Development of Video-Based Emotion Recognition System using Transfer Learning

Due to the complexity of its system and the numerous advantages of its implementation, video emotion identification is a popular area of study today. There have been multiple ways implemented. In this project, emotion recognition in videos will be performed using deep learning. The model will include initialization, feature extraction, emotion categorization, and prediction. LeNet and AlexNet, two distinct neural networks, are used to extract features and classify emotions. There will be some parameter tuning to determine if it enhances the employed architecture’s performance, including optimizers and batch size. Each architecture for deep learning will generate a final forecast of four fundamental emotions: disgust, happiness, sadness, and surprise. AlexNet’s performance is enhanced by the SGD optimizer, whereas RMSprop improves LeNet’s performance. Results showed that AlexNet with SGD optimizer provides 93.00% recognition accuracy.