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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The feature embedding module is a deep Convolutional Neural Network (CNN) which embeds face images into feature vectors.
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.
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 images that overcomes the data sparsity challenge by pre-training on readily available basic facial expression data, before training on specialised engagement data.
In this work, we explore the benefits of using a man- ifold network structure for covariance pooling to improve facial expression recognition.
We then introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets.
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.
This is carried out through recognition of facial expressions, which accompany the hand signs used in this language.