Adaboost and multi-orientation 2D Gabor-based noisy iris recognition

In this paper, we present a noisy iris recognition frame which is learned by Adaboost on a 2D Gabor-based feature set. First, the irises are segmented and normalized by rubber sheet or simplified rubber sheet according to whether segmentations are accurate or not. Then, irises are divided into different amount of patches according to normalization. Moreover, a feature set is constructed based on 2D-Gabor for whole iris and patches. Finally, Adaboost learning is used for accurately and inaccurately segmented irises separately.The proposed method was evaluated by the NICE:II (Noisy Iris Challenge Evaluation – Part 2). We were ranked 2nd among all of the 67 participants from 29 different countries/districts.

► Both global and local texture information are used for iris recognition. ► A set of regional hamming distance combined with Adaboost is used for classifiers. ► A new method is proposed for the not accurately segmented noise irise recognition. ► Two recognition methods are adopted according to the segmentations.