Cross-sensor iris verification applying robust fused segmentation algorithms

Currently, identity management systems work with heterogeneous iris images captured by different types of iris sensors. Indeed, iris recognition is being widely used in different environments where the identity of a person is necessary. Therefore, it is a challenging problem to maintain a stable irisrecognition system which is effective for all type of iris sensors. This paper proposes a new cross-sensor iris recognition scheme that increases the recognition accuracy.

The novelty of this work is the new strategy in applying robust fusion methods at level of segmentation stage for cross-sensor irisrecognition. The experiments with the Casia-V3-Interval, Casia-V4-Thousand, Ubiris-V1 and MBGC-V2 databases show that our scheme increases the recognition accuracy and it is robust to different types of iris sensors while the user interaction is reduced.

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