Abstract
While combining more than one biometric sample, recognition algorithm, modality or sensor, commonly referred to as multi-biometrics, is common practice to improve accuracy of biometric systems, fusion at segmentation level has so far been neglected in literature. This paper introduces the concept of multi-segmentation fusion for combining independent iris segmentation results. Fusion at segmentation level is useful to (1) obtain more robust recognition rates compared to single segmentation; (2) avoid additional storage requirements compared to feature-level fusion, and (3) save processing time compared to employing parallel chains of feature-extractor dependent segmentation. As proof of concept, manually labeled segmentation results are combined using the proposed technique and shown to increase recognition accuracy for representative algorithms on the well-known CASIA-V4-Interval dataset.
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