A novel cancelable iris recognition system based on feature learning techniques

Article Type

Research Article

Publication Title

Information Sciences


A novel cancelable iris recognition system is proposed in this paper. Based on the performance of various feature learning techniques such as (i) Bag-of-Words, (ii) Sparse Representation Coding and (iii) Locality-constrained Linear Coding we choose the second one followed by Spatial Pyramid Mapping technique for feature computation from iris pattern. To build the proposed system the existing BioHashing technique is modified using two different tokens: one is user specific and the other user independent. To test the performance of the proposed system we have tried it on six benchmark iris databases namely: MMU1, UPOL, CASIA-Interval-v3, IITD, UBIRIS.v1 and CASIA-syn. The experimental results are demonstrated for each database and are compared with that of the state-of-the-art methods with respect to these databases. The results show the robustness and effectiveness of the proposed approach.

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