An optimized Capsule-LSTM model for facial expression recognition with video sequences
This addresses the problem of recognizing facial expressions in videos for applications like human-computer interaction, but it is incremental as it combines existing capsule and LSTM methods.
The paper tackled facial expression recognition from video sequences by proposing a Capsule-LSTM model, which improved accuracy on the MMI dataset.
To overcome the limitations of convolutional neural network in the process of facial expression recognition, a facial expression recognition model Capsule-LSTM based on video frame sequence is proposed. This model is composed of three networks includingcapsule encoders, capsule decoders and LSTM network. The capsule encoder extracts the spatial information of facial expressions in video frames. Capsule decoder reconstructs the images to optimize the network. LSTM extracts the temporal information between video frames and analyzes the differences in expression changes between frames. The experimental results from the MMI dataset show that the Capsule-LSTM model proposed in this paper can effectively improve the accuracy of video expression recognition.