论文标题
虹膜识别中基于深度学习的特征提取：使用现有模型，从头开始使用微调或训练？
Deep Learning-Based Feature Extraction in Iris Recognition: Use Existing Models, Fine-tune or Train From Scratch?
论文作者
论文摘要
现代深度学习技术可用于为虹膜识别的任务生成有效的功能提取器。问题出现了：我们应该在相对较大的虹膜图像数据集上从头开始训练此类结构，还是最好微调现有模型以使其适应新域？在这项工作中，我们探索了流行的Resnet-50体系结构的五组不同的权重，以找出特定于特定的功能提取器是否比训练非IRIS任务的模型更好。从每个卷积层中提取特征，并且在数据集中测量了支持向量机所达到的分类精度，该数据集与训练Resnet-50模型训练中使用的样品不相交。我们表明，最佳训练策略是对虹膜识别域进行一组现成的权重。这种方法的准确性比现成的重量和从头开始训练的模型更高。与先前的工作相比，获胜的微调方法还显示出性能的提高，在虹膜功能提取中，仅使用现成的模型（不是微调）模型。我们制作了表现最佳的Resnet-50型号，并用超过360,000次虹膜图像进行了微调，并与本文公开可用。
Modern deep learning techniques can be employed to generate effective feature extractors for the task of iris recognition. The question arises: should we train such structures from scratch on a relatively large iris image dataset, or it is better to fine-tune the existing models to adapt them to a new domain? In this work we explore five different sets of weights for the popular ResNet-50 architecture to find out whether iris-specific feature extractors perform better than models trained for non-iris tasks. Features are extracted from each convolutional layer and the classification accuracy achieved by a Support Vector Machine is measured on a dataset that is disjoint from the samples used in training of the ResNet-50 model. We show that the optimal training strategy is to fine-tune an off-the-shelf set of weights to the iris recognition domain. This approach results in greater accuracy than both off-the-shelf weights and a model trained from scratch. The winning, fine-tuned approach also shows an increase in performance when compared to previous work, in which only off-the-shelf (not fine-tuned) models were used in iris feature extraction. We make the best-performing ResNet-50 model, fine-tuned with more than 360,000 iris images, publicly available along with this paper.
