Real-Time Facial Emotion Recognition System Using Convolutional Neural Networks and Computer Vision

Abstract

Facial emotion recognition has emerged as a significant research area in the fields of computer vision and artificial intelligence due to its wide range of applications in humancomputer interaction, healthcare, security, and entertainment. Emotions play a crucial role in human communication, and the ability to automatically detect and interpret facial expressions can enhance the effectiveness of intelligent systems. This research presents a real-time facial emotion recognition system using Convolutional Neural Networks (CNN) and computer vision techniques.The proposed system utilizes a deep learning-based approach to classify facial expressions into seven categories: Angry, Disgusted, Fearful, Happy, Neutral, Sad, and Surprised. The model is trained using grayscale facial images of size 48×48 pixels, ensuring computational efficiency while maintaining classification accuracy. Data preprocessing is performed using image normalization techniques, and an image generator is used to efficiently load and augment training data.The CNN architecture consists of multiple convolutional layers, pooling layers, and fully connected layers. Convolutional layers extract spatial features from input images, while pooling layers reduce dimensionality and prevent overfitting. Dropout layers are incorporated to enhance generalization by reducing model dependency on specific neurons. The final softmax layer outputs probability distributions over the emotion classes.The system operates in two modes: training and real-time detection. During training, the model learns from labeled datasets and evaluates performance using accuracy and loss metrics. In realtime mode, the system captures live video input through a webcam using the OpenCV library. Face detection is performed using Haar Cascade classifiers, and detected facial regions are processed and fed into the trained model for emotion prediction.Experimental results demonstrate that the system achieves reliable performance in recognizing facial expressions under varying lighting conditions and orientations. The integration of deep learning with real-time video processing enables accurate and efficient emotion detection. In conclusion, the proposed system provides an effective solution for real-time facial emotion recognition. Future improvements may include the use of advanced deep learning architectures, larger datasets, and integration with multimodal systems for enhanced accuracy and robustness.