2013 IEEE International Conference on Computer Vision 2013
DOI: 10.1109/iccv.2013.397
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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 … Show more

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Cited by 100 publications
(94 citation statements)
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References 27 publications
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“…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.…”
Section: Sun Memorability Datasetsupporting
confidence: 68%
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“…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.…”
Section: Sun Memorability Datasetsupporting
confidence: 68%
“…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.…”
Section: Memnet: Cnn For Memorabilitymentioning
confidence: 99%
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“…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].…”
Section: B Learning the Selection Of Componentsmentioning
confidence: 96%