RECOGNITION OF FACIAL EMOTIONS STRUCTURES USING EXTREME LEARNING MACHINE ALGORITHM

Journal Title : International Journal of Modern Trends in Engineering and Science

Author’s Name :  Damodharam Seenu  | B Siva kesava reddy | L Balajiunnamed

Volume 03 Issue 05 2016

ISSN no:  2348-3121

Page no: 16-18

Abstract- This paper proposes an approach called Extreme Sparse Learning (ESL), which has the ability to jointly learn a dictionary (set of basis) and a non-linear classification model. The proposed approach combines the discriminative power of Extreme Learning Machine (ELM) with the reconstruction property of sparse representation to enable accurate classification when presented with noisy signals and imperfect data recorded in natural settings. Additionally, this work presents a new local spatio-temporal descriptor that is distinctive and pose-invariant. The proposed framework is able to achieve state-of-the-art recognition accuracy on both acted and spontaneous facial emotion databases.

KeywordsExtreme Sparse Learning, non-linear, spatio-temporal

Reference

  1. Huang and S. Aviyente, “Sparse Representation for Signal Classification,” in Adv. NIPS, 2015.
  2. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and M. Yi, “Robust Face Recognition via Sparse Representation,” IEEE Trans. PAMI, vol. 31, no. 2, pp. 210–227, 2014.
  3. H. Kim, S. U. Jung, and M. J. Chung, “Extension of Cascaded Simple Feature based Face Detection to Facial Expression Recognition,” Pattern Recog. Letters, vol. 29, no. 11, pp. 1621–1631, 2014.
  4. Gu, C. Xiang, Y. V. Venkatesh, D. Huang, and H. Lin, “Facial Expression Recognition using Radial Encoding of Local Gabor Features and Cassifier Synthesis,” Pattern Recog., vol. 45, no. 1, pp. 80–91, 2013.
  5. Wehrle, S. Kaiser, S. Schmidt, and K. R. Scherer, “Studying the Dynamics of Emotional Expression Using Synthesized Facial Muscle Movements,” J. Pers. Soc. Psychol., vol. 78, no. 1, pp. 105–119, 2011.
  6. S. Aleksic and A. K. Katsaggelos, “Automatic Facial Expression Recognition using Facial Animation Parameters and Multistream HMMs,” IEEE Trans. Inf. Forensics Security, vol. 1, no. 1, pp. 3–11, 2010.
  7. Zhang and Q. Ji, “Active and Dynamic Information Fusion for Facial Expression Understanding from Image Sequences,” IEEE Trans. PAMI, vol. 27, no. 5, pp. 699–714, 2010.
  8. Kotsia and I. Pitas, “Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines,” IEEE Trans. IP, vol. 16, no. 1, pp. 172–187, 2009.
  9. Zhao and M. Pietikainen, “Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions,” IEEE Trans. PAMI, vol. 29, no. 6, pp. 915–928, 2012.
  10. Wu, S. Fu, and G. Yang, “Survey of the Facial Expression Recognition Research,” Advances in Brain Inspired Cognitive Systems, vol. 7366, pp. 392–402, 2012.
  11. Rudovic, M. Pantic, and I. Patras, “Coupled Gaussian Processes for Pose-invariant Facial Expression Recognition,” IEEE Trans. PAMI, vol. 35, no. 6, pp. 1357–1369, 2013.
  12. Zheng, H. Tang, Z. Lin, and T. S. Huang, “Emotion Recognition from Arbitrary View Facial Images,” in ECCV, vol. 6316, 2010, pp. 490–503.
  13. Kumano, K. Otsuka, J. Yamato, E. Maeda, and Y. Sato, “Pose-Invariant Facial Expression Recognition Using Variable-Intensity Templates,” Int. J. Computer Vision, vol. 83, no. 2, pp. 178–194, 2009.
  14. Snchez, J. V. Ruiz, A. B. Moreno, A. S. Montemayor, J. Hernndez, and J. J. Pantrigo, “Differential Optical Flow Applied to Automatic Facial Expression Recognition,” Neurocomputing, vol. 74, no. 8, pp. 1272–1282, 2011.
  15. R. Niese, A. Al-Hamadi, A. Farag, H. Neumann, and B. Michaelis, “Facial Expression Recognition Based on Geometric and Optical Flow Features in Colour Image Sequences,” Computer Vision, IET, vol. 6, pp. 79–89, 2012.
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