Efficient Facial Expression Detection by using the Adaptive-neuro-fuzzy-inference-system and the Bezier Curve.

Q. JAVAID; M. ARIF; D. AWAN; M.A. SHAH

Author(s)

Q. JAVAID Department of CS and SE, International Islamic University, Islamabad, Pakistan

M. ARIF Faculty of Computer Science, Preston University Islamabad, Pakistan

D. AWAN Department of Computer Science, University of Gujrat, Pakistan

M.A. SHAH Department of Computer Science, Islamabad Campus, CIIT, Pakistan

Abstract

Human beings have long been interested in detecting gestures of other human beings, especially facial gestures. The emotional states of humans are difficult to computational glean from their facial gestures; none the less, this recognition task is widely applied in daily life. In human communication the facial expressions recognition plays very important role and it is applied in many real worlds' applications. Present methods for facial expression recognition in static images are not completely utilized and considered features for muscle movements and facial features, which show dynamic, statics, and geometric as well as the emergence characteristics of the facials expressions. In our paper, we develop the system that can recognize the basic emotional states efficiently and in real time based on human facial expressions. This system uses the adaptive neuro-fuzzy-inference system (ANFIS) and the Bezier curves. The system was evaluated based on the human facial images obtained from the databases of Japanese female facial expression and Cohn Kanade. The proposed approach achieves high correct recognitions rate inthe comparisons of the state of the art techniques.

Publication Details

Page(s) 595-600

DOI DOI not available

Published Journal: Sindh University Research Journal, Volume: 48, Issue: 3, Year: 2016

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References

Ortony , A., 1990.The cognitive structure of emotions, 1 1 -224

Pfister , T.,M.Pietikäinen,X.Li,G. Zhao,,PanticM.,L. J. M.Rothkrantz, 2000."Automatic analysis of facial expressions: The state of the art,",Pattern Analysis and Machine Intelligence 22 1424 -1445

Pavlovic , V. I.,R.Sharma,T. S. Huang, 1997."Visual interpretation of hand gestures for humancomputer interaction: A review,",Pattern Analysis and Machine Intelligence 19 677 -695

Shan , C.,S.Gong,P. W. McOwan,S. K.,D.Chauhan,M.Vatsa,R.Singh, 2003."A robust skin color based face detection algorithm,",Tamkang Journal of Science and Engineering 6 227 -234

SmithaK.,A.Vinod, 2013."Hardware efficient FPGA implementation of emotion recognizer for autistic children," in Electronics, Computing and Communication Technologies (CONECCT, 1 4 -

Senechal , T.,V.Rapp,H.Salam,R.Seguier,K.Bailly,L. Prevost,,PartB,Cybernetics, 2012."Facial action recognition combining heterogeneous features via multikernel learning,",Systems, Man, and Cybernetics 42 993 -1005

ŠtrucV.,N.Pavešić, 2009."Gabor-based kernel partial-least-squares discrimination features for face recognition,",Informatica 20 115 -138

Tian , Y. T.,Kanade , and J. F.,Cohn, 2011."Facial expression recognition," Handbook of face recognition, 487 -519

Tsai , C. C.,Y. Z.Chen,C. W.Liao,L. H. N. D. T.Nguyen,T. S. Hai, 2011."A facial expression classification system integrating canny, principal component analysis and artificial neural network,",arXiv preprint arXiv:1111 407 4052 -

Verma A.,L. K.Sharma, 2013."A Comprehensive Survey on Human Facial Expression Detection,",International Journal of Image Processing (IJIP) 7 -

Zhang L.,D.Tjondronegoro, 2011."Facial expression recognition using facial movement features," Affective Computing, IEEE Transactions on, 2 219 -229