CFP last date
15 March 2024
Reseach Article

Human Facial Expression Recognition using Gabor Filter Bank with Minimum Number of Feature Vectors

by Priya Sisodia, Akilesh Verma, Sachin Kansal
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Number 9
Year of Publication: 2013
Authors: Priya Sisodia, Akilesh Verma, Sachin Kansal
10.5120/ijais13-450971

Priya Sisodia, Akilesh Verma, Sachin Kansal . Human Facial Expression Recognition using Gabor Filter Bank with Minimum Number of Feature Vectors. International Journal of Applied Information Systems. 5, 9 ( July 2013), 9-13. DOI=10.5120/ijais13-450971

@article{ 10.5120/ijais13-450971,
author = { Priya Sisodia, Akilesh Verma, Sachin Kansal },
title = { Human Facial Expression Recognition using Gabor Filter Bank with Minimum Number of Feature Vectors },
journal = { International Journal of Applied Information Systems },
issue_date = { July 2013 },
volume = { 5 },
number = { 9 },
month = { July },
year = { 2013 },
issn = { 2249-0868 },
pages = { 9-13 },
numpages = {9},
url = { https://www.ijais.org/archives/volume5/number9/508-0971/ },
doi = { 10.5120/ijais13-450971 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T17:59:00.871107+05:30
%A Priya Sisodia
%A Akilesh Verma
%A Sachin Kansal
%T Human Facial Expression Recognition using Gabor Filter Bank with Minimum Number of Feature Vectors
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 5
%N 9
%P 9-13
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Human Facial Expression Recognition is used in many fields such as mood detection and Human Computer Interaction (HCI). Gabor Filters are used to extract features. Gabor has the useful property of robustness against slight object rotation, distortion and variation in illumination. In the present work the effort has been made to provide the modules of for Human facial expression recognition by reducing the number of parameters use to represent Gabor feature the space complexity can reduce. SVM classifier has multi-classes. SVM classifies the expression by comparing it with the trained data.

References
  1. Ekman, P. , & Rosenberg, E. L. ," what the face reveals: Basic and applied studies of spontaneous expression using the Facial Action Coding System (FACS)", New York: Oxford University Press, 1997.
  2. B. Fasel, J. Luettin, "Automatic facial expression analysis: a survey", Pattern Recognition, Vol. 36, pp 259-275, 2003.
  3. Fernando De La Torre & Jeffrey F. Cohn, 'Facial Expression Analysis', Springer, VAH, pp 377-409, 2011.
  4. Shishir Bashyal, Ganesh K. Venayagamoorthy, "Recognition of Facial Expression using Gabor wavelets and learning vector quantization", Elsevier, EAAI 21, pp 1056-1064, 2009.
  5. Ying-Li Tian, Takeo Kanade, and Jeffrey F. Cohn, "Facial expression Analysis", Springer Link, 2005.
  6. Zhengyou Zhang et al. , "Comparison between Geometry- based and Gabor Wavelets-based facial expressions recognition using Multi-layer perception", IEEE conf. , 1998.
  7. Fei Long et al. , "Learning Spatiotemporal feature by using Independent component analysis with application to facial expression recognition", Elsevier Trans. , Neurocomputing, 126-132, 2012.
  8. Andrew J. Calder, et al. , "A Principal Component analysis of facial expressions", Elsevier Science Ltd Trans. , 2001.
  9. Ceifeng Shan, et al. , "Facial expression recognition based on Local Binary Patterns: A comprehensive Study", Elsevier Science Ltd Trans. , 2009.
  10. Ira Cohen, et al. , "Facial expression recognition from video sequences: Temporal and Static modeling", Elsevier Inc. Trans. , 2003
  11. Jun Wang, Lijun Yin, "Static topographic modeling for facial expression recognition and analysis", Elsevier Science Ltd. Trans. , 2007.
  12. M. Dahmane and J. Meunier, "Emotion recognition using dynamic grid based HOG feature", IEEE In. conf. ,2011.
  13. Praseeda Lekshmi V. , et al. , "Analysis of Facial Expressions from Video Images using PCA", IEEE Conf. , 2008.
  14. S. Luiz OLiveira, et al. , "2D Principal Component Analysis for face and facial expression recognition", IEEE Trans. , 2011.
  15. Spiros V. Loannou, et al. , "Emotion recognition through facial expression analysis based on a Neurofuzzy network", Elsevier Science Ltd. Trans. , 2005.
  16. Wenfei Gu, et al. , "Facial expression recognition using radial encoding of local Gabor Feature and Classifier Synthesis", Elsevier Ltd Trans. , 2012.
  17. Shishir Bashyal et al. , "Recognition of facial expression using Gabor wavelet and learning vector quantization" ,Elsevier Trans. ,Engineering Application of Artificial Intelligence 21, 1056-1064, 2008.
  18. http://www. kasrl. org/jaffe_info. html.
  19. Wei-lun Chao, "Gabor wavelet transform and its application", R98942073.
  20. Ligang Zhang, "Facial Expression Recognition Using Facial Movement Features", IEEE Trans. , Affective Computing, Vol. 2, issue 4, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Image Acquisition Preprocessing Feature Extraction Classification