Signal, Image and Video Processing

, Volume 9, Supplement 1, pp 99–109 | Cite as

A novel template protection scheme for multibiometrics based on fuzzy commitment and chaotic system

  • Ning Wang
  • Qiong Li
  • Ahmed A. Abd El-Latif
  • Jialiang Peng
  • Xuehu Yan
  • Xiamu Niu
Original Paper


In recent years, biometrics template protection has been extensively studied and lots of schemes have been proposed. However, most of them have not considered the forgery, large difference of intra-class and the security of unimodal biometrics leakage. And there is no multibiometrics template scheme based on the fusion of dual iris, thermal and visible face images. In this paper, a novel multibiometrics template protection scheme based on fuzzy commitment and chaotic system, and the security analysis approach for unimodal biometrics leakage are proposed. Firstly, the thermal face images are captured to overcome the forgery. Then, the fuzzy commitment is generated from the corporation of error correcting code (ECC) and the fusion binary features. Additionally, the dual iris feature vectors are encrypted via the chaotic system, and the score level fusion based on Aczél-Alsina triangular-norm (AA T-norm) is implemented to acquire the final verification performance. Finally, the entropy of both mutlibiometrics and unimodal information leakage is analyzed to show the security of the proposed approach. The experimental tests are conducted on a virtual multibiometrics database, which merges the challenging CASIA-Iris-Thousand and the NVIE face database. The verification performance decreases from EER of \(3 \times 10^{-2}\) to \(1.163 \times 10^{-1}\) %, but the multibiometrics template security is enhanced from 80.53 to 167.80 bits based on BCH ECC (1,023, 123, 170).


Template protection Multibiometrics Fuzzy commitment Chaotic system Entropy analysis 

List of symbols


Error correcting code

AA T-norm

Aczél-Alsina triangular-norm


Fuzzy commitment


Fuzzy vault;


One-way coupled map lattice


Natural visible and infrared facial expression


Equal error rate


False matching rate


False non-matching rate


Detection error tradeoff


Genuine matching rate





This work is supported by the National Natural Science Foundation of China (Grant No.: 61100187), the Fundamental Research Funds for the Central Universities (Grant No.: HIT. NSRIF. 2013061.) and Ministry of Scientific Research (Egypt-Tunisia Cooperation Program, Code No: 4-13-A1).


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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Ning Wang
    • 1
  • Qiong Li
    • 1
    • 2
  • Ahmed A. Abd El-Latif
    • 1
    • 3
  • Jialiang Peng
    • 1
    • 4
  • Xuehu Yan
    • 1
  • Xiamu Niu
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.Science Zone of Harbin Institute of TechnologyHarbinChina
  3. 3.Mathematics Department, Faculty of ScienceMenoufia UniversityMenufiaEgypt
  4. 4.Information and Network Administration CenterHeilongjiang UniversityHarbinChina

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