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Emotion Recognition with a Hybrid VGG-ResNet Deep Learning Model: A Novel Approach for Robust Emotion Classification

By
N Karthikeyan ,
N Karthikeyan

Department of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

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K. Madheswari ,
K. Madheswari

Department of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

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Hrithik Umesh ,
Hrithik Umesh

Department of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

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Rajkumar N ,
Rajkumar N

Department of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

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Viji C ,
Viji C

Department of Computer Science and Engineering, Alliance College of Engineering and Technology, Alliance University, Bangalore, India

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Abstract

The recognition and interpretation of human emotions are crucial for various applications such as education, healthcare, and human-computer interactions. Effective emotion recognition can significantly enhance user experience and response accuracy in these fields. This research aims to develop a robust emotion recognition system by integrating VGG and ResNet architectures to improve the identification of subtle variations in facial expressions. This paper proposes a hybrid deep learning approach using a combination of VGG and ResNet models. This system incorporates multiple convolutional and pooling layers along with residual blocks to capture intricate patterns in facial expressions. The FER2013 dataset was employed to train and evaluate the model's performance. Comparative analysis was conducted against other models, including VGG16, DenseNet, and MobileNet. The hybrid model demonstrated superior performance, achieving a training accuracy of 99.80% and a validation accuracy of 66.17%. In contrast, the VGG16, DenseNet, and MobileNet models recorded training accuracies of 54.27%, 68.51%, and 84.68%, and validation accuracies of 46.58%, 56.11%, and 60.35%, respectively. The proposed hybrid approach effectively enhances emotion recognition capabilities by leveraging the strengths of VGG and ResNet architectures. This method outperforms existing models, offering a significant improvement in both training and validation accuracies for emotion recognition systems.

How to Cite

1.
Karthikeyan N, Madheswari K, Umesh H, N R, C V. Emotion Recognition with a Hybrid VGG-ResNet Deep Learning Model: A Novel Approach for Robust Emotion Classification. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 Jul. 4 [cited 2024 Jul. 19];3:960. Available from: https://conferencias.saludcyt.ar/index.php/sctconf/article/view/960

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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