Advertisement

Face Quality Measure for Face Authentication

  • Quynh Chi TruongEmail author
  • Tran Khanh Dang
  • Trung Ha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10018)

Abstract

In a face authentication system, face image quality can significantly influence system performance. Designing an effective image quality measure is necessary to reduce the number of poor quality face images acquired during enrollment and authentication, thereby improving system performance. Furthermore, image quality scores can be used as weights in multimodal system based on weighted score level fusion. In this paper, the authors examined image quality factors, such as contrast, brightness, focus and illumination, and defined quality measure for these factors. The quality measure used template image’s, or registration image’s, quality as reference quality. Thus, the quality measure does not rely on any reference good quality and criteria to evaluate how good a face image is. The quality measure reflects difference in quality between a template image and a query image. Then, we proposed a face quality measure by combining these factors. Finally, we conducted experiments to evaluate the relationship between face authentication performance and individual image quality factors as well as the combined face quality measure.

Keywords

Face quality index Face quality measure Face authentication Quality metrics 

References

  1. 1.
    Jain, A., Ross, A., Nandakumar, K.: Introduction to Biometrics. Springer, New York (2011)CrossRefGoogle Scholar
  2. 2.
    Zuo, J., Schmid, N.: Adaptive quality-based performance prediction and boosting for iris authentication: methodology and its illustration. IEEE Trans. Inf. Forensics Sec. 2013(8), 1051–1060 (2013)CrossRefGoogle Scholar
  3. 3.
    Merkle, J., Schwaiger, M., Breitenstein, M.: Towards improving the NIST fingerprint image quality (NFIQ) algorithm. In: International Conference on Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany (2010)Google Scholar
  4. 4.
    Hsu, R.L.V., Shah, J., Martin, B.: Quality assessment of facial images. In: Biometric Consortium Conference (BCC), Baltimore, MD, USA (2006)Google Scholar
  5. 5.
    Bhattacharjee, D., Prakash, S., Gupta, P.: No-reference image quality assessment for facial images. In: Huang, D.-S., Gan, Y., Gupta, P., Gromiha, M.M. (eds.) ICIC 2011. LNCS (LNAI), vol. 6839, pp. 594–601. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-25944-9_77 Google Scholar
  6. 6.
    Wong, Y., Chen, S., Mau, S., Sanderson, C., Lovell, B.: Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Colorado Springs, CO, USA, 2011, pp. 74–81 Won+11, KrD06, WaB02, AdD06, VSN08, Gao+07, Yao+08 (2011)Google Scholar
  7. 7.
    Kryszczuk, K., Drygajlo, A.: On combining evidence for reliability estimation in face verification. In: European Signal Processing Conference (EUSIPCO), Florence, Italy (2006)Google Scholar
  8. 8.
    Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Sig. Process. Lett. 2002(9), 81–84 (2002)CrossRefGoogle Scholar
  9. 9.
    Adler, A., Dembinsky, T.: Human vs. automatic measurement of biometric sample quality. In: IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Ottawa, Canada (2006)Google Scholar
  10. 10.
    Vatsa, M., Singh, R., Noore, A.: SVM-based adaptive biometric image enhancement using quality assessment. In: Prasad, B., Prasanna, S. (eds.) Speech, Audio, Image and Biomedical Signal Processing using Neural Networks. SCI, vol. 83, pp. 351–367. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Gao, X., Li, S.Z., Liu, R., Zhang, P.: Standardization of face image sample quality. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 242–251. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Yao, Y., Abidi, B.R., Kalka, N.D., Schmid, N.A., Abidi, M.A.: Improving long range and high magnification face recognition: database acquisition, evaluation, and enhancement. Comput. Vis. Image Underst. 2008(111), 111–125 (2008)CrossRefGoogle Scholar
  13. 13.
    Wyszecki, G., Stiles, W.S.: Color science, Concepts and Methods, Quantitative Data and Formulae. Wiley, New York (2000)Google Scholar
  14. 14.
    Bezryadin, S., Bourov, P., Ilinih, D.: Brightness calculation in digital image processing. In: International Symposium on Technologies for Digital Fulfillment, Las Vegas, NV, USA (2007)Google Scholar
  15. 15.
    Michelson, A.: Studies in Optics. University of Chicago Press, Chicago (1927)zbMATHGoogle Scholar
  16. 16.
    Bex, P.J., Makous, W.: Spatial frequency, phase, and the contrast of natural images. J. Opt. Soc. Am. A 19(6), 1096–1106 (2002)CrossRefGoogle Scholar
  17. 17.
    Peli, E.: Contrast in complex images. J. Opt. Soc. Am. A 7(10), 2032–2040 (1990)CrossRefGoogle Scholar
  18. 18.
    Yap, P.-T., Raveendran, P.: Image focus measure based on Chebyshev moments. IEEE Proc. Vis. Image Sig. Process. 151(2), 128–136 (2004)CrossRefGoogle Scholar
  19. 19.
    Pertuz, S., Puig, D., Garcia, M.A.: Analysis of focus measure operators for shape-from-focus. Pattern Recogn. 46(5), 1415–1432 (2013)CrossRefzbMATHGoogle Scholar
  20. 20.
    Abaza, A., Harrison, M.A., Bourlai, T., Ross, A.: Design and evaluation of photometric image quality measures for effective face recognition. IET Biometrics 3(4), 314–324 (2014)CrossRefGoogle Scholar
  21. 21.
    Abdel-Mottaleb, M., Mahoor, M.: Application notes algorithms for assessing the quality of facial images. IEEE Comput. Intell. Mag. 2, 10–17 (2007)CrossRefGoogle Scholar
  22. 22.
    Grother, P., Tabassi, E.: Performance of biometric quality measures. IEEE Trans. Pattern Anal. Mach. Intell. 29, 531–543 (2007)CrossRefGoogle Scholar
  23. 23.
    Kryszczuk, K., Richiardi, J., Drygajlo, A.: Impact of combining quality measures on biometric sample matching. In: IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), Washington, DC, USA (2009)Google Scholar
  24. 24.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3, 71–86 (1991)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Ho Chi Minh City University of TechnologyHo Chi Minh CityVietnam
  2. 2.Vietnam National University Ho Chi Minh City, University of Information TechnologyHo Chi Minh CityVietnam

Personalised recommendations