A robust VGG network combined with Denoising Autoencoder module for human emotion recognition

Research Article
Open access

A robust VGG network combined with Denoising Autoencoder module for human emotion recognition

Xiaotian Li 1*
  • 1 The University of Melbourne    
  • *corresponding author xiaotian.li5@student.unimelb.edu.au
Published on 11 December 2023 | https://doi.org/10.54254/2755-2721/27/20230136
ACE Vol.27
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-199-5
ISBN (Online): 978-1-83558-200-8

Abstract

Human emotion can be divided into multiple categories, which makes it possible to recognize emotions automatically. One critical approach for automated emotion recognition is applying the convolutional neural network to classify emotions on human expression images, but the performance decreases if input distortions occur. This paper introduced a hybrid neural network architecture to make the automated emotion recognition robust towards distorted input images and perform similarly to prediction on clean images. This hybrid neural network combines the Denoise Autoencoder (DAE) network with the Visual Geometry Group (VGG) network. Multiple standalone VGG and Hybrid network experiments were conducted with the control variables method. FER-2013 data set from Kaggle was used as the experimental data set. Distorted input images were generated by adding random noise to clean images. As a result, the research raised a valid hybrid network architecture. The hybrid network improved the emotion classification accuracy on the distorted data set from 16.70% to 57.73%, and the accuracy is similar to the classification result on the clean data set.

Keywords:

Automated emotion recognition, Denoise autoencoder, VGG network

Li,X. (2023). A robust VGG network combined with Denoising Autoencoder module for human emotion recognition. Applied and Computational Engineering,27,168-178.
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References

[1]. Dzedzickis A Kaklauskas A and Bucinskas V 2020 Human Emotion Recognition: Review of Sensors and Methods Sensors 20 592.

[2]. Tomkins S 1962 Affect Imagery Consciousness: Volume I: The Positive Affects (Springer Publishing Company).

[3]. Izard C E 1977 Human Emotions (Boston, MA: Springer US).

[4]. Plutchik R 1980 Chapter 1 - A General Psychoevolutionary Theory Of Emotion Theories of Emotion ed R Plutchik and H Kellerman (Academic Press) pp 3–33.

[5]. Yu C and Wang M 2022 Survey of emotion recognition methods using EEG information Cognitive Robotics 2 132–46.

[6]. Mather M and Thayer J F 2018 How heart rate variability affects emotion regulation brain networks Current Opinion in Behavioral Sciences 19 98–104.

[7]. Song Z 2021 Facial Expression Emotion Recognition Model Integrating Philosophy and Machine Learning Theory Front. Psychol. 12 759485.

[8]. Badrulhisham N A S and Mangshor N N A 2021 Emotion Recognition Using Convolutional Neural Network (CNN) J. Phys.: Conf. Ser. 1962 012040.

[9]. Simonyan K and Zisserman A 2015 Very deep convolutional networks for large-scale image recognition 3rd International Conference on Learning Representations (ICLR 2015).

[10]. Atabansi C C Chen T Cao R and Xu X 2021 Transfer Learning Technique with VGG-16 for Near-Infrared Facial Expression Recognition J. Phys.: Conf. Ser. 1873 012033.

[11]. Lévêque L Villoteau F Sampaio E V B Perreira Da Silva M and Le Callet P 2022 Comparing the Robustness of Humans and Deep Neural Networks on Facial Expression Recognition Electronics 11 4030.

[12]. Bajaj K Singh D K and Ansari Mohd A 2020 Autoencoders Based Deep Learner for Image Denoising Procedia Computer Science 171 1535–41.

[13]. Vincent P Larochelle H Lajoie I Bengio Y and Manzagol P A 2010 Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion Journal of Machine Learning Research 11 3371–408.

[14]. Manas S 2020 (online) FER-2013: Learn facial expressions from an image Kaggle Retrieved from https://www.kaggle.com/datasets/msambare/fer2013?select=test.

[15]. Shorten C and Khoshgoftaar T M 2019 A survey on Image Data Augmentation for Deep Learning J Big Data 6 60.

[16]. Singh D and Singh B 2020 Investigating the impact of data normalization on classification performance Applied Soft Computing 97 105524.

Cite this article

Li,X. (2023). A robust VGG network combined with Denoising Autoencoder module for human emotion recognition. Applied and Computational Engineering,27,168-178.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning

ISBN：978-1-83558-199-5(Print) / 978-1-83558-200-8(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.27
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Dzedzickis A Kaklauskas A and Bucinskas V 2020 Human Emotion Recognition: Review of Sensors and Methods Sensors 20 592.

[2]. Tomkins S 1962 Affect Imagery Consciousness: Volume I: The Positive Affects (Springer Publishing Company).

[3]. Izard C E 1977 Human Emotions (Boston, MA: Springer US).

[4]. Plutchik R 1980 Chapter 1 - A General Psychoevolutionary Theory Of Emotion Theories of Emotion ed R Plutchik and H Kellerman (Academic Press) pp 3–33.

[5]. Yu C and Wang M 2022 Survey of emotion recognition methods using EEG information Cognitive Robotics 2 132–46.

[6]. Mather M and Thayer J F 2018 How heart rate variability affects emotion regulation brain networks Current Opinion in Behavioral Sciences 19 98–104.

[7]. Song Z 2021 Facial Expression Emotion Recognition Model Integrating Philosophy and Machine Learning Theory Front. Psychol. 12 759485.

[8]. Badrulhisham N A S and Mangshor N N A 2021 Emotion Recognition Using Convolutional Neural Network (CNN) J. Phys.: Conf. Ser. 1962 012040.

[9]. Simonyan K and Zisserman A 2015 Very deep convolutional networks for large-scale image recognition 3rd International Conference on Learning Representations (ICLR 2015).

[10]. Atabansi C C Chen T Cao R and Xu X 2021 Transfer Learning Technique with VGG-16 for Near-Infrared Facial Expression Recognition J. Phys.: Conf. Ser. 1873 012033.

[11]. Lévêque L Villoteau F Sampaio E V B Perreira Da Silva M and Le Callet P 2022 Comparing the Robustness of Humans and Deep Neural Networks on Facial Expression Recognition Electronics 11 4030.

[12]. Bajaj K Singh D K and Ansari Mohd A 2020 Autoencoders Based Deep Learner for Image Denoising Procedia Computer Science 171 1535–41.

[13]. Vincent P Larochelle H Lajoie I Bengio Y and Manzagol P A 2010 Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion Journal of Machine Learning Research 11 3371–408.

[14]. Manas S 2020 (online) FER-2013: Learn facial expressions from an image Kaggle Retrieved from https://www.kaggle.com/datasets/msambare/fer2013?select=test.

[15]. Shorten C and Khoshgoftaar T M 2019 A survey on Image Data Augmentation for Deep Learning J Big Data 6 60.

[16]. Singh D and Singh B 2020 Investigating the impact of data normalization on classification performance Applied Soft Computing 97 105524.

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