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Multimedia Tools and Applications

, Volume 72, Issue 1, pp 637–666 | Cite as

Secure multimodal biometric authentication with wavelet quantization based fingerprint watermarking

  • Bin Ma
  • Yunhong Wang
  • Chunlei Li
  • Zhaoxiang ZhangEmail author
  • Di Huang
Article

Abstract

As malicious attacks greatly threaten the security and reliability of biometric systems, ensuring the authenticity of biometric data is becoming increasingly important. In this paper we propose a watermarking-based two-stage authentication framework to address this problem. During data collection, face features are embedded into a fingerprint image of the same individual as data credibility token and secondary authentication source. At the first stage of authentication, the credibility of input data is established by checking the validness of extracted patterns. Due to the specific characteristics of face watermarks, the face detection based classification strategies are introduced for reliable watermark verification instead of conventional correlation based watermark detection. If authentic, the face patterns can further serve as supplemental identity information to facilitate subsequential biometric authentication. In this framework, one critical issue is to guarantee the robustness and capacity of watermark while preserving the discriminating features of host fingerprints. Hence a wavelet quantization based watermarking approach is proposed to adaptively distribute watermark energy on significant DWT coefficients of fingerprint images. Experimental results which evaluate both watermarking and biometric authentication performance demonstrate the effectiveness of this work.

Keywords

Biometrics Digital watermarking DWT Dither modulation 

Notes

Acknowledgements

This work is funded by the National Basic Research Program of China (No. 2010CB327902), the National Natural Science Foundation of China (No. 60873158, No. 61005016, No. 61061130560) and the Fundamental Research Funds for the Central Universities.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Bin Ma
    • 1
  • Yunhong Wang
    • 1
  • Chunlei Li
    • 2
  • Zhaoxiang Zhang
    • 1
    Email author
  • Di Huang
    • 1
  1. 1.Intelligent Recognition and Image Processing Lab. (IRIP), Beijing Key Laboratory of Digital Media, School of Computer Science and EngineeringBeihang UniversityBeijingChina
  2. 2.School of Electronic and Information EngineeringZhongyuan University of TechnologyZhengzhouChina

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