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.