Iris recognition using low-level CNN layers without training and single matching

Abstract

Iris is one of the most accurate biometrics. This has led to the successful development of large-scale applications. However, with population growth, and new international applications, datasets are constantly increasing in size, requiring more robust and faster methods. Many descriptors and feature extractors have been developed to extract features that represent the iris biometric pattern. Most of them have been designed by human experts and require a bit-shifting process to increase their robustness to eye rotations, at the expense of increased matching time. We propose a fast iris recognition method that requires a single matching operation and is based on pre-trained image classification models as feature extractors. Our approach uses the filters of the first layers from Convolutional Neural Networks as feature extractors and does not require fine-tuning for new datasets. Since our selected features extracted from convolutional layers encode the iris surface, they have the advantage of not being restricted to specific spatial positions. Thus, it is not necessary to perform a bit-shifting process in the matching stage, eliminating a significant number of computations. Additionally, to mitigate the effect produced by the mask border in rubber-sheet images, we propose filtering the feature map tensors by masking their channels and selecting the most relevant features. Our method was assessed on the publicly available datasets CASIA Iris Lamp and CASIA Iris Thousand, and showed significant improvement both in accuracy and in matching time.