Figure2

Figure 2. The long-term multi-granularity deep framework for driver drowsiness detection. The first stage is well-aligned multi-granularity patches that consist of local regions, main parts, and the global face. Parallel convolutional layers are well-applied to process these patches separately. In the second stage, a fully connected layer fuses local and global clues and generates a representation. The first two stages together construct the MCNN. The third stage uses Recurrent Neural Networks (RNN) with multiple LSTM blocks to mine the clues in the temporal dimension, together with a fully connected layer.