MyeongOh Lee  Ui Nyoung Yoon  Seunghyun Ko  Geun-Sik Jo

10.5626/JOK.2019.46.12.1241

facial expression recognition efficient cnn group convolution Depth-wise Separable Convolution channel attention

Recently, studies using the convolutional neural network have been actively conducted to recognize emotions from facial expressions. In this paper, we propose an efficient convolutional neural network that solves the model complexity problem of the deep convolutional neural network used to recognize the emotions in facial expression. To reduce the complexity of the model, we used group convolution, depth-wise separable convolution to reduce the number of parameters, and the computational cost. We also enhanced the reuse of features and channel information by using Skip Connection for feature connection and Channel Attention. Our method achieved 70.32% and 85.23% accuracy on FER2013, RAF-single datasets with four times fewer parameters (0.39 Million, 0.41 Million) than the existing model.