What question did this study set out to answer?

To develop an automated facial emotion recognition framework that minimizes classification error through enhanced feature selection and classifier hyperparameter optimization.

January 1, 2024Mathematical Biosciences & EngineeringOpen Access

Improved optimizer with deep learning model for emotion detection and classification

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Authors

CJChristeena JosephKarunya University GKG. Jaspher W. KathrineKarunya University SVShanmuganathan VimalSri Eshwar College of Engineering

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Implication

Deep learning study demonstrates over 99% accuracy across benchmark image datasets, indicating improved reliability for human-robot interfaces.

Key Points

To develop an automated facial emotion recognition framework that minimizes classification error through enhanced feature selection and classifier hyperparameter optimization.
Preprocessed facial images using a gradient wavelet anisotropic filter (GWAF) and extracted initial features with SqueezeNet.
Applied the improved Botox optimization algorithm (IBoA) for feature selection and classified emotions using an enhanced kernel ResNet50 (EK-ResNet50) tuned by the walrus optimization algorithm (WOA).
Implemented the pipeline in Python and evaluated classification performance on the public CK+ and FER-2013 benchmark datasets.
Achieved an overall classification accuracy of 99.37% on the CK+ dataset.
Achieved an overall classification accuracy of 99.25% on the FER-2013 dataset, surpassing state-of-the-art comparative methods across sensitivity, specificity, and F1-score.

Cite This Study

Joseph et al. (2024) studied this question.

synapsesocial.com/papers/6a153e3bcb0379474a820c5f https://doi.org/10.3934/mbe.2024290

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Implication

Deep learning study demonstrates over 99% accuracy across benchmark image datasets, indicating improved reliability for human-robot interfaces.

Improved optimizer with deep learning model for emotion detection and classification

Discussion

Member takes

Implication

Key Points

Cite This Study

Improved optimizer with deep learning model for emotion detection and classification

Discussion

Member takes

Implication

Key Points

Cite This Study

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