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Then, we compute a number of features and train a model to predict the average rating of a video frame. We evaluate our model with cross-validation, and show that it is better able to select quality still frames than previous techniques, such as simply omitting frames that contain blinking or motion blur, or selecting only smiles. We also evaluate our technique qualitatively on videos that were not part of our validation set, and were taken outdoors and under different lighting conditions.<\/jats:p>","DOI":"10.1145\/2070781.2024162","type":"journal-article","created":{"date-parts":[[2011,11,30]],"date-time":"2011-11-30T13:58:46Z","timestamp":1322661526000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Candid portrait selection from video"],"prefix":"10.1145","volume":"30","author":[{"given":"Juliet","family":"Fiss","sequence":"first","affiliation":[{"name":"University of Washington"}]},{"given":"Aseem","family":"Agarwala","sequence":"additional","affiliation":[{"name":"Adobe Systems, Inc."}]},{"given":"Brian","family":"Curless","sequence":"additional","affiliation":[{"name":"University of Washington"}]}],"member":"320","published-online":{"date-parts":[[2011,12,12]]},"reference":[{"volume-title":"Proc. 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