Using iris and sclera for detection and classification of contact lenses

Detecting contact lenses is required for reliable iris-based authentication.Iris and sclera information are both relevant for detection and classification.A reliable segmentation method is devised to extract iris and sclera.A bag-of-feature method based on dense descriptors is proposed.Classification performance improves significantly w.r.t. previous state-of-the-art. Detecting the presence of contact lenses and their type helps increasing the reliability of iris-based authentication systems. We propose a machine-learning approach for this task, based on expressive local image descriptors. The image is first segmented to extract the iris and sclera regions, then scale-invariant local descriptors (SID) are computed densely on both areas, and summarized through the Bag-of-Features paradigm. Classification is based on a properly trained linear SVM. The major contributions of our proposal concern the segmentation algorithm, the use of information drawn from the sclera, and the use of non-rectified data to preserve local structures. A number of variants of the proposed method are investigated, working on different areas of the image, with alternative local descriptors, and with different encoding techniques. Eventually, results are compared with the state-of-the-art in the field. The experimental analysis, carried out on several publicly available datasets, shows that the proposed classification method based on a dense scale invariant descriptor outperforms all the reference techniques.