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, specially with the use of convolutional neural networks. The main objective of these methods is to try to recover finer texture details generating more photo-realistic images based on the optimization of an objective function depending basically on the CNN architecture and the training approach. In this work, we explore single image Super-Resolution using CNNs for iris recognition. For this, we test different CNN architectures as well as the use of different training databases, validating our approach on a database of 1.872 near infrared iris images and on a mobile phone image database. We 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. © The Institution of Engineering and Technology 2015