Cancelable Biometrics Using Noise Embedding

This paper presents a cancelable biometric (CB) scheme for iris recognition system. The CB approaches are roughly classified into two categories depending on whether the method stresses more on non-invertibility or on discriminability. The former is to use non-invertibly transformed data for the recognition instead of the original, so that the impostors cannot retrieve the original biometric information from the stolen data. The latter is to use a salting method that mixes random signals generated by user-specific keys so that the imposters cannot retrieve the original data without the key. The proposed CB can be considered a combination of these methods, which applies a non-invertible transform to the salted data for binary biocode input. We use the reduced random permutation and binary salting (RRP-BS) method as the biometric salting, and use the Hadamard product for enhancing the non-invertibility of salted data. Moreover, we generate several templates for an input, and define non-coherent and coherent matching regions among these templates. We show that salting the non-coherent matching regions is less influential on the overall performance. Specifically, embedding the noise in this region does not affect the performance, while making the data difficult to be inverted to the original.

[1]  Pong C. Yuen,et al.  A Hybrid Approach for Generating Secure and Discriminating Face Template , 2010, IEEE Transactions on Information Forensics and Security.

[2]  Andrew Beng Jin Teoh,et al.  Biohashing: two factor authentication featuring fingerprint data and tokenised random number , 2004, Pattern Recognit..

[3]  Alice J. O'Toole,et al.  FRVT 2006 and ICE 2006 Large-Scale Experimental Results , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Pritee Khanna,et al.  Biometric template protection using cancelable biometrics and visual cryptography techniques , 2016, Multimedia Tools and Applications.

[5]  Rama Chellappa,et al.  Secure and Robust Iris Recognition Using Random Projections and Sparse Representations , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Libor Masek,et al.  Recognition of Human Iris Patterns for Biometric Identification , 2003 .

[7]  Andrew Beng Jin Teoh,et al.  Random Multispace Quantization as an Analytic Mechanism for BioHashing of Biometric and Random Identity Inputs , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Andreas Uhl,et al.  A survey on biometric cryptosystems and cancelable biometrics , 2011, EURASIP J. Inf. Secur..

[9]  Nalini K. Ratha,et al.  Generating Cancelable Fingerprint Templates , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Andreas Uhl,et al.  Cancelable Iris Biometrics Using Block Re-mapping and Image Warping , 2009, ISC.

[11]  Nalini K. Ratha,et al.  Enhancing security and privacy in biometrics-based authentication systems , 2001, IBM Syst. J..

[12]  Rama Chellappa,et al.  Cancelable Biometrics: A review , 2015, IEEE Signal Processing Magazine.

[13]  Christoph Busch,et al.  Alignment-free cancelable iris biometric templates based on adaptive bloom filters , 2013, 2013 International Conference on Biometrics (ICB).

[14]  Rama Chellappa,et al.  Sectored Random Projections for Cancelable Iris Biometrics , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[15]  Nalini K. Ratha,et al.  Cancelable iris biometric , 2008, 2008 19th International Conference on Pattern Recognition.

[16]  Christoph Busch,et al.  Unlinkable and irreversible biometric template protection based on bloom filters , 2016, Inf. Sci..

[17]  Yen-Lung Lai,et al.  Cancellable iris template generation based on Indexing-First-One hashing , 2017, Pattern Recognit..