Blind subjects faces database

Blind subjects faces database

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Using your face to unlock a mobile device is not only an appealing security solution, but also a desirable or entertaining feature that is comparable with taking selfies. It is convenient, fast, and does not require much effort. Nevertheless, for users with visual impairments, taking selfies could potentially be a challenging task. In order to study the usability and ensure the inclusion of mobile-based identity authentication technology, the authors have collected the blind-subject face database (BSFDB). Ensuring that technology is accessible to disabled people is important because they account for about 15% of the world population. The BSFDB contains some 40 individuals with visual disabilities who took selfies with a mock-up mobile device. The database comes with four experimental protocols which are defined by a dichotomy of two controlled covariates, namely, whether or not a subject is guided by audio feedback and whether or not he/she has received explicit instructions to take the selfie. The findings suggest the importance of appropriate design of human computer interaction as well as alternative feedback design based on the audio cue. All the data is available online including more than 70, 000 detected face images of blind and partially blind subjects.


    1. 1)
      • 1. Jain, A.K., Flynn, P., Ross:, A.A.: ‘Handbook of biometrics’ (Springer-Verlag, NJ, USA, 2007).
    2. 2)
      • 2. Blanco-Gonzalo, R., Sanchez-Reillo, R.: ‘Biometrics on mobile devices’, in Li, S.Z., Jain, A.K. (Eds.): ‘Encyclopedia of biometrics’ (Springer, US, 2014), pp. 18.
    3. 3)
    4. 4)
    5. 5)
      • 5. Phillips, P.J., Flynn, P.J., Scruggs, T., et al: ‘Overview of the face recognition grand challenge’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, CVPR 2005, 20–25 June 2005, vol. 1, pp. 947954.
    6. 6)
    7. 7)
    8. 8)
    9. 9)
      • 9. Garcia-Salicetti, S., Beumier, C., Chollet, E.A.: ‘BIOMET: a multimodal person authentication database including face, voice, fingerprint, hand and signature modalities’, in Kittler, J., Nixon, M.S. (Eds.): ‘Audio- and video-based biometric person authentication’ (No. 2688 in Lecture Notes in Computer Science), (Springer, Berlin, Heidelberg, 2003), pp. 845853.
    10. 10)
      • 10. Beveridge, J.R., Phillips, P.J., Bolme, D.S., et al: ‘The challenge of face recognition from digital point-and-shoot cameras. 2013 IEEE Sixth Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS), 29 September 2013–2 October 2013, pp. 18.
    11. 11)
      • 11. Berg, T., Berg, A., Edwards, J., et al: ‘Names and faces in the news’. Proc. of the 2004 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, CVPR 2004, June 2004, vol. 2, pp. II848–II–854.
    12. 12)
      • 12. Messer, K., Matas, J., Kittler, J., et al: ‘XM2vtsdb: the extended M2vts database’. Second Int. Conf. on Audio and Video-based Biometric Person Authentication, 1999, pp. 7277.
    13. 13)
      • 13. Bailly-Baillire, E., Bengio, S., Bimbot, F., et al: ‘The BANCA database and evaluation protocol’, in Kittler, J., Nixon, M.S. (Eds.): ‘Audio- and video-based biometric person authentication’, (No. 2688 in Lecture Notes in Computer Science), (Springer, Berlin, Heidelberg, 2003), pp. 625638.
    14. 14)
      • 14. WHO: ‘World report on disability’. Available at
    15. 15)
      • 15. Wong, R., Poh, N., Kittler, J., et al: ‘Interactive quality-driven feedback for biometric systems’. 2010 Fourth IEEE Int. Conf. on Biometrics: Theory Applications and Systems (BTAS), September 2010, pp. 17.
    16. 16)
      • 16. Wong, R., Poh, N., Kittler, J., et al: ‘Towards inclusive design in mobile biometry’. 2010 Third Conf. on Human System Interactions (HSI), May 2010, pp. 267274.
    17. 17)
      • 17. Doddington, G., Liggett, W., Martin, A., et al: ‘Sheep, goats, lambs and wolves a statistical analysis of speaker performance in the NIST 1998 speaker recognition evaluation’. Int. Conf. on Spoken Language Processing, 1998.
    18. 18)
    19. 19)
      • 19. Poh, N., Kittler, J.: ‘A biometric menagerie index for characterising template/model-specific variation’, in Tistarelli, M., Nixon, M.S. (Eds.): ‘Advances in biometrics’ (No. 5558 in Lecture Notes in Computer Science), (Springer, Berlin, Heidelberg, 2009), pp. 816827.
    20. 20)
      • 20. Principal Component Analysis and Factor Analysis. In: ‘Principal Component AnalysisSpringer Series in Statistics (Springer, New York, 2002), pp. 150166.
    21. 21)
      • 21. Štruc, V., Pavešíc, N.: ‘Gabor-Based Kernel Partial-Least-Squares Discrimination Features for Face Recognition’. 2007.
    22. 22)
    23. 23)
      • 23. The Orl database of faces, Available at
    24. 24)
      • 24. Dai, G., Qian, Y.: ‘A Gabor direct fractional-step LDA algorithm for face recognition’. 2004 IEEE Int. Conf. on Multimedia and Expo, 2004. ICME ‘04, June 2004, vol. 1, pp. 6164.
    25. 25)
      • 25. Majumdar, A., Ward, R.K.: ‘Discriminative SIFT features for face recognition’. Canadian Conf. on Electrical and Computer Engineering, 2009. CCECE '09, 3–6 May 2009, pp. 2730.
    26. 26)
      • 26. Teja, G.P., Ravi, S.: ‘Face recognition using subspaces techniques’. 2012 Int. Conf. on Recent Trends In Information Technology (ICRTIT),19–21 April 2012, pp. 103107.
    27. 27)
      • 27. Kamencay, P., Breznan, M., Jelsovka, D., et al: ‘Improved face recognition method based on segmentation algorithm using SIFTPCA’. 35th Int. Conf. on Telecommunications and Signal Processing (TSP), 2012.

Related content

This is a required field
Please enter a valid email address