A facial action unit detection algorithm combining expert prior knowledge and motion correlation
Journal Title: Journal of Air Force Medical University - Year 2023, Vol 44, Issue 10
Abstract
Objective Facial action unit (AU) provides support for tasks such as expression recognition, micro-expression analysis, stress detection, fatigue detection, and lie detection. The purpose of this paper is to develop a stable and accurate facial AU detection algorithm based on integrating expert prior knowledge of face regions and AU relationships. Methods First, the key points of faces were used to divide each region of face images according to the expert prior knowledge of face regions, and the high-dimensional features of face images in each region were extracted with convolutional neural network. Then, the encoder in Transformer model was used to model the AU relationships and encode the high-dimensional features of faces in each region. Finally, an AU classifier was constructed using the fully connected layer. To tackle the problems of category imbalance and positive-negative sample imbalance in the dataset, a weighted differentiable F1 loss was incorporated into the loss function to constrain the AU detection model. Results In this paper, 3-fold cross-validation experiments were conducted on BP4D and DISFA datasets, and their average F1 scores for AU detection reached 63. 8% and 61. 8% , respectively. Conclusion The experimental results show that compared with the existing mainstream AU detection algorithms, the proposed algorithm has higher stability and detection accuracy on different datasets. The algorithm can accurately detect the facial AU, thus providing effective support for other tasks such as facial expression analysis.
Authors and Affiliations
LI Kui, MO Jianhua, WANG Jiajun
Keywords
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