Modifying the Memorability of Face Photographs

By Aditya Khosla, Wilma A. Bainbridge, Antonio Torralba, Aude Oliva. 2013

115 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

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Citations in the corpus

What makes an image popular? (2014, methodology): “Motivated by Khosla et al. [29, 27], we use various low-level computer vision features that are likely used by humans for visual processing.”
GANalyze: Toward Visual Definitions of Cognitive Image Properties (2019, methodology): “The memorability of an image, like faces, can be manipulated using warping techniques [17].”
Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images (2016, methodology): “Some target a specific domain and attribute, such as methods to enhance the “memorability” [17] or age [16] of facial photos, or to edit outdoor scenes with transient attributes like weather [22].”
Adopting Abstract Images for Semantic Scene Understanding (2016, background): “Studying the contribution of different semantic features (e.g. presence, locations, attributes, cooccurrences of objects, etc.) to memorability would go a long way in understanding memorability.”
How We've Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis (2020, methodology): “—10K US Adult Faces [82] The PPB database was the first database to explicitly annotate skin tone as a proxy for race, annotating images with two different skin tones: darker and lighter.”
Understanding and Predicting Image Memorability at a Large Scale (2015, background): “Indeed, computer vision works [12, 18, 15, 7] have been able to reliably estimate the memorability ranks of novel pictures, or faces, accounting for half of the variance in human consistency.”