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Sensors 2017, 17(12), 2933;

Noisy Ocular Recognition Based on Three Convolutional Neural Networks

Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea
Author to whom correspondence should be addressed.
Received: 18 October 2017 / Revised: 10 December 2017 / Accepted: 14 December 2017 / Published: 17 December 2017
(This article belongs to the Section Physical Sensors)
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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. View Full-Text
Keywords: noisy iris and ocular image; iris and periocular; convolutional neural network noisy iris and ocular image; iris and periocular; convolutional neural network

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Lee, M.B.; Hong, H.G.; Park, K.R. Noisy Ocular Recognition Based on Three Convolutional Neural Networks. Sensors 2017, 17, 2933.

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