Development of a clustering based fusion framework for locating the most consistent IrisCodes bits

Iris-based biometric systems are widely considered as one of the most accurate forms for authenticating individual identities. Features from an iris image are commonly represented as a sequence of bits, known as IrisCodes. The work in this paper focuses on locating and subsequently extracting the most consistent bit-locations from these binary iris features. We achieve this objective by initially constructing a Matching-Code vector from some specifically designated training IrisCodes, and subsequently forming a series of 1D clusters in them. Every cluster element is then assigned a score in the range [0 - 1] on the basis of two cluster properties - the size of the cluster it belongs to and its distance from the center of the cluster. We term this cumulative score as the Significance Index S(b) for a cluster element b. Finally, we select those locations which correspond to the highest scores for every IrisCode. We have tested our approach for four benchmark iris databases (CASIAv3-Interval, CASIAv4-Thousand, IIT Delhi and MMU2) while varying the number of extracted bit-locations from 50 to 300. Our empirical results exhibit significant improvements over the baseline results regarding both the consistency of the extracted bit-locations, as well as the overall performance of the resulting biometric system. (C) 2019 Elsevier Inc. All rights reserved.