Iris super-resolution using CNNs: is photo-realism important to iris recognition?
This work addresses the challenge of enhancing iris recognition accuracy for applications using low-resolution images from mobile phones or surveillance, but it is incremental as it builds on existing CNN super-resolution techniques.
The authors tackled the problem of improving iris recognition from low-resolution images by testing various CNN-based super-resolution methods, finding that deeper architectures trained on texture databases balancing edge preservation and smoothness led to good recognition results, validated on a database of 1,872 near-infrared iris images and a mobile phone image database.
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.