On the Performance Improvement of Iris Biometric System

Abstract

Iris is an established biometric modality with many practical applications. Its performance is influenced by noise, database size, and feature representation. This
thesis focusses on mitigating these challenges by efficiently characterising iris texture,developing multi-unit iris recognition, reducing the search space of large iris databases, and investigating if iris pattern change over time.To suitably characterise texture features of iris, Scale Invariant Feature Transform (SIFT) is combined with Fourier transform to develop a keypoint descriptor-F-SIFT. Proposed F-SIFT is invariant to transformation, illumination, and occlusion along with strong texture description property. For pairing the keypoints from gallery and probe iris images, Phase-Only Correlation (POC) function is used. The use of phase
information reduces the wrong matches generated using SIFT. Results demonstrate the effectiveness of F-SIFT over existing keypoint descriptors.To perform the multi-unit iris fusion, a novel classifier is proposed known
as Incremental Granular Relevance Vector Machine (iGRVM) that incorporates incremental and granular learning into RVM. The proposed classifier by design is
scalable and unbiased which is particularly suitable for biometrics. The match scores from individual units of iris are passed as an input to the corresponding iGRVM
classifier, and the posterior probabilities are combined using weighted sum rule. Experimentally, it is shown that the performance of multi-unit iris recognition improves
over single unit iris. For search space reduction, local feature based indexing approaches are developed
using multi-dimensional trees. Such features extracted from annular iris images are used to index the database using k-d tree. To handle the scalability issue of k-d tree,
k-d-b tree based indexing approach is proposed. Another indexing approach using R-tree is developed to minimise the indexing errors. For retrieval, hybrid coarse-to-fine
search strategy is proposed. It is inferred from the results that unification of hybrid search with R-tree significantly improves the identification performance.
Iris is assumed to be stable over time. Recently, researchers have reported that false rejections increase over the period of time which in turn degrades the performance. An empirical investigation has been made on standard iris aging databases to find whether iris patterns change over time. From the results, it is found that the rejections are primarily due to the presence of other covariates such as blur, noise, occlusion, pupil
dilation, and not due to aging