Comparative Study of Existing Facial Emotion Recognition and Stress Detection Models with Proposed AI-Based Framework

Rohini Narayan Pawar

Comparative Study of Existing Facial Emotion Recognition and Stress Detection Models with Proposed AI-Based Framework

Author(s)	Ms. Rohini Narayan Pawar
Country	India
Abstract	Facial Emotion Recognition (FER) and stress level identification have become important research areas in Artificial Intelligence, Machine Learning, and Human Computer Interaction. Human facial expressions provide useful information about emotional and mental conditions. In this research work, an AI-based framework is proposed for recognizing human emotions and estimating stress levels using facial parameters in real-time environments. The system uses facial features such as eye movement, eyebrow movement, lip movement, and facial muscle changes for analysis. The proposed work is implemented using different Machine Learning and Deep Learning classifiers such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Keras-Deep Neural Network, and Neural Network Sklearn. Both primary and secondary datasets are used for training and testing purposes to improve the robustness and diversity of the system. Experimental results show that the proposed model achieved 0.95 training accuracy and 0.89 testing accuracy for emotion recognition on the dataset, while stress recognition achieved 0.91 training accuracy and 0.88 testing accuracy. On the primary dataset, the model achieved 0.91 training accuracy and 0.83 testing accuracy for emotion detection, and 0.90 training accuracy and 0.84 testing accuracy for stress detection. The proposed and 0.84 testing accuracy for stress detection. The proposed system provides a non-contact, real-time, and efficient solution for emotion and stress analysis. This research can be useful in healthcare, mental health monitoring, smart surveillance, and human-computer interaction applications.
Keywords	Facial Emotion Recognition (FER) , Stress Detection, Artificial Intelligence, Machine Learning, Deep Learning, Facial Parameters, KNN , SVM, Random Forest, Keras-Deep Neural Network, and Neural Network Sklearn, Human Computer Interaction, Real-Time Monitoring, Emotion Classification, Stress Estimation.
Field	Computer
Published In	Volume 17, Issue 2, April-June 2026
Published On	2026-06-03

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Comparative Study of Existing Facial Emotion Recognition and Stress Detection Models with Proposed AI-Based Framework

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