Facial Emotion Recognition using CNN in PyTorch
This is an incremental improvement for facial emotion recognition applications, aimed at reducing computational resource usage.
The authors tackled real-time facial emotion recognition by implementing a CNN in PyTorch, focusing on reducing space complexity compared to existing methods that read all data at once, but no concrete performance numbers were provided.
In this project, we have implemented a model to recognize real-time facial emotions given the camera images. Current approaches would read all data and input it into their model, which has high space complexity. Our model is based on the Convolutional Neural Network utilizing the PyTorch library. We believe our implementation will significantly improve the space complexity and provide a useful contribution to facial emotion recognition. Our motivation is to understanding clearly about deep learning, particularly in CNNs, and analysis real-life scenarios. Therefore, we tunned the hyper parameter of model such as learning rate, batch size, and number of epochs to meet our needs. In addition, we also used techniques to optimize the networks, such as activation function, dropout and max pooling. Finally, we analyzed the result from two optimizer to observe the relationship between number of epochs and accuracy.