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Robust Facial Expression Recognition for MuCI A Comprehensive Neuromuscular Signal Analysis - Preprint.pdf
IEEE Transactions on Affective Computing (2016)
  • Mahyar Hamedi
  • Sh-Hussain Salleh
  • Chee-Ming Ting, PhD
  • Alias M. Noor
Abstract
This paper presents a comprehensive study on the analysis of neuromuscular signal activities to recognize eleven facial expressions for Muscle Computer Interfacing applications. A robust denoising protocol comprised of Wavelet transform and Kalman filtering is proposed to enhance the electromyogram (EMG) signal-to-noise ratio and improve classification performance. The effectiveness of eight different time-domain facial EMG features on system performance is examined and compared in order to identify the most discriminative one. Fourteen pattern recognition-based algorithms are employed to classify the extracted features. These classifiers are evaluated in terms of classification accuracy and processing time. Finally, the best methods that obtain almost identical system performance are compared through the Normalized Mutual Information (NMI) criterion and a repeated measure analysis of variance (ANOVA) for a statistical significant test.To clarify the impact of signal denoising, all considered EMG features and classifiers are assessed with and without this stage. Results show that: (1) the proposed denosing step significantly improves the system performance; (2) Root Mean Square is the most discriminative facial EMG feature; (3) discriminant analysis when the parameters are estimated by the Maximum Likelihood algorithm achieves the highest classification accuracy and NMI; however, ANOVA reveals no significant difference among the best methods with almost similar performance.
Keywords
  • Facial Neuromuscular Activity,
  • Muscle Computer Interaction (MuCI),
  • EMG Denoising,
  • Feature Extraction,
  • Classification,
  • Facial Expression Recognition.
Disciplines
Publication Date
May 19, 2016
DOI
10.1109/TAFFC.2016.2569098
Publisher Statement
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TAFFC.2016.2569098, IEEE Transactions on Affective Computing 
1949-3045 (c) 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more
information.
Citation Information
Mahyar Hamedi, Sh-Hussain Salleh, Chee-Ming Ting and Alias M. Noor. "Robust Facial Expression Recognition for MuCI A Comprehensive Neuromuscular Signal Analysis - Preprint.pdf" IEEE Transactions on Affective Computing (2016) ISSN: 1949-3045
Available at: http://works.bepress.com/chee-ming_ting/17/