Department of Electronic and Computer Engineering, Universidad de Córdoba, Edificio Leonardo da Vinci, Campus de Rabanales, 14071 Córdoba, Spain.
Department of Chemical, Physics and Applied Thermodynamics, Universidad de Córdoba, Edificio Leonardo da Vinci, Campus de Rabanales, 14071 Córdoba, Spain.
Sensors (Basel). 2019 Sep 22;19(19):4096. doi: 10.3390/s19194096.
The temperature of the forehead is known to be highly correlated with the internal body temperature. This area is widely used in thermal comfort systems, lie-detection systems, etc. However, there is a lack of tools to achieve the segmentation of the forehead using thermographic images and non-intrusive methods. In fact, this is usually segmented manually. This work proposes a simple and novel method to segment the forehead region and to extract the average temperature from this area solving this lack of non-user interaction tools. Our method is invariant to the position of the face, and other different morphologies even with the presence of external objects. The results provide an accuracy of 90% compared to the manual segmentation using the coefficient of Jaccard as a metric of similitude. Moreover, due to the simplicity of the proposed method, it can work with real-time constraints at 83 frames per second in embedded systems with low computational resources. Finally, a new dataset of thermal face images is presented, which includes some features which are difficult to find in other sets, such as glasses, beards, moustaches, breathing masks, and different neck rotations and flexions.
额头的温度与体内温度高度相关，这一区域广泛应用于热舒适系统、测谎系统等。然而，目前缺乏使用热成像图像和非侵入性方法实现额头分割的工具。实际上，这通常是手动分割的。本工作提出了一种简单而新颖的方法来分割额头区域，并从该区域提取平均温度，从而解决了缺乏非用户交互工具的问题。我们的方法对人脸的位置以及其他不同的形态具有不变性，即使存在外部物体也是如此。与使用 Jaccard 系数作为相似性度量的手动分割相比，我们的方法的准确率达到了 90%。此外，由于所提出方法的简单性，它可以在计算资源有限的嵌入式系统中以每秒 83 帧的实时速度工作。最后，我们提出了一个新的热人脸图像数据集，其中包含一些在其他数据集难以找到的特征，如眼镜、胡须、髭须、呼吸面罩，以及不同的颈部旋转和弯曲。
