The FER+ dataset is an extension of the original FER dataset, where the images have been re-labelled into one of 8 emotion types: neutral, happiness, surprise, sadness, anger, disgust, fear, and contempt.
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The Expression in-the-Wild (ExpW) dataset is for facial expression recognition and contains 91,793 faces manually labeled with expressions. Each of the face images is annotated as one of the seven basic expression categories: “angry”, “disgust”, “fear”, “happy”, “sad”, “surprise”, or “neutral”.
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The Radboud Faces Database (RaFD) is a set of pictures of 67 models (both adult and children, males and females) displaying 8 emotional expressions.
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4DFAB is a large scale database of dynamic high-resolution 3D faces which consists of recordings of 180 subjects captured in four different sessions spanning over a five-year period (2012 - 2017), resulting The database can be used for both face and facial expression recognition, as well as behavioural biometrics. It can also be used to learn very powerful blendshapes for parametrising facial behaviour.
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FERG is a database of cartoon characters with annotated facial expressions containing 55,769 annotated face images of six characters.
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The Real-world Affective Faces Database (RAF-DB) is a dataset for facial expression. It contains 29672 facial images tagged with basic or compound expressions by 40 independent taggers.
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…Camera-face distance is about 60 cm. Subjects were asked to make a facial expression according to an expression example shown in picture sequences.
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This dataset consists of 600+ items of faces with different emotions and mixed races that are ready to use for optimizing the accuracy of computer vision models.
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