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

• Published in: Information sciences Vol. 491; pp. 1 - 15
• Main Authors: Sadhya, Debanjan, De, Kanjar, Balasubramanian, Raman, Pratim Roy, Partha
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.07.2019
• Subjects: Biometrics; Consistent bits; Fusion; Iris; Biometrics; Iris; Consistent bits; Fusion
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2019.04.001

•A framework has been developed for extracting the most consistent bits from iris features (IrisCodes).•The proposed model is based on the incorporation of some novel features such as use of iris masks and optimized scoring based on 1D cluster formation.•The results obtained on four benchmark iris databases (CASIAv3 Interval, IITD, MMU2, and CASIAv4 Thousand) demonstrate significant improvements over the baseline EER (%). 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 IndexS(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.