AN IMPROVED ILLUMINATION NORMALIZATION APPROACH BASED ON WAVELET TRANFORM FOR FACE RECOGNITION FROM SINGLE TRAINING IMAGE PER PERSON

Chun-Nian Fan, Fu-Yan Zhang

Abstract

Recent research on face recognition shows that the illumination change is one of the key issues remaining to be addressed. To recognize faces under varying illuminations with single training image per person conditions, we propose an improved wavelet-based normalization method. We use wavelet transform to decompose an image into its low frequency and high frequency components. Then, we apply histogram equalization to the low frequency coefficients and de-noise the high frequency coefficients adaptively. Lastly, the high frequency coefficients are accentuated by multiplying by a scalar so as to enhance edges. A normalized image is obtained from the modified coefficients by inverse wavelet transform. Among others, the proposed method has the following advantages: (1) it does not need any prior information of 3D shape or light sources, and it aims at addressing illumination issue for face recognition from only one training image per person; (2) due to the multiscale nature of wavelet transform, it has better edge-preserving ability in low frequency illumination fields; and (3) it is computationally feasible and fast. We use PCA method to recognize normalized image with only one training image. The experimental results obtained by testing on the Yale face database B demonstrate the effectiveness of our method with significant improvement in the face recognition system.

References

  1. Adini, Yael, Yael Moses and Shimon Ullman. (1997). Face Recognition: The Problem of Compensating for Changes in Illumination Direction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 721-732.
  2. Belhumeur, P. N. and D. J. Kriegman. (1998). What is the set of images of an object under all possible illumination conditions. International Journal of Computer Vision, 28(3), 245-260.
  3. Donoho, D. L. (1995). De-noising by Soft-thresholding. IEEE Transaction on Information Theory, 41(3), 613-627.
  4. Du, Shan and Rabab Ward. (2005). Wavelet-based illumination normalization for face recognition. In IEEE International Conference on Image Processing(ICIP 2005).
  5. Feng, G. C., P. C. Yuen and D.Q. Dai (2000). Human face recognition using PCA on wavelet subband. Journal of Electronic Imaging, 2(9), 226-233.
  6. Georghiades, Athinodoros S. and Peter N. Belhumeur. (2001). From Few to Many Illumination Cone Models for Face Recognition Under Variable Lighting and Pose. IEEE Transactions On Pattern Analysis And Machine Intelligence, 23(6), 643-660.
  7. Lee, K.-C.J., Ho, and D. J. Kriegman. (2005). Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions On Pattern Analysis And Machine Intelligence, 27(5), 684-698.
  8. Li Bicheng,Luo Jianshu. (2003). Wavelet Analysis and Its Application. Beijing : Publishing House of Electronics Industry.
  9. Phillips P. J., et al. (2007) FRVT 2006 and ICE 2006 Large-Scale Results [Online]. Available: http://www.frvt.org/FRVT2006/
  10. Phillips P. J., Wechsler H., Huang J.and Rauss P. J. (1998). The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing, 16(5), 295-306.
  11. Savvides , M. and V. Kumar. (2003). Illumination normalization using logarithm transforms for face authentication. In Proc. IAPR AVBPA, 549-556.
  12. Shan, Shiguang, Wen Gao, Bo Cao and Debin Zhao. (2003). Illumination normalization for robust face recognition against varying lighting conditions. In Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG'03).
  13. Tan, X., Chen, S., Zhou, Z.-H. & Zhang, F. (2006) Face recognition from a single image per person: A survey. Pattern Recognition, 39, 1725-1745.
  14. Wei Wang, Jiatao Song, Zhongxiu Yang, Zheru Chi. (2006). Wavelet-based Illumination Compensation for Face Recognition using Eigenface Method. In Proceedings of the 6th World Congress on Intelligent Control and Automation, June 21 - 23, Dalian, China.
  15. Zhang, Taiping, Bin Fang, Yuan Yuan, Yuan Yan Tang, Zhaowei Shang, Donghui Li and Fangnian Lang. (2009). Multiscale facial structure representation for face recognition under varying illumination. Pattern Recognition 42, 251-258.
  16. Zhao, WenYi, R. CHELLAPPA, P. J. PHILLIPS and A. ROSENFELD. (2003). Face Recognition: A Literature Survey. ACM Computing Surveys, 35(4), 399-458.
Download


Paper Citation


in Harvard Style

Fan C. and Zhang F. (2010). AN IMPROVED ILLUMINATION NORMALIZATION APPROACH BASED ON WAVELET TRANFORM FOR FACE RECOGNITION FROM SINGLE TRAINING IMAGE PER PERSON . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 473-476. DOI: 10.5220/0002785304730476


in Bibtex Style

@conference{visapp10,
author={Chun-Nian Fan and Fu-Yan Zhang},
title={AN IMPROVED ILLUMINATION NORMALIZATION APPROACH BASED ON WAVELET TRANFORM FOR FACE RECOGNITION FROM SINGLE TRAINING IMAGE PER PERSON},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={473-476},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002785304730476},
isbn={978-989-674-029-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
TI - AN IMPROVED ILLUMINATION NORMALIZATION APPROACH BASED ON WAVELET TRANFORM FOR FACE RECOGNITION FROM SINGLE TRAINING IMAGE PER PERSON
SN - 978-989-674-029-0
AU - Fan C.
AU - Zhang F.
PY - 2010
SP - 473
EP - 476
DO - 10.5220/0002785304730476