Emotion recognition and understanding is a vital componentin human-machine interaction. Dimensional models of affectsuch as those using valence and arousal have advantages overtraditional categorical ones due to the complexity of emo-tional states in humans. However, dimensional emotion an-notations are difficult and expensive to collect, therefore theyare still limited in the affective computing community. To ad-dress these issues, we propose a method to generate syntheticimages from existing categorical emotion datasets using facemorphing, with full control over the resulting sample distri-bution as well as dimensional labels in the circumplex space  , while achieving augmentation factors of at least 20x or more.
Emotion recognition and understanding is a vital component in human-machine interaction. Dimensional models of affect such as those using valence and arousal have advantages over traditional categorical ones due to the complexity of emotional states in humans. However, dimensional emotion annotations are difficult and expensive to collect, therefore they are not as prevalent in the affective computing community. To address these issues, we propose a method to generate synthetic images from existing categorical emotion datasets using face morphing as well as dimensional labels in the circumplex space with full control over the resulting sample distribution , while achieving augmentation factors of at least 20x or more.
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componentin
component in
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affectsuch
affect such
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overtraditional
over traditional
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emo-tional
emotional
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an-notations
annotations
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theyare still limited
they are not as prevalent
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ad-dress
address
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syntheticimages
synthetic images
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facemorphing, with full control over the resulting sample distri-bution
face morphing
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with full control over the resulting sample distribution