Abstract
In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user’s eyes looking somewhere else, not into the front of the camera), specular reflection (SR) and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR) illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs). Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II) training dataset (selected from the university of Beira iris (UBIRIS).v2 database), mobile iris challenge evaluation (MICHE) database, and institute of automation of Chinese academy of sciences (CASIA)-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.
Highlights
Over the recent years, biometric technology has been widely used in a variety of fields such as financial transactions, access control and smartphone security
We propose a new method of recognition by using one iris and two periocular regions based on the three convolutional neural networks (CNNs)
We used the NICE.II training dataset for conducting the experiments on the method proposed by this study
Summary
Biometric technology has been widely used in a variety of fields such as financial transactions, access control and smartphone security. When iris images are obtained under unconstrained conditions or without the user’s consent, the quality of the image may be undermined due to optical and motion blur, off-angle view (the user’s eyes looking elsewhere and not into the camera lens), specular reflection (SR) and other factors. These noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. An NIR illuminator of high intensity is usually necessary in order to capture the image of clear iris patterns, which leads to additional power consumption and can be an obstacle for the adoption in smartphones. To consider these issues with the practical and research reasons, studies visible light
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