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- W2095233599 abstract "We describe a method for providing feedback on portrait expressions, and for selecting the most attractive expressions from large video/photo collections. We capture a video of a subject's face while they are engaged in a task designed to elicit a range of positive emotions. We then use crowdsourcing to score the captured expressions for their attractiveness. We use these scores to train a model that can automatically predict attractiveness of different expressions of a given person. We also train a cross-subject model that evaluates portrait attractiveness of novel subjects and show how it can be used to automatically mine attractive photos from personal photo collections. Furthermore, we show how, with a little bit ($5-worth) of extra crowdsourcing, we can substantially improve the cross-subject model by fine-tuning it to a new individual using active learning. Finally, we demonstrate a training app that helps people learn how to mimic their best expressions." @default.
- W2095233599 created "2016-06-24" @default.
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- W2095233599 date "2014-11-19" @default.
- W2095233599 modified "2023-10-18" @default.
- W2095233599 title "Mirror mirror" @default.
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- W2095233599 doi "https://doi.org/10.1145/2661229.2661287" @default.
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