Abstract
The use of low-resolution images adopting more relaxed acquisition conditions such as mobile phones and surveillance videos is becoming increasingly common in iris recognition nowadays. Concurrently, a great variety of single image super-resolution techniques are emerging, especially with the use of convolutional neural networks (CNNs). The main objective of these methods is to try to recover finer texture details generating more photo-realistic images based on the optimisation of an objective function depending basically on the CNN architecture and training approach. In this work, the authors explore single image super-resolution using CNNs for iris recognition. For this, they test different CNN architectures and use different training databases, validating their approach on a database of 1.872 near infrared iris images and on a mobile phone image database. They also use quality assessment, visual results and recognition experiments to verify if the photo-realism provided by the CNNs which have already proven to be effective for natural images can reflect in a better recognition rate for iris recognition. The results show that using deeper architectures trained with texture databases that provide a balance between edge preservation and the smoothness of the method can lead to good results in the iris recognition process.
Highlights
The main goal of super resolution (SR) is to produce, from one or more images, an image with a higher resolution at the same time that produces a more detailed and realistic image being faithful to the low-resolution (LR) image(s)
The describable texture dataset (DTD) with 5640 images of sizes range between 300 × 300 and 640 × 640 categorised into 47 classes [34]
We explore different convolutional neural networks (CNNs) architectures that are proven to be effective in reconstructing natural images for iris SR
Summary
The main goal of super resolution (SR) is to produce, from one or more images, an image with a higher resolution (with more pixels) at the same time that produces a more detailed and realistic image being faithful to the low-resolution (LR) image(s). One of the most used examples is bicubic interpolation that, despite producing more pixels and being faithful to the image at LR, does not produce more detailed texture details generating more noise or blur than realism [1]. Especially in the pattern recognition area, demand, in an ideal environment, images in high resolution (HR) where details and textures from the images may be critical to the final result [2]. One of them is biometrics as, e.g. face and iris recognition using mobile phone devices. The lack of pixel resolution in images supplied by less robust sensors (such as mobile phones or surveillance cameras) and the focal length may compromise the performance of recognition systems [3]. In [4], a significant recognition performance degradation is shown when the iris image resolution is reduced
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