Dataset classification

• A robust Grammatical Facial Expressions (GFEs) classifier for facial expression recognition systems is proposed, along with an efficient feature extraction method using validated statistical approaches. • A new set of data was created based on 70 participants (comprising 33 males and 37 females) whose ages ranged from 18 to 46. A total of 765 video clips were gathered, from which 17 features were extracted in this study. • non-manual features: facial expression, head movement, and eye-gaze. For facial expression, facial action coding system (FACS) was used to extract action units (AUs). For head movement and eye-gaze, the constrained local neural field (CLNF) technique was utilized to extract the 3D coordinate (X, Y, and Z), pitch, yaw, roll, X-angle, and Y-angle features. • The proposed system was also validated by testing it on the ASL dataset. Compared to the previous works for the american sign language (ASL) dataset. 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%. Display Omitted