Browsing by Author "Prasad, M.V.N.K."

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    Cancelable iris template generation using modulo operation
    (2018) Prasad, M.V.N.K.; Jyothi, A.; Lasya, K.
    Templates in conventional iris recognition systems are unprotected and highly vulnerable to numerous security and privacy issues. To overcome these issues, biometric template is morphed before storing. The morphed template is called cancelable template and it is unlinkable and non-invertible. The cancelable can be easily replaced and reissued. In this paper, we proposed a novel approach of protecting biometric template based on modulo operation. The proposed method utilizes consistent bit vector which is generated from pre-aligned IrisCodes. These IrisCodes are generated by applying 1-D Log Gabor filter on the iris images. To confirm the efficiency of proposed approach, experiments are conducted on different iris data-sets. By applying proposed cancelable technique, we achieved Equal Error Rate of 0.54% and 0.86% for CASIA-V 1.0 and CASIA-V3-Interval iris datasets respectively. This proposed method also satisfies revocability, unlinkability and irreversibilty criteria. � 2017 IEEE.
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    Cancelable iris template generation using modulo operation
    (Institute of Electrical and Electronics Engineers Inc., 2017) Prasad, M.V.N.K.; Jyothi, A.; Lasya, K.
    Templates in conventional iris recognition systems are unprotected and highly vulnerable to numerous security and privacy issues. To overcome these issues, biometric template is morphed before storing. The morphed template is called cancelable template and it is unlinkable and non-invertible. The cancelable can be easily replaced and reissued. In this paper, we proposed a novel approach of protecting biometric template based on modulo operation. The proposed method utilizes consistent bit vector which is generated from pre-aligned IrisCodes. These IrisCodes are generated by applying 1-D Log Gabor filter on the iris images. To confirm the efficiency of proposed approach, experiments are conducted on different iris data-sets. By applying proposed cancelable technique, we achieved Equal Error Rate of 0.54% and 0.86% for CASIA-V 1.0 and CASIA-V3-Interval iris datasets respectively. This proposed method also satisfies revocability, unlinkability and irreversibilty criteria. © 2017 IEEE.