Dataset classification: An efficient feature extraction approach for grammatical facial expression recognition

In this paper, an efficient features extraction using validated statistical approaches is proposed, along with a robust Grammatical Facial Expressions (GFEs) classifier in facial expression recognition systems. Accordingly, a new dataset was collected from 70 participants (33 males and 37 females) ranging in age from 18 to 46. The total number of video clips collected was 765.The features extracted in this study consist of 17 features associated with three categories of non-manual features: facial expression, head movement, and eye-gaze. Automatic recognition of nine classes of grammatical facial expressions in two languages (Arabic and Persian) is performed using a linear Support Vector Machine (SVM) classifier.The proposed system was also validated by testing it on the American Sign Language (ASL) dataset. In comparison to previous works on the ASL dataset, the results showed a higher accuracy rate of 95%.

Automated summary

In this paper, an efficient method of features extraction using validated statistical approaches is proposed, along with a robust classifier for grammatical facial expressions (GFEs) in facial expression recognition systems. A new dataset is collected from 70 participants, and the features extracted include facial expression, head movement, and eye-gaze. The proposed system achieves a higher accuracy rate of 95% when tested on the American Sign Language (ASL) dataset, compared to previous works.