  A large portion of iris images captured in real world scenarios are poor
quality due to the uncontrolled environment and the non-cooperative subject. To
ensure that the recognition algorithm is not affected by low-quality images,
traditional hand-crafted factors based methods discard most images, which will
cause system timeout and disrupt user experience. In this paper, we propose a
recognition-oriented quality metric and assessment method for iris image to
deal with the problem. The method regards the iris image embeddings Distance in
Feature Space (DFS) as the quality metric and the prediction is based on deep
neural networks with the attention mechanism. The quality metric proposed in
this paper can significantly improve the performance of the recognition
algorithm while reducing the number of images discarded for recognition, which
is advantageous over hand-crafted factors based iris quality assessment
methods. The relationship between Image Rejection Rate (IRR) and Equal Error
Rate (EER) is proposed to evaluate the performance of the quality assessment
algorithm under the same image quality distribution and the same recognition
algorithm. Compared with hand-crafted factors based methods, the proposed
method is a trial to bridge the gap between the image quality assessment and
biometric recognition. The code is available at
https://github.com/Debatrix/DFSNet.
