Advertisement

A Thermal Imaging Based Classification of Affective States Using Multiclass SVM

  • C. M. Naveen KumarEmail author
  • G. ShivakumarEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

In research performances, affective computing has become a developing area because of its large use of application in interface of human computer. Recognition of emotion is one of the art techniques state in determining present human being psychological state. Assessment of the emotional state of humans has been traditionally learned using several direct psychological self-reports and psychological measures. There are various measures to recognize emotional states of human such as facial pictures, gestures, neuro-imaging methods and physiological signals. Therefore, some of these approaches need expensive and sizeable equipment which might hinder free motion. Emotions of human are very overlapping in nature and thus it requires an efficient feature-classifier and extractor assembly. It is a novel non-invasive technique to divide emotion of human through thermal face pictures. Invariants of Hu’s moment of different patches have been fused with statistical characteristic of histogram and used as features of robust in machine of multiclass support vector based division. Here 200 highly expressive thermal images are considered for training and 120 images for testing from IVITE database. The proposed system has overall accuracy of 87.50%.

Keywords

Human emotions Thermal images Statistical features Support vector machine 

References

  1. 1.
    Basu, A., et al.: Human emotion recognition from facial thermal image based on fused statistical feature and multi-class SVM. In: 2015 Annual IEEE India Conference (INDICON). IEEE (2015)Google Scholar
  2. 2.
    He, S., et al.: Facial expression recognition using deep Boltzmann machine from thermal infrared images. In: Humaine Association Conference on Affective Computing and Intelligent Interaction, pp. 239–244 (2013)Google Scholar
  3. 3.
    Khan, M.M., Ingleby, M., Ward, R.D.: Automated facial expression classification and affect interpretation using infrared measurement of facial skin temperature variations. ACM Trans. Auton. Adapt. Syst. 1(1), 91–113 (2006)CrossRefGoogle Scholar
  4. 4.
    Nhan, B.R., Chau, T.: Classifying affective states using thermal infrared imaging of the human face. IEEE Trans. Biomed. Eng. 57(4), 979–987 (2010)CrossRefGoogle Scholar
  5. 5.
    Yoshitomi, Y., Sugimoto, Y., Tomita, S.: A method for detecting transitions of emotional stated using thermal facial image based on a synthesis of facial expressions. Robot. Auton. Syst. 31(3), 147–160 (2000)CrossRefGoogle Scholar
  6. 6.
    Khan, M.M., et al.: Automated classification and recognition of facial expressions using infrared thermal imaging. In: IEEE Conference on Cybernetics and Intelligent Systems, vol. 1. IEEE (2004)Google Scholar
  7. 7.
    Trujillo, L., et al.: Automatic feature localization in thermal images for facial expression recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, CVPR Workshops. IEEE (2005)Google Scholar
  8. 8.
    Miyawaki, N., Yoshitomi, Y., Tomita, S., Kimura, S.: Facial Expression Recognition Using Thermal Image Processing and Neural Network (1997)Google Scholar
  9. 9.
    Hernández, B., et al.: Visual learning of texture descriptors for facial expression recognition in thermal imagery. Comput. Vis. Image Underst. 106, 258 (2007)CrossRefGoogle Scholar
  10. 10.
    Jarlier, S., et al.: Thermal analysis of facial muscles contractions. IEEE Trans. 2(1), 2–9 (2011)Google Scholar
  11. 11.
    Wang, S., Shen, P., Liu, Z.: Facial expression recognition from infrared thermal images using temperature difference by voting. In: IEEE 2nd International Conference on Cloud Computing and Intelligent Systems, pp. 94–98 (2012)Google Scholar
  12. 12.
    Esposito, A., et al.: A naturalistic database of thermal emotional facial expressions and effects of induced emotions on memory. In: Cognitive Behavioral Systems, pp. 158–173. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Cardone, D., et al.: New frontiers for applications of thermal infrared imaging devices: computational psychophysiology in the neurosciences. Sensors 17(5), 1042 (2017)CrossRefGoogle Scholar
  14. 14.
    Grosan, C., Abraham, A.: Intelligent Systems: A Modern Approach. Intelligent Systems Reference Library. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of E&I EngineeringMalnad College of EngineeringHassanIndia

Personalised recommendations