Bsfoa-Dt Based Facial Emotion Recognition Using Facial Features
DOI:
https://doi.org/10.21070/jicte.v10i1.1722Keywords:
Facial Emotion Recognition, Deep Feature Extraction, Binary Starfish Optimization Algorithm, Decision Tree Classification, Dimensionality ReductionAbstract
General Background: Automated facial emotion recognition serves as a vital component in modern human-computer interaction and affective computing systems. Specific Background: Convolutional neural networks extract high-dimensional representation features from expressive face images across various public datasets. Knowledge Gap: Existing approaches often retain redundant spatial attributes, increasing computational overhead and reducing overall classification efficiency. Aims: This paper proposes a hybrid methodology combining AlexNet deep feature extraction, Binary Starfish Optimization Algorithm feature selection, and Decision Tree classification. Results: Experimental evaluations on FER and FER+ datasets demonstrate significant dimensionality reduction from 128 to 60 features alongside a peak testing accuracy of 94.54%. Novelty: The integration of the binary starfish metaheuristic provides an optimal feature selection mechanism specifically tailored for discrete decision tree boundaries. Implications: This architecture offers a scalable and computationally efficient pipeline for real-time intelligent emotion monitoring systems.
Key Findings Highlights
Integrated AlexNet feature extraction with Binary Starfish Optimization Algorithm to select critical facial attributes.
Reduced high-dimensional feature vectors from 128 to 60 essential parameters without loss of predictive stability.
Achieved superior performance on FER+ benchmark datasets compared to conventional metaheuristic classifiers.
Keywords: Facial Emotion Recognition, Deep Feature Extraction, Binary Starfish Optimization Algorithm, Decision Tree Classification, Dimensionality Reduction
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