Emotion Detection Using Haar-Cascade Classifier and CNN

To create a reliable and efficient system for detecting emotions by fusing a convolutional neural network (CNN) with the Haar cascade classifier for face detection. Background Study: Enhancing human-computer interaction in domains like mental health and customer service requires the ability to identify emotions. The efficacy and usability of traditional approaches are limited by their frequent difficulties with accurate emotion classification and real-time processing. Methods: The proposed technique combines the Haar Cascade Classifier with a Convolutional Neural Network (CNN). First, the Haar Cascade Classifier efficiently detects face regions, which reduces the computational strain on the CNN. The CNN, which has multiple convolutional and pooling layers, is then trained on a large dataset of various facial expressions to identify emotions. This integrated technique improves the speed and accuracy of real-time emotion recognition. Results: The integrated system obtained 92% accuracy, greatly outperforming solo techniques. The Haar Cascade Classifier ensures accurate face detection, which improves CNN emotion classification and overall processing time. Validation on a public dataset confirmed the system’s better performance, indicating its suitability for real-time applications. Conclusion: The Haar Cascade Classifier combined with CNN provides a highly accurate and efficient solution for emotion detection. This hybrid technique increases real-time processing and classification accuracy, making it appropriate for applications such as real-time emotion monitoring, interactive gaming, and customer support platforms.