# CBAM-DenseNet with multi-feature quality filtering: advancing accuracy in small-sample iris recognition

## TL;DR

This paper introduces a new iris recognition method that improves accuracy and robustness using multi-feature fusion and advanced neural networks.

## Contribution

The novel approach combines a quality filtering scheme, improved CAN network, and CBAM-DenseNet for enhanced small-sample iris recognition.

## Key findings

- The method significantly improves recognition accuracy on small sample sizes.
- It demonstrates robustness across various public iris databases.
- Multi-feature fusion and attention mechanisms enhance feature expressiveness.

## Abstract

In the context of the information age, traditional password and key-based authentication mechanisms are no longer sufficient to meet the growing demands for information security. Iris recognition technology has garnered attention due to its high security and uniqueness. Current iris recognition methods based on single feature extraction are prone to loss of feature information, which affects recognition rates. To address this, this paper proposes a multi-feature fusion-based iris recognition method. The method employs a comprehensive quality evaluation scheme to filter iris images, ensuring the quality of the input images. An improved CAN network is used to effectively remove image noise, and a DenseNet network-based iris feature extraction method is combined with a fusion space and attention mechanism (CBAM) to enhance the expressiveness of features. Through experiments with small sample sizes and testing on various public iris databases, the proposed method has been validated for significant improvements in recognition accuracy and robustness.