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.
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This paper is aimed at creating extremely small and fast convolutional neural networks (CNN) for the problem of facial expression recognition (FER) from frontal face images.
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.
This paper proposes two 3D-CNN methods: MicroExpSTCNN and MicroExpFuseNet, for spontaneous facial micro-expression recognition by exploiting the spatiotemporal information in CNN framework.
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.
We present a new end-to-end network architecture for facial expression recognition with an attention model.
This paper presents a deep learning model to improve engagement recognition from face images captured in the wild that overcomes the data sparsity challenge by pre-training on readily available basic facial expression data, before training on specialised engagement data.
The proposed architecture achieves 99. 6% for CKP and 98. 63% for MMI, therefore performing better than the state of the art using CNNs.
SOTA for Facial Expression Recognition on MMI