Cancelable Iris Biometrics Based on Transformation Network

The application of iris biometric data has become prevalent across various domains, encompassing access control, identity verification, and criminal investigations. Consequently, there is a pressing need to develop effective methods for safeguarding the privacy of iris data. While numerous methods for iris data protection have been proposed, the majority of them fall short of meeting the ISO/IEC 24745 standards about irreversibility, revocability, and unlinkability. In this paper, we introduce a novel iris data protection method called TNCB, which is based on a transformation network. The TNCB involves performing a block-wise permutation of the original iris images using application-specific parameters, followed by pixel-by-pixel modulo and inversion fusion operations. The resulting images are subsequently employed for pre-training a recognition network that will be used to recognize protected images. Afterwards, a transformation network is introduced to achieve a further non-invertible transformation. Our security analysis demonstrates that the TNCB could fulfill the three major security requirements. To validate its effectiveness, we conducted a series of attack and performance experiments on the CASIA-Iris-Lamp and CASIA-Iris-Thousand datasets. Experimental results substantiated the robustness of TNCB in maintaining recognition performance while safeguarding the privacy of iris data. Furthermore, experimental results also highlight that our scheme could effectively support iris recognition in both open-set and close-set modes.