AI Article Synopsis
- Several studies have focused on improving the accuracy of micro-expression recognition but often overlook the computational complexity, which makes real-time application costly.
- The paper introduces a new feature extraction technique using 68-point landmarks guided by the Facial Action Coding System (FACS), allowing for the analysis of micro-expressions from a single input frame instead of requiring multiple frames.
- The results show that this new approach achieves a recognition accuracy of up to 87.33% and processes each sample in just 2 milliseconds on a Xeon Processor E5-2650.
Article Abstract
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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321161 | PMC |
| http://dx.doi.org/10.3390/jimaging6120130 | DOI Listing |
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