Cancelable Iris Recognition with DPL

The security of users may be one of the challenges that need to be addressed in the practical applications of iris recognition. In order to prevent the theft of iris patterns, non-invertible transformations are desired to be adopted. Combining random projections (RP) with dictionary pair learning (DPL) mode, a cancelable iris recognition scheme is proposed in this paper. Using the framework of DPL, dictionary pair of synthesis dictionary and analysis dictionary can be learnt jointly for iris pattern classification, which greatly reduce the computation complexity without sacrificing match accuracy. And cancelable iris templates are created with random projections. It is very difficult to extract useful information from the transformed iris patterns, which can enhance the related security. The suggested algorithm of cancelable iris recognition with DPL is also robust with the sectored random projections. Experiment results on public datasets have shown that a robust and accurate cancelable iris recognition can be obtained.

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

[2]  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.

[3]  W BowyerKevin,et al.  Presentation Attack Detection for Iris Recognition , 2018 .

[4]  R. DeVore,et al.  A Simple Proof of the Restricted Isometry Property for Random Matrices , 2008 .

[5]  Richa Singh,et al.  Review of Iris Presentation Attack Detection Competitions , 2019, Handbook of Biometric Anti-Spoofing, 2nd Ed..

[6]  Lei Zhang,et al.  Projective dictionary pair learning for pattern classification , 2014, NIPS.

[7]  Rama Chellappa,et al.  Sparsity inspired selection and recognition of iris images , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[8]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  John Daugman,et al.  High Confidence Visual Recognition of Persons by a Test of Statistical Independence , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Tien Dat Nguyen,et al.  Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor , 2018, Sensors.