This paper presents a new method to classify facial expressions from frontal pose images. In our method, first Pseudo Zernike Moment Invariant (PZMI) was used to extract features from the global information of the images and then Radial Basis Function (RBF) Network was employed to classify the facial expressions, based on the features which had been extracted by PZMI. Also, the images were preprocessed to enhance their gray-level, which helps to increase the accuracy of classification. For JAFFE facial expression database, the achieved rate of classification in our experiment is 98.33%. This result leads to a conclusion that the proposed method can ensure a high accuracy rate of classification.
Index Terms-Facial expression classification, pseudoZernike moment invariant, RBF neural network.
This paper presents a new approach to classify facial expressions from frontal pose images. In our method, first Pseudo Zernike Moment Invariant (PZMI), Zernike Moment Invariant (ZMI) and Principal Component Analysis (PCA) are used to extract features from the global information of the images and then Radial Basis Function (RBF) Network is employed to classify the facial expressions based on the features which had been already extracted by PZMI, ZMI, PCA. Also, the images are preprocessed to enhance their gray-level, which helps to increase the accuracy of classification. For JAFFE facial expression database, the achieved rate of classification in our experiment is 98.76%. This result shows that the proposed method can ensure a high accuracy rate of classification. Tóm tȃt. Phân ló. p biê ' u lô. ca 'm xúc khuôn mȃ. t là mô. t bài toán quan tro. ng dã và dang du. o. . c nhiề u nhà nghiên cú. u trong và ngoài nu .ó. c quan tâm. Phân ló. p chính xác biê ' u lô. ca 'm xúc khuôn mȃ. t có thê 'ú. ng du. ng trong nhiề u lĩnh vu. . c thu. . c tế khác nhau: tâm lý ho. c, thầ n kinh ho. c.. . Bài báo dề xuất mô. t mô hình phân ló. p biê ' u lô. ca 'm xúc mó. i du. . a trên viê. c kết ho. . p ba kỹ thuâ. t rút trích dȃ. c tru. ng (Pseudo Zernike Moment Invariant (PZMI), Zernike Moment Invariant (ZMI), Principal Component Analysis (PCA)) vó. i ma. ng Radial Basis Function Neural (RBFN)); mô hình này ta. m go. i là mô hình PZMI-ZMI-PCA-RBFN. Viê. c thu. ' nghiê. m dánh giá mô hình so vó. i mô. t số phu. o. ng pháp truyề n thống khác trên co. so. ' dũ. liê. u Jaffe cho thấy tính kha ' thi cu 'a kỹ thuâ. t dề xuất.
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