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Biometric bits extraction through phase quantization based on feature level fusion

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Abstract

Biometric bits extraction has emerged as an essential technique for the study of biometric template protection as well as biometric cryptosystems. In this paper, we present a non-invertible but revocable bits extraction technique by means of quantizing the facial data from two feature extractors in the phase domain, which we coin as aligned feature-level fusion phase quantization (AFPQ). In this technique, we utilize helper data to achieve the revocability requirement of bits extraction. The feature averaging and remainder normalization technique are integrated with the helper data to reduce feature variance within the same individual and increase the distinctiveness of bit strings of different individuals to achieve good recognition performance. A scenario in which the system is compromised by an adversary is also considered. As a generic technique, AFPQ can be easily extended to multiple different biometric modalities.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Biometrics Engineering Research Center (BERC), Yonsei University, Seoul, Republic of Korea

    Hyunggu Lee, Andrew Beng Jin Teoh & Jaihie Kim

Authors
  1. Hyunggu Lee
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  2. Andrew Beng Jin Teoh
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  3. Jaihie Kim
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Correspondence to Jaihie Kim.

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Lee, H., Teoh, A.B.J. & Kim, J. Biometric bits extraction through phase quantization based on feature level fusion. Telecommun Syst 47, 255–273 (2011). https://doi.org/10.1007/s11235-010-9317-z

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