Recognition Oriented Iris Image Quality Assessment in the Feature Space

TL;DR

This paper introduces a recognition-oriented iris image quality assessment method using deep neural networks that improves recognition performance and reduces image rejection, addressing limitations of traditional hand-crafted quality metrics.

Contribution

The paper proposes a novel iris image quality metric based on feature space distance and deep learning, bridging the gap between quality assessment and recognition performance.

Findings

Significantly improves iris recognition accuracy.

Reduces the number of images discarded for recognition.

Establishes a relationship between IRR and EER for evaluation.

Abstract

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.