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

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posted on 14.04.2021, 01:36 by Harisu Abdullahi Shehu, William Browne, Hedwig Eisenbarth
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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Preferred citation

Shehu, H. A., Browne, W. & Eisenbarth, H. (2020). Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique. Advances in Visual Computing (12510 LNCS, pp. 618-632). Springer International Publishing. https://doi.org/10.1007/978-3-030-64559-5_49

Book title

Advances in Visual Computing

Publisher

Springer International Publishing

Pagination

618-632

Volume

12510 LNCS

ISBN

9783030645588

ISSN

0302-9743

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