. In spite of efforts made to improve the accuracy of FER systems using DNN, existing methods still are not generalizable enough in practical applications. This paper proposes a 3D Convolutional Neural Network method for FER in videos. This new network architecture consists of 3D Inception-ResNet layers followed by an LSTM unit that together extracts the spatial relations within facial images as well as the temporal relations between different frames in the video. Facial landmark points are also used as inputs to our network which emphasize on the importance of facial components rather than the facial regions that may not contribute significantly to generating facial expressions. Our proposed method is evaluated using four publicly available databases in subject-independent and cross-database tasks and outperforms state-of-the-art methods.
Hasani et al. REF propose a network architecture that consists of 3D convolutional layers followed by a Long-Short Term Memory (LSTM) unit that together extract the spatial relations within facial images and the temporal relations between different frames in the video.
Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Computer Science