What Happens in Face During a Facial Expression? Using Data Mining Techniques to Analyze Facial Expression Motion Vectors
Article
Roshanzamir, Mohamad, Jafari, Mahboobeh, Alizadehsani, Roohallah, Roshanzamir, Mahdi, Shoeibi, Afshin, Gorriz, Juan M., Khosravi, Abbas, Nahavandi, Saeid and Acharya, U. Rajendra. 2024. "What Happens in Face During a Facial Expression? Using Data Mining Techniques to Analyze Facial Expression Motion Vectors." Information Systems Frontiers: a journal of research and innovation. https://doi.org/10.1007/s10796-023-10466-7
| Article Title | What Happens in Face During a Facial Expression? Using Data Mining Techniques to Analyze Facial Expression Motion Vectors |
|---|---|
| ERA Journal ID | 17912 |
| Article Category | Article |
| Authors | Roshanzamir, Mohamad, Jafari, Mahboobeh, Alizadehsani, Roohallah, Roshanzamir, Mahdi, Shoeibi, Afshin, Gorriz, Juan M., Khosravi, Abbas, Nahavandi, Saeid and Acharya, U. Rajendra |
| Journal Title | Information Systems Frontiers: a journal of research and innovation |
| Number of Pages | 19 |
| Year | 2024 |
| Publisher | Springer |
| Place of Publication | United States |
| ISSN | 1387-3326 |
| 1572-9419 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s10796-023-10466-7 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s10796-023-10466-7 |
| Abstract | Automatic facial expression recognition is a big challenge in human–computer interaction. Analyzing the changes in the face during a facial expression can be used for this purpose. In this paper, these changes are extracted as a number of motion vectors. These motion vectors are extracted using an optical flow algorithm. Then, they are used to analyze facial expressions by some of the data mining algorithms. This analysis has not only determined what changes occur in the face during facial expression but has also been used to recognize facial expressions. Cohen-Kanade facial expression dataset was used in this research. Based on our findings, the vertical lengths of motion vectors created in the lower part of the face have the greatest impact on the classification of facial expressions. Among the investigated classification algorithms, deep learning, support vector machine, and C5.0 had better performance, yielding an accuracy of 95.3%, 92.8%, and 90.2% respectively. |
| Keywords | Automatic Facial Expression Recognition; Facial Expression Analysis; Optical Flow; Data Mining; Deep Learning; Supported Vector Machine; C5.0 |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | The accessible file is the accepted version of the paper. Please refer to the URL for the published version. |
| Byline Affiliations | Fasa University, Iran |
| University of Granada, Spain | |
| Deakin University | |
| University of Tabriz, Iran | |
| University of Cambridge, United Kingdom | |
| Swinburne University of Technology | |
| Harvard University, United States | |
| School of Mathematics, Physics and Computing |
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