Surface Electromyography-Based Facial Expression Recognition in Bi-Polar Configuration

Problem statement: Facial expression recognition has been improved recently and it has become a significant issue in diagnostic and medical fields, particularly in the areas of assistive technology and rehabilitation. Apart from their usefulness, there are some problems in their applications like peripheral conditions, lightening, contrast and quality of video and images. Approach: Facial Action Coding System (FACS) and some other methods based on images or videos were applied. This study proposed two methods for recognizing 8 different facial expressions such as natural (rest), happiness in three conditions, anger, rage, gesturing ‘a’ like in apple word and gesturing no by pulling up the eyebrows based on Three-channels in Bi-polar configuration by SEMG. Raw signals were processed in three main steps (filtration, feature extraction and active features selection) sequentially. Processed data was fed into Support Vector Machine and Fuzzy C-Means classifiers for being classified into 8 facial expression groups. Results: 91.8 and 80.4% recognition ratio had been achieved for FCM and SVM respectively. Conclusion: The confirmed enough accuracy and power in this field of study and FCM showed its better ability and performance in comparison with SVM. It’s expected that in near future, new approaches in the frequency bandwidth of each facial gesture will provide better results.