Real-Time Emotion Classification and Prediction Using a Hybrid Facial Expression Recognition Model Emotion Recognition in Human Resources’ Future

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MAIN FINDING: The study presents a hybrid model that enhances emotion recognition accuracy in real-time settings.
KEY EVIDENCE: Utilizing a deep convolutional neural network, the model categorizes facial expressions into seven emotion types.
APPROACH: The method integrates a haar cascade model with GPU acceleration using the FER dataset for effective training.
SIGNIFICANCE: Emotion recognition tools can transform HR practices, balancing benefits with privacy and ethical considerations.

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Hybrid model demonstrates improved emotion recognition in HR, suggesting new methods for employee engagement assessment.

Sharma et al. (2025) studied this question.

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