Human Emotion Recognition Using ResNet Architechture

Authors

  • Jayashri Musale Department of Computer Engineering, MIT Academy of Engineering, Pune, India. Author
  • Nikita Hajare Department of Computer Engineering, MIT Academy of Engineering, Pune, India. Author
  • Shruti Garud Department of Electronics and Telecommunication Engineering, MIT Academy of Engineering, Pune, India. Author
  • Radhika Chaudhari Department of Electronics and Telecommunication Engineering, MIT Academy of Engineering, Pune, India. Author
  • Dr. Pramod Ganjewar Department of Computer Engineering, MIT Academy of Engineering, Pune, India. Author

DOI:

https://doi.org/10.47392/IRJAEH.2025.0483

Keywords:

Emotion Detection, ResNet Architecture, Deep Learning, Human- Computer Interaction, Machine Learning, Real-time Emotion Classification

Abstract

Emotion detection plays a crucial role in enabling systems to accurately interpret and respond to human emotions, thereby enhancing human-computer interaction. This re- search leverages the Residual Neural Network (ResNet) architecture—a deep learning model specifically designed to tackle challenges like the vanishing gradient problem in deep networks—to deliver an improved approach to emotion detection. By leveraging ResNet’s ability to learn residuals, the proposed system achieves superior accuracy in classifying emotions from facial expressions, outperforming traditional models. Com- pared to KNearest Neighbors (KNN), which struggles with high-dimensional data, and Convolutional Neural Networks (CNNs), which require large datasets and computational resources, ResNet excels with its residual connections, allowing deeper networks to ef- ficiently learn subtle facial features. This leads to better performance in challenging conditions like lighting variations and occlusions. Despite its higher computational cost, ResNet’s accuracy makes it the ideal choice for emotion detection and face recognition in this study.

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Published

2025-07-24

How to Cite

Human Emotion Recognition Using ResNet Architechture. (2025). International Research Journal on Advanced Engineering Hub (IRJAEH), 3(07), 3285-3293. https://doi.org/10.47392/IRJAEH.2025.0483

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