Abstract
The recent advancements in computer vision have opened new horizons for deploying biometric recognition algorithms in mobile and handheld devices. Similarly, iris recognition is now much needed in unconstraint scenarios with accuracy. These environments make the acquired iris image exhibit occlusion, low resolution, blur, unusual glint, ghost effect, and off-angles. The prevailing segmentation algorithms cannot cope with these constraints. In addition, owing to the unavailability of near-infrared (NIR) light, iris recognition in visible light environment makes the iris segmentation challenging with the noise of visible light. Deep learning with convolutional neural networks (CNN) has brought a considerable breakthrough in various applications. To address the iris segmentation issues in challenging situations by visible light and near-infrared light camera sensors, this paper proposes a densely connected fully convolutional network (IrisDenseNet), which can determine the true iris boundary even with inferior-quality images by using better information gradient flow between the dense blocks. In the experiments conducted, five datasets of visible light and NIR environments were used. For visible light environment, noisy iris challenge evaluation part-II (NICE-II selected from UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE-I) datasets were used. For NIR environment, the institute of automation, Chinese academy of sciences (CASIA) v4.0 interval, CASIA v4.0 distance, and IIT Delhi v1.0 iris datasets were used. Experimental results showed the optimal segmentation of the proposed IrisDenseNet and its excellent performance over existing algorithms for all five datasets.
Highlights
In the last two decades, biometrics have been completely incorporated into our daily life
The IrisDenseNet dense encoder consists of 18 layers, including five 1 × 1 Conv layers used as the bottleneck layer in transition layers after each convolutional layers, including five 1 × 1 Conv layers used as the bottleneck layer in transition layers dense block, which is useful in reducing the number of input feature maps for computational efficiency
The noisy iris challenge evaluation part-II (NICE-II) dataset is used as iris images in visible light environment
Summary
In the last two decades, biometrics have been completely incorporated into our daily life. Biometrics are adopted to various applications such as person authentication and identification at airports or in national databases. Most iris recognition systems consist of five elementary steps: iris image acquisition, pre-processing, iris boundary segmentation, iris feature extraction, and matching for authentication or identification. In non-ideal environments, the images contain blurs, off-angles, non-uniform light intensities, and obstructions In both ideal and non-ideal environments, a real iris boundary without occlusion is required for better error-free features, so a segmentation algorithm is needed to separate each type of noise from the iris image and provide a real iris boundary even in non-ideal situations [13]. Proenca et al analyzed 5000 images of UBRIS, CASIA, and ICE databases, and concluded that the incorrect segmentation in horizontal and vertical directions affects the recognition errors [16]
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