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Article

FACS-Based Graph Features for Real-Time Micro-Expression Recognition

1
Faculty of Engineering, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Selangor, Malaysia
2
School of Information Technology, Monash University Malaysia, Subang Jaya 47500, Selangor, Malaysia
*
Author to whom correspondence should be addressed.
Received: 2 September 2020 / Revised: 17 November 2020 / Accepted: 27 November 2020 / Published: 30 November 2020
(This article belongs to the Special Issue Imaging Studies for Face and Gesture Analysis)
Several studies on micro-expression recognition have contributed mainly to accuracy improvement. However, the computational complexity receives lesser attention comparatively and therefore increases the cost of micro-expression recognition for real-time application. In addition, majority of the existing approaches required at least two frames (i.e., onset and apex frames) to compute features of every sample. This paper puts forward new facial graph features based on 68-point landmarks using Facial Action Coding System (FACS). The proposed feature extraction technique (FACS-based graph features) utilizes facial landmark points to compute graph for different Action Units (AUs), where the measured distance and gradient of every segment within an AU graph is presented as feature. Moreover, the proposed technique processes ME recognition based on single input frame sample. Results indicate that the proposed FACS-baed graph features achieve up to 87.33% of recognition accuracy with F1-score of 0.87 using leave one subject out cross-validation on SAMM datasets. Besides, the proposed technique computes features at the speed of 2 ms per sample on Xeon Processor E5-2650 machine. View Full-Text
Keywords: facial expression; micro-expression; emotion recognition; real-time classification; feature extraction facial expression; micro-expression; emotion recognition; real-time classification; feature extraction
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MDPI and ACS Style

Buhari, A.M.; Ooi, C.-P.; Baskaran, V.M.; Phan, R.C.W.; Wong, K.; Tan, W.-H. FACS-Based Graph Features for Real-Time Micro-Expression Recognition. J. Imaging 2020, 6, 130. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6120130

AMA Style

Buhari AM, Ooi C-P, Baskaran VM, Phan RCW, Wong K, Tan W-H. FACS-Based Graph Features for Real-Time Micro-Expression Recognition. Journal of Imaging. 2020; 6(12):130. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6120130

Chicago/Turabian Style

Buhari, Adamu M.; Ooi, Chee-Pun; Baskaran, Vishnu M.; Phan, Raphaël C.W.; Wong, KokSheik; Tan, Wooi-Haw. 2020. "FACS-Based Graph Features for Real-Time Micro-Expression Recognition" J. Imaging 6, no. 12: 130. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6120130

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