基于残差网络的虹膜图像性别分类

Gender Classification of Iris Image Based on Residual Network

生物特征的识别是计算机科学技术一个有吸引力的研究领域,虹膜作为一种软生物特征,具有唯一性、稳定性和防伪性等优点,从虹膜图像中识别一个人的性别在身份验证和安全监控等领域均具有广阔的应用前景。针对传统机器学习与浅层神经网络在虹膜图像性别分类中存在的不足以及卷积神经网络对图像特征提取的优势,提出一种基于残差网络(ResNet)的虹膜图像性别分类模型,采用ResNet结合迁移学习在ImageNet图像数据集上进行预训练。采用该模型在数据集上训练一个端到端的虹膜图像性别分类器,准确率达到94.6%。将训练好的模型与其他相关模型在相同的数据集上进行对比,结果表明该模型的测试精度与识别效率均优于其他模型。

The recognition of biometrics is an attractive research field in computer science and technology.As a soft biometric,the iris has the advantages of uniqueness,stability and anti-counterfeiting.Recognizing the gender of a person from the iris image is used in identity verification and security.Monitoring and other fields have broad application prospects.Aiming at the shortcomings of traditional machine learning and shallow neural networks in gender classification of iris image and the advantages of convolutional neural networks in image feature extraction,a residual network(ResNet)-based gender classification of iris image model is proposed,which uses ResNet combined with transfer learning is used for pre-training on ImageNet image dataset.The model is used to train an end-to-end iris image gender classifier on the dataset,the accuracy rate reaches 94.6%.Comparing the trained model with other related models on the same dataset,the results show that the test accuracy and recognition efficiency of this model are better than other models.