Sustainable Energy and Artificial Intelligence

Emotion recognition in Video using a hybrid of supervised and unsupervised deep neural networks

Document Type : Original Article

Authors

Haniyeh Amirbeigi 1
Hamid Reza Shahdoosti 2

1 Electrical and Computer Engineering, Hamedan University of Technology, Hamedan, Iran

2 Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Hamedan University of Technology, Hamedan, Iran.

https://doi.org/10.66224/seai.2605-1056
Abstract
Facial expressions constitute one of the most important forms of non-verbal communication, enabling humans to convey emotions and establish effective interpersonal interactions. In recent years, facial emotion recognition (FER) has significantly benefited from deep learning techniques, particularly architectures capable of modeling both spatial and temporal information in video sequences. Motivated by the dynamic nature of facial expressions, this study proposes and evaluates two hybrid deep learning frameworks for video-based emotion recognition: GRBM-ConvLSTM2D and GRBM-3DCNN. In both configurations, a Gaussian Restricted Boltzmann Machine (GRBM) is employed as a preliminary feature extraction layer to enhance spatiotemporal representation learning. Implemented in Python, the proposed models are designed to classify seven emotional categories: surprise, happiness, anger, sadness, fear, disgust, and contempt. The experimental evaluation was conducted on the benchmark CK+ dataset. The results demonstrate that both proposed architectures achieve strong recognition performance, with the GRBM-ConvLSTM2D model attaining a validation accuracy of 91.16% and the GRBM-3DCNN model achieving 88.80%, confirming the effectiveness and robustness of the proposed hybrid frameworks for video-based facial emotion recognition.

Keywords

Emotion Recognition
Hybrid Neural Network
Convolutional Neural Network
Recurrent Neural Network
Gaussian Restricted Boltzman Machine
Subjects
Sustainable Energy and Artificial Intelligence
Volume 2, Issue 3
Summer 2026
Pages 227-239

  • Receive Date 26 May 2026
  • Revise Date 05 July 2026
  • Accept Date 07 July 2026
  • First Publish Date 07 July 2026

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