Advances in Iris Recognition: Transfer Learning and Enhanced Preprocessing

his repository contains the implementation of an iris recognition system based on transfer learning and enhanced preprocessing techniques. The proposed approach improves feature extraction quality and increases recognition accuracy under varying imaging conditions.

The code includes modules for image preprocessing, feature extraction using pre-trained deep learning models, and classification. The preprocessing stage incorporates noise reduction, normalization, and contrast enhancement to improve the quality of iris images prior to feature extraction.

Transfer learning is applied using a pre-trained convolutional neural network (CNN), which is fine-tuned for iris recognition tasks. The system is evaluated on standard iris datasets, demonstrating improved robustness and performance compared to baseline methods.

本仓库实现了一套基于迁移学习与增强预处理技术的虹膜识别系统。所提出的方法可提升特征提取质量，并在不同成像条件下提高识别准确率。

该代码包含图像预处理、基于预训练深度学习模型的特征提取以及分类三大模块。预处理阶段集成了降噪、归一化与对比度增强操作，用于在特征提取前优化虹膜图像质量。

本系统采用预训练卷积神经网络（Convolutional Neural Network, CNN）实现迁移学习，并针对虹膜识别任务对该网络进行微调。经标准虹膜数据集验证，相较于基线方法，本系统的鲁棒性与性能均得到显著提升。