Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(380.31KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
SVM Based Recognition of Facial Expressions Used In Indian Sign Language
Daleesha M Viswanathan, Sumam Mary Idicula
Pages - 32 - 40     |    Revised - 31-1-2015     |    Published - 28-2-2015
Volume - 9   Issue - 1    |    Publication Date - January / February 2015  Table of Contents
MORE INFORMATION
KEYWORDS
Indian Sign Language, Facial Expression, Gabor Wavelet, Euclidian Distance, SVM.
ABSTRACT
In sign language systems, facial expressions are an intrinsic component that usually accompanies hand gestures. The facial expressions would modify or change the meaning of hand gesture into a statement, a question or improve the meaning and understanding of hand gestures. The scientific literature available in Indian Sign Language (ISL) on facial expression recognition is scanty. Contrary to American Sign Language (ASL), head movements are less conspicuous in ISL and the answers to questions such as yes or no are signed by hand. Purpose of this paper is to present our work in recognizing facial expression changes in isolated ISL sentences. Facial gesture pattern results in the change of skin textures by forming wrinkles and furrows. Gabor wavelet method is well-known for capturing subtle textural changes on surfaces. Therefore, a unique approach was developed to model facial expression changes with Gabor wavelet parameters that were chosen from partitioned face areas. These parameters were incorporated with Euclidian distance measure. Multi class SVM classifier was used in this recognition system to identify facial expressions in an isolated facial expression sequences in ISL. An accuracy of 92.12 % was achieved by our proposed system.
CITED BY (0)  
1 Directory of Open Access Journals (DOAJ)
2 Google Scholar
3 CiteSeerX
4 refSeek
5 Scribd
6 slideshare
7 PdfSR
1 N. Tan Dat and S. Ranganath, "Facial expressions in American sign language: Tracking and recognition", Pattern Recognition, vol 45, pp. 1877-1891, 2012.
2 M.W. Morgan,"Topology of Indian Sign Language verbs from a comparative perspective", An annual review of South Asian languages and linguistics,vol 222, pp. 103-131, 2009.
3 B. Bridges and M. Metzger," Deaf tend your", Calliope press. Silver Spring, 1996, pp 27- 38 .
4 Y.L. Tiam, T. Kanade and J.F. Cohn, " Facial expression analysis", Springer New York, 2005, pp. 247-275.
5 C. Shan, S. Gong and P.W. McOwan," Facial expression recognition based on local binary patterns: A comprehensive study", Image and Vision Computing, vol. 27, pp. 803-816, 2009.
6 D. Das,"Human's facil parts extraction to recognize facial expression", International journal on information theory, vol. 3, pp. 65-72. 2014.
7 R.A, Patil, V. Sahula and A.S. Mandal,"Facial expression recognition in image sequences using active shape model and SVM", Uksim Fifth European Modelling Symposium on Computer Modelling and Simulation,, 2011, pp. 168-173.
8 M. Turk and A.P. Pentlanad,"Face recognition using Eigen face", IEEE Conference on computer vision and pattern recognition, 1991.
9 P.N. Belhumeur, J.P. Hespanha and D.J. Kriegman,"Eigen faces versus Fisher faces: Recognition using class specific linear projection", IEEE Transactions on pattern analysis and machine intelligence, pp. 711-720, 1997.
10 Bartlett, M.S., J.R. Movellan and T.J. Sejnowski," Face recognition by independent component analysis", IEEE Transactions on Neural Networks, pp. 1450-1464, 2002.
11 M.J. Lyions, J. Budynek and S. Akametsu,"Automatic classification of single facial image", IEEE Transactions on pattern analysis and machine intelligence, pp. 1357-1362, 1999.
12 R. Londhe and V. Pawar,"Facial expression recognition based on affine moment invariants", International Journal of Computer Science, vol. 9, pp. 382-392, 2012.
13 Y. Zhan and G. Zhou,"Facial expression recognition based on hybrid features and fusing discrete HMMs", Virtual reality-Book chapter, Springer 2007, pp. 408-417.
14 J.M. Gold,"Efficiency of dynamic and static facial expression",Journal of vision, pp. 1-12, 2013.
15 P. Viola and M. Jones,"Rapid object detection using a bossted cascade of simple features", Computer visison and pattern recognition, 2001.
16 C. Liu and H. Wechsler," Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition", IEEE Transactions on image processing, 2002, pp. 467-476.
17 V. Struc and N. Vavesic,"From Gabor magnitude to Gabor face features: Tackling the problem of face recognition under severe illumination changes",Face recognition-Chaper 12. www.intechopen.com, 2011, p. 215-238.
18 V. Vapnik,"The nature of statistical learning theory",Springer, 2000.
19 S. Sindhumol, K. Anil and B. Kannan,"Abnormality detection from Multispectoral brain MRI using multiresolution independent component analysis", International journal of signal processing, image processing and pattern recognition, pp. 177-190, 2013.
20 I. Ari and L. Akarun,"Facial Feature Tracking and Expression Recognition for Sign Language",2009 IEEE 17th Signal processing and communications applications conference, 2009, pp. 479-482.
21 S.S.K, Nair, N.V.S. Reddy and K.S. Hareesha," Explointing heterogenous features to improve insillico prediction of peptide status of amyloidogenic or non-amyloidogenic", BMC bioinformatics, pp. 2-9. 2011.
Mr. Daleesha M Viswanathan
Cochin University Of Science and Technology - India
daleesha_mv@rediffmail.com
Miss Sumam Mary Idicula
Department of Computer Science Cochin University of Science and Technology Kochi-22, Kerala, India - India