Generation of Cancelable Iris Templates via Randomized Bit Sampling

Iris-based biometric models are widely recognized to be one of the most accurate forms for authenticating individual identities. Features extracted from the captured iris images (known as IrisCodes) conventionally get stored in their native format over a data repository. However, from a security aspect, the stored templates are highly vulnerable to a wide spectrum of adversarial attack forms. The study in this paper addresses this issue by introducing a privacy-preserving and secure biometric scheme based on the notion of locality sensitive hashing (LSH). In this paper, we have generated cancelable IrisCode features, coined as locality sampled code (LSC), which simultaneously provides strong security guarantees and satisfactory system performance. The functionality of our proposed framework pivots around the fact that intra-class IrisCode samples are close to each other, due to which they hash to the same location. Alternatively, the inter-class IrisCodes features are comparatively dissimilar and consequently hash to different locations. We have rigorously examined the intrinsic properties of the LSCs by estimating the intra-class and inter-class collision probabilities for two distinct IrisCodes. Furthermore, we have formally analyzed the security guarantees of non-invertibility, revocability, and unlinkability in our model by establishing various bounds on the adversarial success probability. Extensive empirical tests on the CASIAv3 and IITD benchmark iris databases demonstrate the superior performance of our proposed model, for which we have obtained the best EERs of 0.105% and 1.4%, respectively.