Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (10): 185-192.DOI: 10.3778/j.issn.1002-8331.2011-0466

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

IrisCodeNet:Iris Feature Coding Network

JIA Dingding, SEHN Wenzhong   

  1. School of Electronic and Information Engineering, Shanghai University of Electric Power, Shanghai 201200, China
  • Online:2022-05-15 Published:2022-05-15



  1. 上海电力大学 电子与信息工程学院,上海 201200

Abstract: Using effective feature extraction algorithm to accurately represent the texture of iris is the key to iris recognition technology. Based on the particularity of the task of iris recognition, IrisCodeNet, a network model for iris feature coding, is presented in this paper. The network architecture uses an improved BasicBlock combined with a loss function AM-Softmax(additive margin softmax) that can expand the decision boundary. What’s more, in order to get the best iris recognition performance, the parameters settings of AM-Softmax, the input forms of iris image preprocessed, the enhancement methods of data and the input sizes of network are studied in detail. The experimental results show that the feature extractor trained by IrisCodeNet is tested on CASIA-Iris-Thousand, CASIA-Iris-Distance and IITD iris databases. The EER(equal error rate) and TAR(true acceptance rate) evaluated are far superior to the used widely traditional algorithms. In particular, IrisCodeNet still achieves excellent recognition results without the steps of traditional iris normalization or precise iris segmentation. The visualization analysis using Grad-CAM(gradient-weighted class activation mapping) algorithm shows that the network framework pays attention to the texture of iris effectively, which proves that IrisCodeNet has a strong ability to extract features of iris.

Key words: iris recognition, feature coding, image preprocessing, AM-Softmax, Grad-CAM

摘要: 使用有效的特征提取算法对虹膜纹理进行准确的表达是虹膜识别技术的关键。基于虹膜识别任务的特殊性,提出了用于虹膜特征编码的网络模型IrisCodeNet。该网络架构使用了改进的BasicBlock,并结合了可以扩大决策边界的损失函数AM-Softmax(additive margin softmax)。为了获取最佳的虹膜识别效果,对AM-Softmax的参数设置、虹膜图像预处理输入形式、数据增强方式、网络输入尺寸做了细致的研究。实验结果表明:使用IrisCodeNet训练得到的特征提取器在CASIA-Iris-Thousand、CASIA-Iris-Distance、IITD虹膜数据库上进行测试,所评估的等错误率(equal error rate,EER)和正确接受率(true acceptance rate,TAR)均远远超过了广泛应用的传统算法。特别地,IrisCodeNet无需传统的虹膜归一化或精确的虹膜分割步骤依然取得了极好的识别效果。并且使用Grad-CAM(gradient-weighted class activation mapping)算法进行了可视化分析,结果表明该网络框架有效地关注了虹膜纹理信息,从而证明了IrisCodeNet具有较强的虹膜纹理特征提取能力。

关键词: 虹膜识别, 特征编码, 图像预处理, AM-Softmax, Grad-CAM