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

• URL: http://arxiv.org/abs/2210.13125v1
• Date: Mon, 24 Oct 2022 11:19:18 GMT
• Title: Iris super-resolution using CNNs: is photo-realism important to iris recognition?
• Authors: Eduardo Ribeiro, Andreas Uhl, Fernando Alonso-Fernandez
• Abstract summary: Single image super-resolution techniques are emerging, especially with the use of convolutional neural networks (CNNs) In this work, the authors explore single image super-resolution using CNNs for iris recognition. They validate their approach on a database of 1.872 near infrared iris images and on a mobile phone image database.
• Score: 67.42500312968455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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