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access icon openaccess Iris super-resolution using CNNs: is photo-realism important to iris recognition?

  • Author(s): Eduardo Ribeiro 1, 2 ; Andreas Uhl 1 ; Fernando Alonso-Fernandez 3
    • View affiliations
  • Source: Volume 8, Issue 1, January 2019, p. 69 – 78
    DOI:  10.1049/iet-bmt.2018.5146 , Print ISSN 2047-4938, Online ISSN 2047-4946
This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 04/12/2017, Accepted 22/06/2018, Revised 19/06/2018, Published 03/08/2018

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.

Inspec keywords: image texture; iris recognition; video signal processing; image resolution; edge detection; visual databases; feedforward neural nets; realistic images; feature extraction

Other keywords: low-resolution images; photo-realism; near infrared iris images; iris super-resolution; mobile phone image database; texture databases; quality assessment; iris recognition process; training databases; edge preservation; single image super-resolution techniques; natural images; convolutional neural networks; CNN architectures; texture recovery; photorealistic image generation; objective function optimisation; surveillance videos

Subjects: Image recognition; Computer vision and image processing techniques; Neural computing techniques; Video signal processing

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