Towards Real-Time Facial Emotion-Based Stress Detection Using CNN and Haar Cascade in AI Systems

Understanding human conduct requires the ability to recognise facial emotions, which has applications in everything from human-computer interaction to psychological wellness monitoring. This research provides a new approach to stress detection using Convolutional Neural Networks (or CNNs) and HaarCascade classifiers. The suggested method uses a CNN to recognise facial expressions and Haar Cascade algorithm for face detection. The methodology begins with preliminary processing the input photos, followed by face detection and extraction of facial regions. Those parts are then fed into the CNN model, which classifies emotions. The system has been trained and tested on publicly available datasets, with encouraging results in stress detection accuracy. This method, which detects stress through facial expressions, has potential uses in stress management, mental health evaluation, and personalised therapies. Face expressions have an important part in transmitting emotions, especially stress, which is a common problem in today's fast-paced world. This research provides a novel approach for detecting stress by analysing facial expressions with Convolutional Neural Networks(CNNs)and Haar Cascade classifiers. The proposed system enhances the precision and effectiveness of stress detection by combining the benefits of both approaches. The methodology begins by preprocessing the input photos to improve their quality and normalise them for subsequent analysis. Haar Cascade classifiers are then used to detect faces in the images, ensuring precise identification of facial regions even under different lighting conditions and orientations. The discovered faces are removed and resized to produce homogeneous inputs for further processing.