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Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique

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Advances in Visual Computing (ISVC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12510))

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Abstract

Emotion categorization can be the process of identifying different emotions in humans based on their facial expressions. It requires time and sometimes it is hard for human classifiers to agree with each other about an emotion category of a facial expression. However, machine learning classifiers have done well in classifying different emotions and have widely been used in recent years to facilitate the task of emotion categorization. Much research on emotion video databases uses a few frames from when emotion is expressed at peak to classify emotion, which might not give a good classification accuracy when predicting frames where the emotion is less intense. In this paper, using the CK+ emotion dataset as an example, we use more frames to analyze emotion from mid and peak frame images and compared our results to a method using fewer peak frames. Furthermore, we propose an approach based on sequential voting and apply it to more frames of the CK+ database. Our approach resulted in up to 85.9% accuracy for the mid frames and overall accuracy of 96.5% for the CK+ database compared with the accuracy of 73.4% and 93.8% from existing techniques.

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Notes

  1. 1.

    People that are trained in emotion categorization. These people labeled these databases based on the assumption that people smile when happy, frown their faces when sad, and scowl when anger irrespective of their age, race, and ethnicity.

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Author information

Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, 6012, Wellington, New Zealand

    Harisu Abdullahi Shehu & Will Browne

  2. School of Psychology, Victoria University of Wellington, 6012, Wellington, New Zealand

    Hedwig Eisenbarth

Authors
  1. Harisu Abdullahi Shehu
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  2. Will Browne
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  3. Hedwig Eisenbarth
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Corresponding author

Correspondence to Harisu Abdullahi Shehu .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Stony Brook University, Stony Brook, NY, USA

    Zhaozheng Yin

  3. Drexel University, Philadelphia, PA, USA

    Edward Kim

  4. RWTH Aachen University, Aachen, Germany

    Jan Bender

  5. University of Edinburgh, Edinburgh, UK

    Kartic Subr

  6. IBM Research – Cambridge, Cambridge, MA, USA

    Bum Chul Kwon

  7. University of Waterloo, Waterloo, ON, Canada

    Jian Zhao

  8. Graz University of Technology, Graz, Austria

    Denis Kalkofen

  9. The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    George Baciu

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Shehu, H.A., Browne, W., Eisenbarth, H. (2020). Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_49

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  • DOI: https://doi.org/10.1007/978-3-030-64559-5_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64558-8

  • Online ISBN: 978-3-030-64559-5

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