Facial expression recognition is the task of classifying the expressions on face images into various categories such as anger, fear, surprise, sadness, happiness and so on.
( Image credit: DeXpression )
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Crowd sourcing has become a widely adopted scheme to collect ground truth labels.
On the other hand, KD is proved to be useful for model compression for the FER problem, and we discovered that its effects gets more and more significant with the decreasing model size.
The feature embedding module is a deep Convolutional Neural Network (CNN) which embeds face images into feature vectors.
In this work, we explore the benefits of using a man- ifold network structure for covariance pooling to improve facial expression recognition.
Extensive experiments show that our RAN and region biased loss largely improve the performance of FER with occlusion and variant pose.
The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge.
SOTA for Facial Expression Recognition on FER2013 (using extra training data)
This paper proposes two 3D-CNN methods: MicroExpSTCNN and MicroExpFuseNet, for spontaneous facial micro-expression recognition by exploiting the spatiotemporal information in CNN framework.
We present a new end-to-end network architecture for facial expression recognition with an attention model.
4DFAB contains recordings of 180 subjects captured in four different sessions spanning over a five-year period.