Efficient iris segmentation algorithm using deep learning techniques

Abstract

The iris segmentation algorithm plays a vital role in iris recognition systems and directly controls the authentication and recognition results. However, traditional iris segmentation techniques have reduced performance, and they are not robust enough when used in noisy iris databases captured under immeasurable conditions. In addition, there is currently no extensive iris database. Therefore, the iris segmentation algorithms cannot reap the benefits of the convolution neural networks. Iris segmentation plays an essential role in maintaining iris accuracy by restricting the defects in iris images. Under these nonideal conditions, presented segmentation based on local operations cannot see the actual iris boundary, and the iris segmentation will fail. The proposed solution contributes to the effect of the distribution of the most irregular iris images under visible light. Gray level co-occurring matrix is proposed for segmentation, and VGG 16 and VGG 19 are applied for iris classification. The proposed iris recognition method divides the iris segmentation process into two steps: localization of the iris area of the eye and segmentation. Well-designed VGG 16 and VGG 19 networks are used to distinguish and locate the eyes. Performance for both the proposed models shows improvement over other techniques in terms of accuracy, precision, recall, E1, and E2.