83–97 (1955)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Fenu, G., Jain, N., Medvet, E., Pellegrino, F.A., Pilutti Namer, M.: On the assessment of segmentation methods for images of mosaics. In: Proceedings of 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015), pp. 130–137 (2015)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Marco De Marco
    • 1
  • Gianfranco Fenu
    • 1
  • Eric Medvet
    • 1
  • Felice Andrea Pellegrino
    • 1
    Email author
  1. 1.Department of Engineering and ArchitectureUniversity of TriesteTriesteItaly

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