Convolutional neural networks were used for multiclass segmentation in thermal infrared face analysis. The principle is based on existing image-to-image translation approaches, where each pixel in an image is assigned to a class label. We show that established networks architectures can be trained for the task of multiclass face analysis in thermal infrared. Created class annotations consisted of pixel-accurate locations of different face classes. Subsequently, the trained network can segment an acquired unknown infrared face image into the defined classes. Furthermore, face classification in live image acquisition is shown, in order to be able to display the relative temperature in real-time from the learned areas. This allows a pixel-accurate temperature face analysis e.g. for infection detection like Covid-19. At the same time our approach offers the advantage of concentrating on the relevant areas of the face. Areas of the face irrelevant for the relative temperature calculation or accessories such as glasses, masks and jewelry are not considered. A custom database was created to train the network. The results were quantitatively evaluated with the intersection over union (IoU) metric. The methodology shown can be transferred to similar problems for more quantitative thermography tasks like in materials characterization or quality control in production.

中文翻译：

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用于语义分割的卷积神经网络作为热红外中多类人脸分析的工具

卷积神经网络用于热红外人脸分析中的多类分割。该原理基于现有的图像到图像转换方法，其中图像中的每个像素都分配给一个类标签。我们表明，可以对已建立的网络架构进行训练，以完成热红外中的多类人脸分析任务。创建的类注释由不同人脸类的像素精确位置组成。随后，经过训练的网络可以将获取的未知红外人脸图像分割成定义的类别。此外，还显示了实时图像采集中的面部分类，以便能够实时显示学习区域的相对温度。这允许进行像素精确的温度面部分析，例如用于像 Covid-19 这样的感染检测。同时，我们的方法提供了专注于面部相关区域的优势。与相对温度计算无关的面部区域或眼镜、口罩和珠宝等配饰不考虑在内。创建了一个自定义数据库来训练网络。使用交集联合 (IoU) 度量对结果进行了定量评估。显示的方法可以转移到类似的问题，以进行更多的定量热成像任务，例如材料表征或生产中的质量控制。使用交集联合 (IoU) 度量对结果进行了定量评估。显示的方法可以转移到类似的问题，用于更多的定量热成像任务，如材料表征或生产中的质量控制。使用交集联合 (IoU) 度量对结果进行了定量评估。显示的方法可以转移到类似的问题，以进行更多的定量热成像任务，例如材料表征或生产中的质量控制。