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2000
Volume 18, Issue 8
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

Abstract

Introduction

Electrocardiogram-based facial emotion identification has been a widely spread field in the last few decades. Due to its non-linearity, non-stationary and noisy properties, it is a very difficult job to create a framework that is capable of recognizing emotions with a high recognition rate.

Methods

In this work, we introduce a new framework for facial emotion detection based on feature creation using a topographic representation of ECG signal properties. The feature map is created using deep learning techniques, and further, extricated features are then used for classification techniques to detect facial emotion recognition.

Results

The recognition results are achieved on two publicly available facial expression datasets, , Ascertain and Dreamer. We illustrated the usefulness of our framework by comparing results with other existing methods.

Conclusion

The recognition results prove that the introduced framework can enhance the identifying rate on various given datasets.

© 2025 Bentham Science Publishers
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2024-04-25
2025-11-15

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Electrocardiogram-based Emotion Recognition using Convolutional Neural Networks
Recent Advances in Electrical & Electronic Engineering 18, 1386 (2025); https://doi.org/10.2174/0123520965293221240411091945
/content/journals/raeeng/10.2174/0123520965293221240411091945
/content/journals/raeeng/10.2174/0123520965293221240411091945
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  • Article Type:     Research Article
Keyword(s): ascertain; deep learning; dreamer; electrocardiogram; Emotion recognition; topographic signals

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