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

TL;DR

This paper investigates the impact of CNN-based super-resolution techniques on iris recognition, analyzing whether photo-realistic images improve recognition accuracy in low-resolution scenarios.

Contribution

It evaluates different CNN architectures and training databases to determine their effectiveness in enhancing iris recognition through super-resolution.

Findings

Deeper CNN architectures trained on texture databases improve recognition rates.

Balance between edge preservation and smoothness yields better super-resolution results.

Photo-realism in super-resolved images correlates with improved iris recognition performance.

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.