September 5, 2025ElectronicsOpen Access

An Ensemble Learning Approach for Facial Emotion Recognition Based on Deep Learning Techniques

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This research demonstrates the application of transfer learning and ensemble learning to improve emotion classification accuracy in facial images.

The approach achieves 100% accuracy on the test set using a meta-classifier, showcasing its effectiveness.
By employing EfficientNet-B0, the study fine-tunes a pretrained model on diverse facial images to identify emotions.
Transfer learning and stacking techniques were integrated to enhance the model's performance in emotion recognition.
Training also included the Cohn-Kanade dataset, achieving an impressive 92% accuracy in emotion classification.

Almubarak et al. (2025) studied this question.

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This research demonstrates the application of transfer learning and ensemble learning to improve emotion classification accuracy in facial images.