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

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.

ScatT-LOOP: scattering tetrolet-LOOP descriptor and optimized NN for iris recognition at-a-distance

Iris Recognition at-a Distance (IAAD) is a major challenge for researchers due to the defects associated with the visual imaging and poor image quality in dynamic environments, which imposed bad impacts on the accuracy of recognition. Thus, in order to enable the effective IAAD, this paper proposes a new method, named, Chronological Monarch Butterfly Optimization (Chronological MBO)-enabled Neural Network (NN). The recognition of iris using NN is trained with the proposed Chronological MBO, which is developed through the combination of Chronological theory in Monarch Butterfly Optimization (MBO). The recognition becomes effective with the automatic segmentation and the normalization of iris image on the basis of Hough Transform (HT) and Daugman's rubber sheet model followed with the process of feature extraction with the developed ScatT-LOOP descriptor, which is the integration of scattering transform (ST), Local Optimal Oriented Pattern (LOOP) descriptor, and Tetrolet transform (TT). The developed ScatT-LOOP descriptor extracts the texture as well as the orientation details of image for effective recognition. The analysis is evaluated with the CASIA Iris dataset with respect to the evaluation metrics, accuracy, False Acceptance Rate (FAR), and False Rejection Rate (FRR). The proposed method has the accuracy, FRR, and FAR of 0.97, 0.005, and 0.005, respectively. The experimental results proved that the proposed method is effective than the existing methods of iris recognition.