TY - GEN
T1 - Facial emotional expressions recognition based on active shape model and radial basis function network
AU - Setyati, Endang
AU - Suprapto, Yoyon K.
AU - Purnomo, Mauridhi Hery
PY - 2012
Y1 - 2012
N2 - Facial emotional expressions recognition (FEER) is important research fields to study how human beings reflect to environments in affective computing. With the rapid development of multimedia technology especially image processing, facial emotional expressions recognition researchers have achieved many useful result. If we want to recognize the human's emotion via the facial image, we need to extract features of the facial image. Active Shape Model (ASM) is one of the most popular methods for facial feature extraction. The accuracy of ASM depends on several factors, such as brightness, image sharpness, and noise. To get better result, the ASM is combined with Gaussian Pyramid. In this paper we propose a facial emotion expressions recognizing method based on ASM and Radial Basis Function Network (RBFN). Firstly, facial feature should be extracted to get emotional information from the region, but this paper use ASM method by the reconstructed facial shape. Second stage is to classify the facial emotion expressions from the emotional information. Finally get the model which is matched with the facial feature outline after several iterations and use them to recognize the facial emotional expressions by using RBFN. The experimental result from RBFN classifiers show a recognition accuracy of 90.73% for facial emotional expressions using the proposed method.
AB - Facial emotional expressions recognition (FEER) is important research fields to study how human beings reflect to environments in affective computing. With the rapid development of multimedia technology especially image processing, facial emotional expressions recognition researchers have achieved many useful result. If we want to recognize the human's emotion via the facial image, we need to extract features of the facial image. Active Shape Model (ASM) is one of the most popular methods for facial feature extraction. The accuracy of ASM depends on several factors, such as brightness, image sharpness, and noise. To get better result, the ASM is combined with Gaussian Pyramid. In this paper we propose a facial emotion expressions recognizing method based on ASM and Radial Basis Function Network (RBFN). Firstly, facial feature should be extracted to get emotional information from the region, but this paper use ASM method by the reconstructed facial shape. Second stage is to classify the facial emotion expressions from the emotional information. Finally get the model which is matched with the facial feature outline after several iterations and use them to recognize the facial emotional expressions by using RBFN. The experimental result from RBFN classifiers show a recognition accuracy of 90.73% for facial emotional expressions using the proposed method.
KW - Active Shape Model
KW - Facial emotional expression recognition
KW - Facial feature extraction
KW - Gaussian Pyramid
KW - Radial Basis Function Network
UR - http://www.scopus.com/inward/record.url?scp=84866727020&partnerID=8YFLogxK
U2 - 10.1109/CIMSA.2012.6269607
DO - 10.1109/CIMSA.2012.6269607
M3 - Conference contribution
AN - SCOPUS:84866727020
SN - 9781457717772
T3 - CIMSA 2012 - 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Proceedings
SP - 41
EP - 46
BT - CIMSA 2012 - 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Proceedings
T2 - 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2012
Y2 - 2 July 2012 through 4 July 2012
ER -