DeepIris: Iris Recognition Using A Deep Learning Approach
This work addresses iris recognition for security applications, but it is incremental as it applies a known deep learning method to an existing biometric task.
The authors tackled iris recognition by proposing an end-to-end deep learning framework based on a residual CNN, achieving promising results and improvements over previous approaches with only a few training images per class.
Iris recognition has been an active research area during last few decades, because of its wide applications in security, from airports to homeland security border control. Different features and algorithms have been proposed for iris recognition in the past. In this paper, we propose an end-to-end deep learning framework for iris recognition based on residual convolutional neural network (CNN), which can jointly learn the feature representation and perform recognition. We train our model on a well-known iris recognition dataset using only a few training images from each class, and show promising results and improvements over previous approaches. We also present a visualization technique which is able to detect the important areas in iris images which can mostly impact the recognition results. We believe this framework can be widely used for other biometrics recognition tasks, helping to have a more scalable and accurate systems.