Face Alignment in Thermal Infrared Images Using Cascaded Shape Regression

Kent Nagumo, Tomohiro Kobayashi, Kosuke Oiwa and Akio Nozawa
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Kent Nagumo: Graduate School of Science and Engineering, Aoyama Gakuin University, Kanagawa 252-5258, Japan
Tomohiro Kobayashi: Graduate School of Science and Engineering, Aoyama Gakuin University, Kanagawa 252-5258, Japan
Kosuke Oiwa: Graduate School of Science and Engineering, Aoyama Gakuin University, Kanagawa 252-5258, Japan
Akio Nozawa: Graduate School of Science and Engineering, Aoyama Gakuin University, Kanagawa 252-5258, Japan

IJERPH, 2021, vol. 18, issue 4, 1-10

Abstract: 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.

Keywords: face alignment; thermal infrared image; facial thermal image; cascaded shape regression; real-time measurement; remote measurement (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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