A keypoints-based feature extraction method for iris recognition under variable image quality conditions

Iris recognition is a very reliable biometric modality for human identification. The immutable and unique characteristics of the iris are the foundations for that claim. Currently, research interest in this field points to challenges regarding less-constrained iris recognition systems. In response, we propose a robust keypoints-based feature extraction method for iris recognition under variable image quality conditions. To this end, three detectors have been used to identify distinctive keypoints: Harris-Laplace, Hessian-Laplace, and Fast-Hessian. Once the three sources of keypoints are obtained, they are described in terms of SIFT features. The proposed method combines the three information sources of SIFT features at matching score level. The combination of these sources reinforces the discriminative power of the proposal for recognition on highly or less textured iris images. The fusion is carried out using a proposed weighted sum rule relies on the ranking of three performance measures. The proposed fusion rule computes weights, which represent the reliability degree to which each individual source must contribute in order to determine the more discriminative matching scores. Our experiments rely on iris standard databases which as a whole constitute a challenging and perfect example of variable image quality conditions. According to the results, our proposal is very competitive and outperforms the state-of-the-art algorithms on the topic. In addition, it is demonstrated that the proposed keypoints-based feature extraction method is feasible and that it could be used even in real-time applications if the database is previously processed.