Modifying the Memorability of Face Photographs
Contemporary life bombards us with many new images of faces every day, which poses non-trivial constraints on human memory. The vast majority of face photographs are intended to be remembered, either because of personal relevance, commercial interests or because the pictures were deliberately designed to be memorable. Can we make a portrait more memorable or more forgettable automatically? Here, we provide a method to modify the memorability of individual face photographs, while keeping the identity and other facial traits (e.g. age, attractiveness, and emotional magnitude) of the individual fixed. We show that face photographs manipulated to be more memorable (or more forgettable) are indeed more often remembered (or forgotten) in a crowd-sourcing experiment with an accuracy of 74%. Quantifying and modifying the `memorability' of a face lends itself to many useful applications in computer vision and graphics, such as mnemonic aids for learning, photo editing applications for social networks and tools for designing memorable advertisements.
Code & Data
10k US Adult Faces Database (Coming soon)
Visual Memory Game
The Intrinsic Memorability of Face Photographs, Wilma A. Bainbridge, Phillip Isola, Aude Oliva. In Journal of Experimental Psychology: General (JEPG), 2013
Memorability of Image Regions, Aditya Khosla, Jianxiong Xiao, Antonio Torralba, Aude Oliva. In Neural Information Processing Systems (NIPS), 2012
Image Memorability and Visual Inception, Aditya Khosla, Jianxiong Xiao, Phillip Isola, Antonio Torralba, Aude Oliva. In SIGGRAPH Asia, 2012 (invited paper, technique briefs section)
Understanding the Intrinsic Memorability of Images, Phillip Isola, Devi Parikh, Antonio Torralba, Aude Oliva. In Advances in Neural Information Processing Systems (NIPS), 2011
What makes an image memorable?, Phillip Isola, Jianxiong Xiao, Antonio Torralba, Aude Oliva. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011
We thank Zoya Bylinskii, Phillip Isola and the reviewers for helpful discussions. This work is funded by a Xerox research award to A.O, Google research awards to A.O and A.T, and ONR MURI N000141010933 to A.T. W.A.B is supported by an NDSEG Fellowship. A.K. is supported by a Facebook Fellowship.
For comments and questions, please contact Aditya Khosla.