Quality-based iris segmentation-level fusion

Abstract

Iris localisation and segmentation are challenging and critical tasks in iris biometric recognition. Especially in non-cooperative and less ideal environments, their impact on overall system performance has been identified as a major issue. In order to avoid a propagation of system errors along the processing chain, this paper investigates iris fusion at segmentation-level prior to feature extraction and presents a framework for this task. A novel intelligent reference method for iris segmentation-level fusion is presented, which uses a learning-based approach predicting ground truth segmentation performance from quality indicators and model-based fusion to create combined boundaries. The new technique is analysed with regard to its capability to combine segmentation results (pupillary and limbic boundaries) of multiple segmentation algorithms. Results are validated on pairwise combinations of four open source iris segmentation algorithms with regard to the public CASIA and IITD iris databases illustrating the high versatility of the proposed method.

Accuracy enhancement of biometric recognition using iterative weights optimization algorithm

“…Extensive work has been done in the area of biometric recognition using different traits such as face, finger print, iris, voice, different hand-based modalities, gait, etc. [1][2][3][4]. However, unimodal systems have their own advantages and limitations.…”