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Article

Viewpoint Robustness of Automated Facial Action Unit Detection Systems

1
Psychological Process Team, Guardian Robot Project, RIKEN, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
2
Faculty of Arts, Kyoto University of the Arts, 2-116 Uryuyama Kitashirakawa, Sakyo, Kyoto 606-8271, Japan
*
Authors to whom correspondence should be addressed.
Academic Editor: Monica Perusquia Hernandez
Appl. Sci. 2021, 11(23), 11171; https://doi.org/10.3390/app112311171
Received: 12 October 2021 / Revised: 5 November 2021 / Accepted: 21 November 2021 / Published: 25 November 2021
(This article belongs to the Special Issue Research on Facial Expression Recognition)
Automatic facial action detection is important, but no previous studies have evaluated pre-trained models on the accuracy of facial action detection as the angle of the face changes from frontal to profile. Using static facial images obtained at various angles (0°, 15°, 30°, and 45°), we investigated the performance of three automated facial action detection systems (FaceReader, OpenFace, and Py-feat). The overall performance was best for OpenFace, followed by FaceReader and Py-Feat. The performance of FaceReader significantly decreased at 45° compared to that at other angles, while the performance of Py-Feat did not differ among the four angles. The performance of OpenFace decreased as the target face turned sideways. Prediction accuracy and robustness to angle changes varied with the target facial components and action detection system. View Full-Text
Keywords: action unit; angle; automatic facial detection action unit; angle; automatic facial detection
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MDPI and ACS Style

Namba, S.; Sato, W.; Yoshikawa, S. Viewpoint Robustness of Automated Facial Action Unit Detection Systems. Appl. Sci. 2021, 11, 11171. https://doi.org/10.3390/app112311171

AMA Style

Namba S, Sato W, Yoshikawa S. Viewpoint Robustness of Automated Facial Action Unit Detection Systems. Applied Sciences. 2021; 11(23):11171. https://doi.org/10.3390/app112311171

Chicago/Turabian Style

Namba, Shushi, Wataru Sato, and Sakiko Yoshikawa. 2021. "Viewpoint Robustness of Automated Facial Action Unit Detection Systems" Applied Sciences 11, no. 23: 11171. https://doi.org/10.3390/app112311171

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