Pruning Framework for Efficient Facial Emotion Recognition using Deep Learning

Authors

  • P. Vijaya Lakshmi, V. Murugesh

Keywords:

Facial Emotion Recognition, Neural Network Compression, Model Pruning, Quantization Techniques, Model Compression Framework, Deep Learning Optimization

Abstract

The application of neural networks in their entirety has produced remarkable outcomes in the domain of facial emotion recognition. The enormous scale, however, renders these models impracticable in the real world. In an effort to address this deficiency, this study introduces an innovative approach that combines two well-known model compression methods—pruning. In order to decrease the dimensions of neural models that are explicitly designed for the purpose of facial emotion recognition, we propose the implementation of a pruning-then-quantization framework. Comprehensive experiments conducted on three separate datasets provide evidence of the framework's capability to significantly compress models without compromising their performance. In order to delve deeper into the nuanced effectiveness and versatility of our innovative framework within fine-grained modules, we execute an exhaustive analysis of the compression performance layer by layer. The accuracy achieved by the pruning process is 97.95%

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Published

24.03.2024

How to Cite

V. Murugesh, P. V. L. (2024). Pruning Framework for Efficient Facial Emotion Recognition using Deep Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 2021–2028. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5668

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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