论文标题
在计算机断层扫描中使用面部的人类识别
Human Recognition Using Face in Computed Tomography
论文作者
论文摘要
随着计算机断层扫描（CT）图像在临床决策中的蘑菇使用，CT数据的管理变得越来越困难。从患者识别的角度来看，使用标准的DICOM标签跟踪患者信息的挑战是诸如拼写，丢失的文件，现场变化等问题。在本文中，我们探讨了将3D CT图像中的面孔作为生物识别特征的可行性。具体而言，我们提出了一条自动处理管道，该管道首先检测3D中的面部标记以进行投资提取，然后生成对齐的2D深度图像，该图像用于自动识别。为了提高识别绩效，我们采用转移学习来减少数据稀少性问题，并引入小组抽样策略，以在训练识别网络时增加类间歧视。我们提出的方法能够在减少记忆消耗的同时捕获医学图像中的潜在身份特征。为了测试其有效性，我们策划了来自多个来源的280名患者的600 3D CT图像，以进行绩效评估。实验结果表明，我们的方法达到了1:56的识别精度为92.53％，1：1验证精度为96.12％，表现优于其他竞争方法。
With the mushrooming use of computed tomography (CT) images in clinical decision making, management of CT data becomes increasingly difficult. From the patient identification perspective, using the standard DICOM tag to track patient information is challenged by issues such as misspelling, lost file, site variation, etc. In this paper, we explore the feasibility of leveraging the faces in 3D CT images as biometric features. Specifically, we propose an automatic processing pipeline that first detects facial landmarks in 3D for ROI extraction and then generates aligned 2D depth images, which are used for automatic recognition. To boost the recognition performance, we employ transfer learning to reduce the data sparsity issue and to introduce a group sampling strategy to increase inter-class discrimination when training the recognition network. Our proposed method is capable of capturing underlying identity characteristics in medical images while reducing memory consumption. To test its effectiveness, we curate 600 3D CT images of 280 patients from multiple sources for performance evaluation. Experimental results demonstrate that our method achieves a 1:56 identification accuracy of 92.53% and a 1:1 verification accuracy of 96.12%, outperforming other competing approaches.
