Graduate School of Science and Engineering, Aoyama Gakuin University, Kanagawa 252-5258, Japan.
Int J Environ Res Public Health. 2021 Feb 12;18(4):1776. doi: 10.3390/ijerph18041776.
The evaluation of physiological and psychological states using thermal infrared images is based on the skin temperature of specific regions of interest, such as the nose, mouth, and cheeks. To extract the skin temperature of the region of interest, face alignment in thermal infrared images is necessary. To date, the Active Appearance Model (AAM) has been used for face alignment in thermal infrared images. However, computation using this method is costly, and it has a low real-time performance. Conversely, face alignment of visible images using Cascaded Shape Regression (CSR) has been reported to have high real-time performance. However, no studies have been reported on face alignment in thermal infrared images using CSR. Therefore, the objective of this study was to verify the speed and robustness of face alignment in thermal infrared images using CSR. The results suggest that face alignment using CSR is more robust and computationally faster than AAM.
利用热红外图像评估生理和心理状态是基于特定感兴趣区域（如鼻子、嘴和脸颊）的皮肤温度。为了提取感兴趣区域的皮肤温度，需要在热红外图像中进行面部对齐。迄今为止，主动外观模型（AAM）已用于热红外图像中的面部对齐。然而，这种方法的计算成本很高，实时性能较低。相反，使用级联形状回归（CSR）的可见光图像的面部对齐已被报道具有较高的实时性能。然而，目前还没有关于使用 CSR 进行热红外图像中的面部对齐的研究报告。因此，本研究的目的是验证使用 CSR 进行热红外图像中的面部对齐的速度和鲁棒性。结果表明，使用 CSR 的面部对齐比 AAM 更稳健且计算速度更快。
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