Margin Based Feature Selection for Cross-Sensor Iris Recognition via Linear Programming
The wide deployments of iris recognition systems promote the emergence of different types of iris sensors. Large differences such as illumination wavelength and resolution result in cross-sensor variations of iris texture patterns. These variations decrease the accuracy of iris recognition. To address this issue, a feasible solution is to select an optimal effective feature set for all types of iris sensors. In this paper, we propose a margin based feature selection method for cross-sensor iris recognition. This method learns coupled feature weighting factors by minimizing a cost function, which aims at selecting the feature set to represent the intrinsic characteristics of iris images from different sensors. Then, the optimization problem can be formulated and solved using linear programming. Extensive experiments on the Notre Dame Cross Sensor Iris Database and CASIA cross sensor iris database show that the proposed method outperforms conventional feature selection methods in cross-sensor iris recognition.