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Hybrid-Deep Learning Model for Emotion Recognition Using Facial Expressions

Author

Listed:
  • Garima Verma

    (DIT University)

  • Hemraj Verma

    (DIT University)

Abstract

Humans use facial expressions as a tool to show emotional states. Facial expression recognition remains an interesting and challenging area of research in computer vision. An improved deep learning approach using a convolution neural network (CNN) is proposed in this paper to predict emotions by analysing facial expressions contained in an image. The model developed in this work consists of one CNN to analyse the primary emotion of the image as being happy or sad and a second CNN to predict the secondary emotion of the image. The proposed model was trained on the FER2013 and Japanese female facial expression (JAFFE) datasets with results suggesting its capability of predicting emotions from facial expressions better than existing state-of-the-art approaches.

Suggested Citation

  • Garima Verma & Hemraj Verma, 2020. "Hybrid-Deep Learning Model for Emotion Recognition Using Facial Expressions," The Review of Socionetwork Strategies, Springer, vol. 14(2), pages 171-180, October.
    • Handle: RePEc:spr:trosos:v:14:y:2020:i:2:d:10.1007_s12626-020-00061-6
    DOI: 10.1007/s12626-020-00061-6
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    Cited by:

    1. Xiaowei Chen & Zainuddin Ibrahim, 2023. "A Comprehensive Study of Emotional Responses in AI-Enhanced Interactive Installation Art," Sustainability, MDPI, vol. 15(22), pages 1-22, November.

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