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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Facial expression recognition is a topic of great interest in most fields from artificial intelligence and gaming to marketing and healthcare.
In this paper we present a novel Human-to-Animation conditional Generative Adversarial Network (HA-GAN) to overcome these two problems by using many (human faces) to one (animated face) mapping.
We propose a novel landmarks-assisted collaborative end-to-end deep framework for automatic 4D FER.
This expression representation is disentangled from identity component by explicitly providing the identity code to the decoder part of DE-GAN.
First, we create a new facial expression dataset of more than 200k images with 119 persons, 4 poses and 54 expressions.
In this paper, we focus on AU detection in micro-expressions.