Facial Emotion Recognition using CNN in PyTorch
Deyuan Qu Sudip Dhakal Dominic Carrillo
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.