Can Electromyography Alone Reveal Facial Action Units? A Pilot EMG-Based Action Unit Recognition Study with Real-Time Validation
by , , , ,
Abstract:
Facial expressions play a crucial role in non-verbal and visual communication, often observed in everyday life. The facial action coding system (FACS) is a prominent framework for categorizing facial expressions as action units (AUs), which reflect the activity of facial muscles. This paper presents a proof-of-concept study for upper face action unit recognition (AUR) using electromyography (EMG) data. The study recorded facial EMG data of a subject over four sessions, who imitated facial expressions corresponding to four different AUs. The subject-dependent models that were trained achieved high accuracy in near-real time and were able to classify AUs not directly underneath the recording sites.
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PDF URL: https://www.csl.uni-bremen.de/cms/images/documents/publications/VeldandaLiuKoschkeSchultzKüster_BIODEVICES2024.pdf
Reference:
Can Electromyography Alone Reveal Facial Action Units? A Pilot EMG-Based Action Unit Recognition Study with Real-Time Validation (Abhinav Veldanda, Hui Liu, Rainer Koschke, Tanja Schultz, Dennis Küster), In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024) - BIODEVICES, SCITEPRESS - Science and Technology Publications, 2024.
Bibtex Entry:
@inproceedings{veldanda2024emg_aur,
  title = {Can Electromyography Alone Reveal Facial Action Units? {A} Pilot {EMG}-Based Action Unit Recognition Study with Real-Time Validation},
  author = {Veldanda, Abhinav and Liu, Hui and Koschke, Rainer and Schultz, Tanja and Küster, Dennis},
  booktitle ={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024) - BIODEVICES},
  pages = {142--151},
  organization = {INSTICC},
  publisher = {SCITEPRESS - Science and Technology Publications},
  year = {2024},
  isbn = {978-989-758-688-0},
  issn = {2184-4305},
  doi = {10.5220/0012399100003657},
  url = {https://www.csl.uni-bremen.de/cms/images/documents/publications/VeldandaLiuKoschkeSchultzKüster_BIODEVICES2024.pdf},
  abstract = {Facial expressions play a crucial role in non-verbal and visual communication, often observed in everyday life. The facial action coding system (FACS) is a prominent framework for categorizing facial expressions as action units (AUs), which reflect the activity of facial muscles. This paper presents a proof-of-concept study for upper face action unit recognition (AUR) using electromyography (EMG) data. The study recorded facial EMG data of a subject over four sessions, who imitated facial expressions corresponding to four different AUs. The subject-dependent models that were trained achieved high accuracy in near-real time and were able to classify AUs not directly underneath the recording sites.}
}