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An Illumination Independent Face Verification Based on Gabor Wavelet and Supported Vector Machine

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 15))

Abstract

Face verification technology is widely used in the fields of public safety, e-commerce and so on. Due its characteristic of insensitive to the varied illumination, a new method about face verification with illumination invariant is presented in this paper based on gabor wavelet. First, ATICR method is used to do light preprocessing on images. Second, certain gabor wavelet filters, which are selected on the experiment inducing different gagor wavelet filter has not the same effect in verification, are used to extract feature of the image, of which the dimension in succession is reduced by Principal Component Analysis. At last, SVM classifiers are modeled on the data with reduced dimension. The experiment results in IFACE database and NIRFACE database indicate the algorithm named “Selected Paralleled Gabor Method” can achieves higher verification performance and better adaptability to the variable illumination.

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References

  1. Yang, F., Shan, S., Ma, B., Chen, X., Gao, W.: Using Score Normalization to Solve the Score Variation Problem in Face Authentication. In: Li, S.Z., Sun, Z., Tan, T., Pankanti, S., Chollet, G., Zhang, D. (eds.) IWBRS 2005. LNCS, vol. 3781, pp. 31–38. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  2. Serrano, A., Diego, I., Conde, C., Cabello, E., Bai, L., Shen, L.: Fusion of Support Vector Classifiers for Parallel Gabor Methods Applied to Face Verification. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 141–150. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  3. Han, J., Bhanu, B.: Statistical Feature Fusion for Gait-based Human Recognition. CVPR (2004)

    Google Scholar 

  4. Kamel, M., Wanas, N.: Data Dependence in Combining Classifiers. In: Windeatt, T., Roli, F. (eds.) MCS 2003. LNCS, vol. 2709, pp. 1–14. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  5. Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Neuroscience 3, 72–86 (1991)

    Google Scholar 

  6. Zhu, J., Liu, B., Schwartz, S.: General Illumination Correction and its Application to Face Normalization. In: Proceeding of AMFG (2003)

    Google Scholar 

  7. Ko, J., Kim, E., Byun, H.: A Simple Illumination Algorithm for Face Recognition. In: Ishizuka, M., Sattar, A. (eds.) PRICAI 2002. LNCS (LNAI), vol. 2417. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  8. Venkatargmani, K., Qidwai, S., Vijayakumar, B.: Face Authentication form Cell Phone Camera Images with Illumination and Temporal Variations. IEEE trans. on SMC. 411–418 (2005)

    Google Scholar 

  9. Zhang, X., Li, H.: A Face Verification Based on Negative Independent Sample Set and SVM. J. Comput. Res. Dev. 2138–2143 (2006)

    Google Scholar 

  10. Li, S., Chu, R., Liao, S., Zhang, L.: Illumination Invariant Face Recognition Using Near-Infrared Images. IEEE Trans. Pattern Anal. Mach. Intel. 29(4) (2007)

    Google Scholar 

  11. Daugman, J.G.: Complete Discrete 2-D Gabor Transforms by Neural Networks for Image Analysis and Compression. IEEE Trans, Acoustics, Speech, Signal Processing 36(7), 1169–1179 (1988)

    Article  MATH  Google Scholar 

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De-Shuang Huang Donald C. Wunsch II Daniel S. Levine Kang-Hyun Jo

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© 2008 Springer-Verlag Berlin Heidelberg

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Zhang, X., Liu, D., Chen, J. (2008). An Illumination Independent Face Verification Based on Gabor Wavelet and Supported Vector Machine. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2008. Communications in Computer and Information Science, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85930-7_21

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  • DOI: https://doi.org/10.1007/978-3-540-85930-7_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85929-1

  • Online ISBN: 978-3-540-85930-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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