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High precision automated face localization in thermal images: Oral cancer dataset as test case

机译：热图像中高精度自动面部定位：口腔癌数据集作为测试用例

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Automated face detection is the pivotal step in computer vision aided facial medical diagnosis and biometrics. This paper presents an automatic, subject adaptive framework for accurate face detection in the long infrared spectrum on our database for oral cancer detection consisting of malignant, precancerous and normal subjects of varied age group. Previous works on oral cancer detection using Digital Infrared Thermal Imaging(DITI) reveals that patients and normal subjects differ significantly in their facial thermal distribution. Therefore, it is a challenging task to formulate a completely adaptive framework to veraciously localize face from such a subject specific modality. Our model consists of first extracting the most probable facial regions by minimum error thresholding followed by ingenious adaptive methods to leverage the horizontal and vertical projections of the segmented thermal image. Additionally, the model incorporates our domain knowledge of exploiting temperature difference between strategic locations of the face. To our best knowledge, this is the pioneering work on detecting faces in thermal facial images comprising both patients and normal subjects. Previous works on face detection have not specifically targeted automated medical diagnosis; face bounding box returned by those algorithms are thus loose and not apt for further medical automation. Our algorithm significantly outperforms contemporary face detection algorithms in terms of commonly used metrics for evaluating face detection accuracy. Since our method has been tested on challenging dataset consisting of both patients and normal subjects of diverse age groups, it can be seamlessly adapted in any DITI guided facial healthcare or biometric applications.
机译：自动面检测是计算机视觉辅助面部医学诊断和生物识别和生物识别的关键步骤。本文提出了一种自动的主题自适应框架，用于在我们的数据库中的长红外光谱中精确脸部检测，用于口腔癌症检测，包括不同年龄组的恶性，癌前和正常科目。以前使用数字红外线热成像（DITI）对口腔癌检测的作用显示，患者和正常受试者在其面部热分布中有显着差异。因此，建立完全自适应框架是一个具有挑战性的任务，以从这种主题特定的模态真实地本地化面部。我们的模型包括首先通过最小的误差阈值阈值提取最可能的面部区域，然后通过巧妙的自适应方法利用分段热图像的水平和垂直投影来利用。此外，该模型包括我们的域名知识对脸部战略位置之间的利用温差。为了我们的最佳知识，这是在包含患者和正常受试者的热面部图像中检测面部的开创性工作。以前的工作面部检测没有专门针对自动化医学诊断;因此，这些算法返回的面部边界盒子因此松动，不适合进一步的医疗自动化。我们的算法在用于评估面部检测精度的常用度量方面显着优于当代面部检测算法。由于我们的方法已经过挑战数据集，这些数据集由两种年龄组的患者和正常主体组成，因此它可以在任何DITI引导的面部医疗保健或生物识别应用中无缝地调整。

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