Face Image Quality Evaluation for ISO/IEC Standards 19794-5 and 29794-5

  • Jitao Sang
  • Zhen Lei
  • Stan Z. Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

Face recognition performance can be significantly influenced by face image quality. The approved ISO/IEC standard 19794-5 has specified recommendations for face photo taking for E-passport and related applications. Standardization of face image quality, ISO/IEC 29794-5, is in progress. Bad illumination, facial pose and out-of-focus are among main reasons that disqualify a face image sample. This paper presents several algorithms for face image quality assessment. Illumination conditions and facial pose are evaluated in terms of facial symmetry, and implemented based on Gabor wavelet features. Assessment of camera focus is done based on discrete cosine transform (DCT). These methods are validated by experiments.

Keywords

Face image quality international standard facial symmetry out-of-focus 

References

  1. 1.
    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)Google Scholar
  2. 2.
    ISO/IEC JTC 1/SC 37 N 1477. Biometric Sample Quality - Part 5: Face Image Data Sample Quality (Working Draft for comment) (February 12, 2007)Google Scholar
  3. 3.
    ISO/IEC JTC 1/SC 37 N 506. Biometric Data Interchange Formats Part 5: Face Image Data (March 22, 2004)Google Scholar
  4. 4.
    Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition 11(4), 467–476 (2002)Google Scholar
  5. 5.
    Ratha, N.K., Chen, S.Y., Jain, A.K.: Adaptive Flow Orientation-based Feature Extraction in Fingerprint Images. Pattern Recognition 28(11), 1657–1672 (1995)Google Scholar
  6. 6.
    Engeldrum, P.G.: Psychometric Scaling: A Toolkit for Imaging Systems Development. Imcotek Press (2000)Google Scholar
  7. 7.
    Shaked, D., Tastl, I.: Sharpness measure: towards automatic image enhancement 1(1), 937–940 (2005)Google Scholar
  8. 8.
    Wee, C., Paramesran, R.: Measure of image sharpness using eigenvalues. Inf. Sci. 177(12) (2007)Google Scholar
  9. 9.
    Zhao, W., Chellappa, R., Phillips, P., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM Computing Surveys 35(4), 399–458 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jitao Sang
    • 1
  • Zhen Lei
    • 1
  • Stan Z. Li
    • 1
  1. 1.Center for Biometrics and Security Research, Institute of AutomationChinese Academy of SciencesBeijingChina

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