September 10, 2025Journal Of Computer Networks Architecture and High Performance ComputingOpen Access

Facial Expression Recognition Using Fused Features: A Comparison of Deep and Machine Learning

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Deep learning techniques significantly outperformed machine learning approaches in facial expression recognition.
The study achieved a 5.22% increase in classification accuracy for the CK+ dataset and 3.07% for Oulu-CASIA.
Evaluation used classification accuracy, precision, recall, and F1-score as performance metrics across two datasets.
Data fusion techniques combining geometrical and appearance features enhanced classifier performance.

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Implication

This comparative study evaluates deep learning and machine learning in facial expression recognition, highlighting classification accuracy improvements.

Cite This Study

Jabbooree et al. (2025) studied this question.

synapsesocial.com/papers/68c2416eb210217d6479fa25 https://doi.org/10.47709/cnahpc.v7i3.6128

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Implication

This comparative study evaluates deep learning and machine learning in facial expression recognition, highlighting classification accuracy improvements.

Facial Expression Recognition Using Fused Features: A Comparison of Deep and Machine Learning

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Discussion

Implication

Cite This Study

Facial Expression Recognition Using Fused Features: A Comparison of Deep and Machine Learning

Key Points

Discussion

Implication

Cite This Study

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