Modifying the Memorability of Face Photographs

Abstract

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. Additional information, data and supplemental material is available at: http://facemem

Understanding and Predicting Image Memorability at a Large Scale

“…This demonstrates the strength of the deep features as shown by a variety of other works. Similar to [15], we observe that the performance increases significantly when accounting for false alarms. Apart from high performance on the SUN Memorability dataset, the features learned by CNNs generalize well to the larger LaMem dataset.…”

“…Then, we combine features in a spatial pyramid [22] resulting in a feature of dimension 5376. This is the best performing feature for predicting memorability as reported by various previous works [15,18,13]. For both HOG2x2 and features from CNNs, we train a linear Support Vector Regression machine [8,6] to predict memorability.…”

“…We used validation data to find the best B and C hyperparameters 3 . As proposed in [15], we evaluate two notions of memorability -one that does not account for false alarms (no FA), and one that does (with FA). It can be important to account for false alarms to reduce the noise in the signal as people may remember some images simply because they are familiar, but not memorable.…”

“…These results suggest that memorable and forgettable images have different intrinsic visual features, making some information easier to remember than others. 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. However, to date, experiments and models for predicting visual memorability have been limited to very small datasets and specific image domains.…”

Data-Driven Synthesis of Cartoon Faces Using Different Styles

“…Previous works all focused on enhancing the attractiveness of real faces [35], [36]. In the contrast, studies on enhancement of facial attractiveness of cartoons have not yet been reported, even though the neural responses related to the evaluation of the attractiveness of cartoon faces have been investigated [37].…”