Enhanced convolutional LSTM with spatial and temporal skip connections and temporal gates for facial expression recognition from video

We propose an algorithm that enhances convolutional long short-term memory (ConvLSTM), i.e., Enhanced ConvLSTM, by adding skip connections to spatial and temporal directions and temporal gates to conventional ConvLSTM to suppress gradient vanishing and use information that is older than the previous frame. We also propose a method that uses this algorithm to automatically recognize facial expressions from videos. The proposed facial expression recognition method consists of two Enhanced ConvLSTM streams. We conducted two experiments using eNTERFACE05 database and CK+. First, we conducted an ablation study to investigate the effectiveness of adding spatial and temporal skip connections and temporal gates to ConvLSTM. Ablation studies have shown that adding skip connections to spatial and temporal and temporal gates to conventional ConvLSTM provides the greatest performance gains. Second, we compared the accuracies of the proposed method and state-of-the-art methods. In an experiment comparing the proposed method and state-of-the-art methods, the accuracy of the proposed method was 49.26% on eNTERFACE05 database and 95.72% on CK+. Our proposed method shows superior performance compared to the state-of-the-art methods on eNTERFACE05.

Automated summary

An algorithm that enhances ConvLSTM by adding skip connections and temporal gates was proposed for facial expression recognition, achieving superior performance compared to state-of-the-art methods. Experiments demonstrated the effectiveness of the proposed method on eNTERFACE05 database and CK+ dataset.