论文标题
使用CNNS的虹膜超分辨率：光真实主义对虹膜识别重要吗？
Iris super-resolution using CNNs: is photo-realism important to iris recognition?
论文作者
论文摘要
如今，采用更轻松的采集条件（例如手机和监视视频）的使用低分辨率图像在如今的虹膜识别中变得越来越普遍。同时，出现了各种各样的单图超分辨率技术，尤其是在使用卷积神经网络（CNN）的情况下。这些方法的主要目的是尝试根据基本取决于CNN体系结构和培训方法的优化，基于目标函数的优化来恢复更多的纹理细节，从而生成更多的照片现实图像。在这项工作中，作者使用CNN探索单图超分辨率，以供虹膜识别。为此，他们测试了不同的CNN体系结构并使用不同的培训数据库，从而在红外IRIS图像附近的1.872数据库和手机图像数据库上验证了他们的方法。他们还使用质量评估，视觉结果和识别实验来验证CNN提供的照片真实性是否已被证明对自然图像有效，是否可以反映出虹膜识别的更好的识别率。结果表明，使用培训的质地数据库训练的更深层次的体系结构在边缘保存和该方法的平滑度之间提供平衡，可以在虹膜识别过程中取得良好的结果。
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.
