CFP last date
16 December 2024
Call for Paper
January Edition
IJAIS solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 16 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Ordered Frequent Itemsets Matrix Based On Fp-Tree Structure And Apriori Algorithm

Abdulkader M. Al-Badani Abdualmajed A. Al-Khulaidi
Random Articles
Reseach Article

Comprehensive Study of Facial Action Coding

by Ali Alomari
journal cover thumbnail

International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 11 - Number 9
Year of Publication: 2017
Authors: Ali Alomari
10.5120/ijais2017451643

Ali Alomari . Comprehensive Study of Facial Action Coding. International Journal of Applied Information Systems. 11, 9 ( Jan 2017), 18-21. DOI=10.5120/ijais2017451643

@article{ 10.5120/ijais2017451643,
author = { Ali Alomari },
title = { Comprehensive Study of Facial Action Coding },
journal = { International Journal of Applied Information Systems },
issue_date = { Jan 2017 },
volume = { 11 },
number = { 9 },
month = { Jan },
year = { 2017 },
issn = { 2249-0868 },
pages = { 18-21 },
numpages = {9},
url = { https://www.ijais.org/archives/volume11/number9/964-2017451643/ },
doi = { 10.5120/ijais2017451643 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:04:42.403993+05:30
%A Ali Alomari
%T Comprehensive Study of Facial Action Coding
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 11
%N 9
%P 18-21
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Facial Action Coding (FAC) is expected to apply the whole world. The solicitations are not controlled to media communications. FAC is a testing difficulty in computer vision and remains alarm security case, and a novel strategy for program FAC is guided to bargain nearby the issue. The principle trial here, head-case and non-unbending outward appearance conformities because of alterations invited by the brutal face decoupling to present as they are coupled to the non-direct pictures. One more trial is the way to proficiently request to empower affiliation to contemplate expression (or different facial components) is to misuse the data. Facial Expression(FE) picture succession is fleeting territory spatial region information directly rise, however moreover the advance is not known. Information considering the increase of appearance together nearby the photo participation information can more improve the presentation of acknowledgment. However, the active information supplied is practical, there how to capture this information dependably and powerfully concerning facing challenges. For instance, an FE arrangement typically, one or additional of the principle times of development and counterbalance beat. Temporary information and preparing with a specific end goal to capture and question transient groupings of identical information, to make the correspondence in the midst of different worldly periods request to be built up. Press can be encoded. In this work, another dynamic FE, hereditary and neural organize based way utilizing the half, and half method are made.

References
  1. P. Ekman and W. Friesen. Unmasking the Face: A guide to recognizing emotions from facial expressions. Consulting Psychologists Press, Palo Alto, CA, 1975.
  2. K.-K. Sung and T. Poggio. Example-based learning for view-based human face detection. Technical Report A.I. Memo 1521, CBCL Paper 112, MIT, Dec. 1994.
  3. H. Rowley, S. Baluja, and T. Kanade. Human face detection in visual scenes. Technical Report CMU-CS-95-158R, School of Computer Science, Carnegie Mellon University, Nov. 1995.
  4. R. Chellappa, C. Wilson, and S. Sirohey. Human and machine recognition of faces: A survey. Proceedings of the IEEE, 83(5):705–740, May 1995.
  5. M. Suma, N. Sugie, and K. Fujimora. A preliminary note on pattern recognition of human emotional expression. In Proceedings of the 4th International Joint Conference on Pattern Recognition pages 408–410, 1978.
  6. K. Mase. Recognition of facial expression from optical flow. IEICE Transactions E, 74(10):3473– 3483, 1991.
  7. Y. Yacoob and L. Davis. Recognizing facial expressions by spatiotemporal analysis. In Proceedings of the International Conference on Pattern Recognition, volume 1, pages 747–749, Jerusalem, Israel, Oct. 1994. Computer Society Press.
  8. M. Bartlett, P. Viola, T.Sejnowski, L. Larsen, J. Hager, and P. Ekman. Classifying facial action. In D. Touretzky, M. Mozer, and M. Hasselmo, editors, Advances in Neural Information Processing Systems 8. MIT Press, Cambridge, MA, 1996.
  9. I. Essa and A. Pentland. Coding, analysis, interpretation, and recognition of facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):757–763, July 1997.
  10. M. Turk and A. Pentland. Eigenfaces for recognition. J. of Cognitive Neuroscience, 3(1):71–86, Mar. 1991.
  11. C. Padgett and G. Cottrell. Identifying emotion in static images. In Proceedings of the 2nd Joint Symposium on Neural Computation, volume 5, pages 91–101, La Jolla, CA, 1997.
  12. G. Cottrell and J. Metcalfe. Face, gender and emotion recognition using holons. In D. Touretzky, editor, Advances in Neural Information Processing Systems 3, pages 564–571. Morgan and Kaufman, San Mateo, 1991.
  13. A. Rahardja, A. Sowmya, and W. Wilson. A neural network approach to component versus holistic recognition of facial expressions in images. In Intelligent Robots and Computer Vision X: Algorithms and Techniques, Volume 1607 of SPIE Proc., pages 62–70, 1991.
  14. A. Lanitis, C. Taylor, and T. Cootes. Automatic interpretation and coding of face images using flexible models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):743–756, July 1997.
  15. Ekman, P., Huang, T.S., Sejnowski, T.J., Hager, J.C. (eds.): NSF Understanding the Face. A Human Face eStore, Salt Lake City, USA, (see Library) (1992).
  16. M. Turk and A. Pentland, Face Recognition Using Eigenfaces, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 586-591, 1991.
  17. M.S. Bartlett, J.R. Movellan and T.J. Sejnowski, Face recognition by independent component analysis , IEEE Transaction on Neural Networks, Vol 13,pp. 14501464,2002.
  18. Z. Jahan, M.Y. Javed and Q. Usman, Low-resolution single Neural Network based Face Recognition, Proceedings of the Fourth International Conference on Computer Vision, Image and Signal Processing, Vol. 22, pp. 189193, 2007.
  19. TimoAhonen, AbdenourHadid, and MattiPietikainen Face Description with Local Binary Patterns: Application to Face Recognition in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 12, December 2006.
  20. S. Bashyal and G.K. Venayagamoorthy, Recognition of Facial expres-sions using Gabor Wavelets and Learning Vector Quantization, Engineer-ing Applications of Artificial Intelligence, Vol. 21, pp. 10561064, 2008.
  21. De Stefano, C., Sansone, C. and Vento M., Comparing generalization and recognition capability of Learning Vector Quantization and Multilayer Perceptron Architectures, Proceedings of the 9th Scandinavian Confer-ence on Image Analysis, pp. 11231130, June 1995.
  22. Divya Bhatnagar, Drashti Pathak, Garima Saini, Amit Kumar Gautam and Vijai Singh. Article: A Review: Analysis of Facial Micro-Expressions. IJCA Proceedings on Recent Trends in Future Prospective in Engineering and Management Technology RTFEM 2016(2):1-3, July 2016.
  23. Anisha Shadi and Anil Khandelwal. Multi-scale and Multi-orientation Face Recognition using Voting based Extreme Learning Machine. International Journal of Computer Applications 152(1):52-56, October 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Facial Action Coding Facial Expression Recognition (FER) and Testing Faces

Powered by PhDFocusTM