Selecting Distinctive Features to Improve Performances of Multidimensional Fuzzy Vault Scheme

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• 作者：Hailun Liu (19) (21)
  Dongmei Sun (19) (21)
  Ke Xiong (19) (20) (21)
  Zhengding Qiu (19) (21)
  
• 关键词：Biometric cryptosystems ; Multidimensional fuzzy vault ; Feature selection ; Recognition rate
• 刊名：Lecture Notes in Computer Science
• 出版年：2011
• 出版时间：2011
• 年：2011
• 卷：7098
• 期：1
• 页码：237-243
• 全文大小：246KB
• 参考文献：1. Uludag, U., Pankanti, S., Prabhakar, S., Jain, A.K.: Biometric Cryptosystems: Issues and Challenges. Proc. of the IEEE聽92(6), 948鈥?60 (2004) CrossRef
  2. Jain, A.K., Nandakumar, K., Nagar, A.: Biometric Template Security. EURASIP Journal on Advances in Signal Processing, 1鈥?7 (2008)
  3. Juels, A., Sudan, M.: A fuzzy vault scheme. In: Proc. of IEEE Int. Symp. on Info. Theory, p. 408. IEEE Press, New York (2002)
  4. Nandakumar, K., Jain, A.K., Pankanti, A.: Fingerprint-based fuzzy vault: Implementation and performance. IEEE Trans. Inf. Forensics Secur.聽2(4), 744鈥?57 (2007) CrossRef
  5. Wang, Y.J., Plataniotis, K.N.: Fuzzy vault for face based cryptographic key generation. In: Proc. Biometrics Symposium, pp. 1鈥?. IEEE Press, New York (2007) CrossRef
  6. Lee, Y.J., Park, K.R., Lee, S.J., Bae, K., Kim, J.: A new method for generating an invariant iris private key based on the fuzzy vault system. IEEE Transactions on Systems, Man, and Cybernetics鈥擯art B: Cybernetics聽38(5), 1302鈥?313 (2008) CrossRef
  7. Liu, H.L., Sun, D.M., Xiong, K., Qiu, Z.D.: 3D Fuzzy Vault Based on Palmprint. In: 2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC 2010), pp. 230鈥?34. IEEE Press, New York (2010) CrossRef
  8. Liu, H.L., Sun, D.M., Xiong, K., Qiu, Z.D.: Is Fuzzy Vault Scheme very Effective for Key Binding in Biometric Cryptosystems? In: CyberC 2011: International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (2011)
  9. Dodis, Y., Ostrovsky, R., Reyzin, L., Smith, A.: Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data. SIAM Journal of Computing聽38(1), 97鈥?39 (2008) CrossRef
  10. Li, Q.: Research on Handmetric Recognition and Feature Level Fusion Method, PhD thesis, BeiJing JiaoTong University, Beijing (2006)
  11. Zhang, Y.Q., Qiu, Z.D., Sun, D.M.: Palmprint Identification using Weighted PCA Feature. In: IEEE 9th International Conference on Signal Processing Proceedings, pp. 2113鈥?116. IEEE Press, New York (2008)
  12. Youmaran, R., Adler, A.: Measuring Biometric Sample Quality in terms of Biometric Information. In: Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference (2006)
  
• 作者单位：Hailun Liu (19) (21)
  Dongmei Sun (19) (21)
  Ke Xiong (19) (20) (21)
  Zhengding Qiu (19) (21)
  
  19. Institute of Information Science, Beijing Jiaotong University, Beijing, China
  21. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, China
  20. Department of Electronic Engineering, Tsinghua University, Beijing, China
  

文摘

Fuzzy vault scheme is one of the most popular biometric cryptosystems. However, the scheme is designed for set differences while Euclidean distance is often used in biometric techniques. Multidimensional fuzzy vault scheme (MDFVS) is a modified version that can be easily implemented based on biometric feature data. In MDFVS, every point is a vector, and Euclidean distance measure is used for genuine points filtering. To get better performances, the step of feature selection in the MDFVS algorithms is very important and should be well designed. In this paper we propose applying recognition rate to measure discrimination of features and selecting strong distinctive features into genuine points. Some principles of selecting strong distinctive features to compose genuine points are discussed. An implementation of MDFVS with feature selection is also presented. Experimental results based on palmprint show that the proposed feature selection approach improves the performances of MDFVS.