Abstract

The high demand for ever more accurate biometric systems has driven the search for methods that reconstruct the iris surface in a 3D model. The intent in adding the depth dimension is to improve accuracy even in large databases. Here, we present a novel approach to iris recognition from 3D models. First, the iris 3D model is reconstructed from a single image using irisDepth, a CNN based method. Then, a 3D descriptor called Spin Image is obtained for keypoints of the 3D model. After that, matches are found between keypoints in the query and the reference 3D models using k-dimensional trees. Finally, those keypoint matches are used to determine the spatial transformation that best aligns the 3D models. A combination of the transformation error and the inlier ratio is used as the metric to assess the similarity of two iris 3D models. We applied this method in a dataset of 100 eyes and 2, 000 iris 3D models. Our results indicate that using the proposed method is more effective than alternative methods, such as Dougman's iris code, point-to-point distance between the 3D models, the 3D rubber sheet model, and CNN-based methods.