Face Painting: querying art with photos

Elliot Crowley, Omkar M. Parkhi, Andrew Zisserman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We study the problem of matching photos of a person to paintings of that person, in order to retrieve similar paintings given a query photo. This is challenging as paintings span many media (oil, ink, watercolor) and can vary tremendously in style (caricature, pop art, minimalist). We make the following contributions: (i) we show that, depending on the face representation used, performance can be improved substantially by learning – either by a linear projection matrix common across identities, or by a per-identity classifier. We compare Fisher Vector and Convolutional Neural Network representations for this task; (ii) we introduce new datasets for learning and evaluating this problem; (iii) we also consider the reverse problem of retrieving photos from a large corpus given a painting; and finally, (iv) using the learnt descriptors, we show that, given a photo of a person, we are able to find their doppelgänger in a large dataset of oil paintings, and how this result can be varied by modifying attributes (e.g. frowning, old looking).
Original languageEnglish
Title of host publicationBritish Machine Vision Conference, 2015
Publication statusPublished - 2015

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