Mirror Mirror: Crowdsourcing Better Portraits
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.
Jun-Yan Zhu, Aseem Agarwala, Alexei A. Efros, Eli Shechtman, and Jue Wang. "Mirror Mirror: Crowdsourcing Better Portraits". ACM Transactions on Graphics (SIGGRAPH Asia 2014), 33(6), November 2014.