Recent Iris and Ocular Recognition Methods in High- and Low-Resolution Images: A Survey

Abstract

Among biometrics, iris and ocular recognition systems are the methods that recognize eye features in an image. Such iris and ocular regions must have a certain image resolution to achieve a high recognition performance; otherwise, the risk of performance degradation arises. This is even more critical in the case of iris recognition where detailed patterns are used. In cases where such low-resolution images are acquired and the acquisition apparatus and environment cannot be improved, recognition performance can be enhanced by obtaining high-resolution images with methods such as super-resolution reconstruction. However, previous survey papers have mainly summarized studies on high-resolution iris and ocular recognition, but do not provide detailed summaries of studies on low-resolution iris and ocular recognition. Therefore, we investigated high-resolution iris and ocular recognition methods and introduced in detail the low-resolution iris and ocular recognition methods and methods of solving the low-resolution problem. Furthermore, since existing survey papers have focused on and summarized studies on traditional handcrafted feature-based iris and ocular recognition, this survey paper also introduced the latest deep learning-based methods in detail.

Keywords

• iris and ocular recognition
• high- and low-resolution images
• super-resolution reconstruction
• handcrafted feature
• deep learning