Quality Index Based Face Recognition under Varying Illumination Conditions

  • K. T. Dilna
  • T. D. Senthilkumar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 192)


Face recognition is one of the most popular biometric techniques for automatically identifying or verifying a person from a video or digital image. The face recognition accuracy can be affected by intraclass variations and interclass variations. A change in lighting condition is one of the intraclass variations. Preprocessing is an approach to normalize the intraclass variations of light varying image. Histogram equalization (HE) is one of the techniques to normalize the variations in illumination. But it is not suitable for well lighted images. Image quality based adaptive face recognition is used for well lighted face image recognition. The multiresolution property of wavelet transforms is used in face recognition to extract facial feature descriptors. Low and high frequency wavelet subbands are extracted and fusion of match scores from the subband is used to improve the recognition accuracy under varying lighting conditions. For face recognition, 2DPCA (2D Principle Component Analysis) method is used and can be verified with illumination variant face images. 2DPCA is based on 2D image matrices rather than 1D vector so the image matrix does not need to be transformed into a vector prior to feature extraction.


Biometrics Face Recognition Quality Measure Wavelet Transform 2DPCA 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sellahewa, H., Jassim, S.A.: Image-Quality-Based Adaptive Face Recognition. IEEE Transactions on Instrumentation and Measurement 59, 805–813 (2010)CrossRefGoogle Scholar
  2. 2.
    Yang, J., Zhang, D., Frangi, A.F.: Two Dimensional PCA A New Approach to Appearance Based Face representation and recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence 26, 131–137 (2004)CrossRefGoogle Scholar
  3. 3.
    Jain, A.K., Ross, A., Prabhakar, S.: An Introduction to Biometric Recognition. IEEE Trans. On Circuits and Systems for Video Technology 14 (2004)Google Scholar
  4. 4.
    Chien, J.T.: Discriminant wavelet faces and nearest feature classifiers for face recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence 24, 1644–1649 (2002)CrossRefGoogle Scholar
  5. 5.
    Chen, W., Meng, J.E., Wu, S.: Illumination Compensation and Normalization for Robust Face Recognition Using Discrete Cosine Transform in Logarithm Domain. IEEE Transactions on Systems, Man, and Cybernetics 36, 458–466 (2006)CrossRefGoogle Scholar
  6. 6.
    Zhang, T., Tang, Y.Y., Fang, B., Shang, Z.: Face Recognition under Varying Illumination Using Gradientfaces. IEEE Transactions on Image Processing 18, 2599–2606 (2009)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Li, S.Z., Chu, R.F., Liao, S.C., Zhang, L.: Illumination Invariant Face Recognition Using Near-Infrared Images. IEEE Transactions on Pattern Analysis And Machine Intelligence 29, 627–639 (2007)CrossRefGoogle Scholar
  8. 8.
    Vázquez, H.M., Reyesand, E.G., Molleda, Y.C.: A New Image Division for LBP Method to Improve Face Recognition under Varying Lighting conditions. IEEE, Los Alamitos (2008)CrossRefGoogle Scholar
  9. 9.
    Wang, H., Li, S.Z., Wang, Y.S.: Face Recognition under Varying Lighting Conditions Using Self Quotient Image. In: IEEE International Conference on Automatic Face and Gesture Recognition (2004)Google Scholar
  10. 10.
    Chen, T., Yin, W., Zhou, X.S.: Total Variation Models for Variable Lighting Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (2006)Google Scholar
  11. 11.
    Wang, Z., Bovik, A.C.: A Universal Image Quality Index. IEEE Signal Processing Letters 9, 81–84 (2002)CrossRefGoogle Scholar
  12. 12.
    Shashua, A., Riklin-Raviv, T.: The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 129–139 (2001)Google Scholar
  13. 13.
    Shan, S., Gao, W., Cao, B., Zhao, D.: Illumination normalization for robust face recognition against varying lighting conditions. In: Proc. IEEE Int. Workshop Anal. Model. Faces Gestures, pp. 157–164 (2003)Google Scholar
  14. 14.
    Ekenel, H.K., Sankur, B.: Multiresolution face recognition. J. Image and Vision Computing 23, 469–477 (2005)CrossRefGoogle Scholar
  15. 15.
    Sellahewa, H., Jassim, S.A.: Illumination and expression invariant face recognition: Toward sample quality-based adaptive fusion. In: Proc. 2nd IEEE Int. Conf. Biometrics, Theory, Appl. Syst., pp. 1–6 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • K. T. Dilna
    • 1
  • T. D. Senthilkumar
    • 1
  1. 1.Department of Electronics and communicationK.S.R College of TechnologyTiruchengodeIndia

Personalised recommendations