Skip to main content
SpringerLink
Log in

Introduction

  • Chapter
  • First Online:
Human Emotion Recognition from Face Images

Part of the book series: Cognitive Intelligence and Robotics ((CIR))

  • 797 Accesses

Abstract

The definition of the emotions  (Kitayama and Markus in Emotion and Culture: Empirical Studies of Mutual Influence. American Psychological Association, 1994 [1]) is the changes in psychological states that comprise thoughts, physiological changes, feelings, and expressive behaviors to act. The accurate combination of the psychological changes fluctuates from emotion to emotion and it is not necessarily accompanied by behaviors.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. S.E. Kitayama, H.R.E. Markus, Emotion and Culture: Empirical Studies of Mutual Influence (American Psychological Association, 1994)

    Google Scholar 

  2. B. Parkinson, A.H. Fischer, A.S.R. Manstead, Emotion in Social Relations: Cultural, Group, and Interpersonal Processes (Psychology Press, 2005)

    Google Scholar 

  3. R. Ekman, What the Face Reveals: Basic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS) (Oxford University Press, USA, 1997)

    Google Scholar 

  4. P. Ekman, W.V. Frisen, Emotion in the Human Face (Prentice Hall, Eagle Woods Cliffs, NJ, 1975)

    Google Scholar 

  5. P. Lucey, J.F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, I. Matthews, The extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression, in Computer Society Conference on Computer Vision and Pattern Recognition-Workshops (IEEE, 2010)

    Google Scholar 

  6. M. Lyons, S. Akamatsu, M. Kamachi, J. Gyoba, Coding facial expressions with gabor wavelets, in Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998. Proceedings (IEEE, 1998), pp. 200–205

    Google Scholar 

  7. M.F. Valstar, M. Pantic, Induced disgust, happiness and surprise: an addition to the mmi facial expression database, in Proceedings of International Conference on Language Resources and Evaluation, Workshop on EMOTION (Malta, 2010), pp. 65–70

    Google Scholar 

  8. N. Aifanti, C. Papachristou, A. Delopoulos. The mug facial expression database, in 11th International Workshop on Image Analysis for Facial Expression Database (Desenzano, Italy, 2010), pp. 12–14

    Google Scholar 

  9. R.W. Picard, R. Picard, Affective Computing, vol. 252 (MIT press Cambridge, 1997)

    Google Scholar 

  10. P. Ekman, W.V. Friesen, Facial Action Coding System (1977)

    Google Scholar 

  11. K. Mase, Recognition of facial expression from optical flow. IEICE Trans. (E) 74, 3474–3483 (1991)

    Google Scholar 

  12. Y. Yaccob, L. Davis, Recognizing facial expressions by spatio-temporal analysis, in Proceedings of the 12th IAPR International Conference on Pattern Recognition, Conference A: Computer Vision & Image Processing, vol. 1 (IEEE, 1994), pp. 747–749

    Google Scholar 

  13. M. Rosenblum, Y. Yacoob, L.S. Davis, Human expression recognition from motion using a radial basis function network architecture. IEEE Trans. Neural Netw. 7(5), 1121–1138 (1996)

    Google Scholar 

  14. A. Lanitis, C.J. Taylor, T.F. Cootes, Automatic interpretation and coding of face images using flexible models. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 743–756 (1997)

    Article  Google Scholar 

  15. Curtis Padgett and Garrison W Cottrell. A simple neural network models categorical perception of facial expressions, in Proceedings of the Twentieth Annual Cognitive Science Conference (1998), pp. 806–807

    Google Scholar 

  16. S. Harnad, Psychophysical and cognitive aspects of categorical perception: a critical overview, in Categorical Perception: The Groundwork of Cognition (Cambridge University Press, 1987), pp. 1–52

    Google Scholar 

  17. Z. Zhang, Feature-based facial expression recognition: sensitivity analysis and experiments with a multilayer perceptron. Int. J. Pattern Recogn. Artif. Intell. 13(06), 893–911 (1999)

    Article  Google Scholar 

  18. K. Anderson, P.W. McOwan, A real-time automated system for the recognition of human facial expressions. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 36(1), 96–105 (2006)

    Google Scholar 

  19. I. Kotsia, I. Pitas, Facial expression recognition in image sequences using geometric deformation features and support vector machines. IEEE Trans. Image Process. 16(1), 172–187 (2007)

    Article  MathSciNet  Google Scholar 

  20. Y. Zhang, Q. Ji, Active and dynamic information fusion for facial expression understanding from image sequences. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 699–714 (2005)

    Article  Google Scholar 

  21. Y.I. Tian, T. Kanade, J.F. Cohn, Recognizing action units for facial expression analysis. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 97–115 (2001)

    Google Scholar 

  22. J. Shi, A. Samal, D. Marx, How effective are landmarks and their geometry for face recognition? Comput. Vis. Image Underst. 102(2), 117–133 (2006)

    Article  Google Scholar 

  23. M.F. Valstar, M. Pantic, Biologically versus logic inspired encoding of facial actions and emotions in video, in 2006 IEEE International Conference on Multimedia and Expo (IEEE, 2006), pp. 325–328

    Google Scholar 

  24. S. Park, J. Shin, D. Kim, Facial expression analysis with facial expression deformation. In 19th International Conference on Pattern Recognition, 2008. ICPR 2008 (IEEE, 2008), pp. 1–4

    Google Scholar 

  25. D. Cai, X. He, Y. Hu, J. Han, T. Huang, Learning a spatially smooth subspace for face recognition, in IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR’07 (IEEE, 2007), pp. 1–7

    Google Scholar 

  26. W. Li, S. Prasad, J.E. Fowler, Hyperspectral image classification using gaussian mixture models and markov random fields. IEEE Geosci. Remote Sens. Lett. 11(1), 153–157 (2014)

    Google Scholar 

  27. X. He, M. Ji, H. Bao, Graph embedding with constraints, in IJCAI9, 1065–1070 (2009)

    Google Scholar 

  28. A.M. Martínez, Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 748–763 (2002)

    Google Scholar 

  29. G. Guo, C.R. Dyer, Learning from examples in the small sample case: face expression recognition. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 35(3), 477–488 (2005)

    Google Scholar 

  30. J. Han, K.-K. Ma, Rotation-invariant and scale-invariant gabor features for texture image retrieval. Image Vis. Comput. 25(9), 1474–1481 (2007)

    Article  Google Scholar 

  31. L. Ma, K. Khorasani, Facial expression recognition using constructive feedforward neural networks. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 34(3), 1588–1595 (2004)

    Google Scholar 

  32. A. Majumder, L. Behera, V.K. Subramanian, Local binary pattern based facial expression recognition using self-organizing map, in 2014 International Joint Conference on Neural Networks (IJCNN) (IEEE, 2014), pp. 2375–2382

    Google Scholar 

  33. J. Yi, X. Mao, L. Chen, Y. Xue, A. Compare, Facial expression recognition considering individual differences in facial structure and texture. IET Comput. Vis. 8(5), 429–440 (2014)

    Article  Google Scholar 

  34. SL Happy and Aurobinda Routray, Automatic facial expression recognition using features of salient facial patches. IEEE Trans. Affect. Comput. 6(1), 1–12 (2015)

    Article  Google Scholar 

  35. M. Matsugu, K. Mori, Y. Mitari, Y. Kaneda, Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Netw. 16(5), 555–559 (2003)

    Article  Google Scholar 

  36. H. Boughrara, M. Chtourou, C.B. Amar, L. Chen, Facial expression recognition based on a MLP neural network using constructive training algorithm. Multimedia Tools Appl. 75(2), 709–731 (2016)

    Google Scholar 

  37. C. Shan, S. Gong, P.W. McOwan, Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis. Comput. 27(6), 803–816 (2009)

    Google Scholar 

  38. M. Pardàs, A. Bonafonte, Facial animation parameters extraction and expression recognition using hidden markov models. Signal Process. Image Commun. 17(9), 675–688 (2002)

    Article  Google Scholar 

  39. F. Bourel, C.C. Chibelushi, A.A. Low, Recognition of facial expressions in the presence of occlusion, in BMVC, pp. 1–10 (2001)

    Google Scholar 

  40. I. Kotsia, I. Buciu, I. Pitas, An analysis of facial expression recognition under partial facial image occlusion. Image Vis. Comput. 26(7), 1052–1067 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer and Systems Sciences, Visva-Bharati University, Santiniketan, West Bengal, India

    Paramartha Dutta

  2. Department of Computer Science and Engineering and Information Technology, Siliguri Institute of Technology, Siliguri, West Bengal, India

    Asit Barman

Authors
  1. Paramartha Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Asit Barman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paramartha Dutta .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Dutta, P., Barman, A. (2020). Introduction. In: Human Emotion Recognition from Face Images. Cognitive Intelligence and Robotics. Springer, Singapore. https://doi.org/10.1007/978-981-15-3883-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-3883-4_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3882-7

  • Online ISBN: 978-981-15-3883-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation