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The employed methods for movies summarization fail to satisfy the user\u2019s requirements due to the subjective nature of movies data. Therefore, in this paper, we present a user\u2010preference based movie summarization scheme. First, we segmented movie into shots using a novel entropy\u2010based shots segmentation mechanism. Next, temporal saliency of shots is computed, resulting in highly salient shots in which character faces are detected. The resultant shots are then forward propagated to our trained deep CNN model for facial expression recognition (FER) to analyze the emotional state of the characters. The final summary is generated based on user\u2010preferred emotional moments from the seven emotions, i.e., afraid, angry, disgust, happy, neutral, sad, and surprise. The subjective evaluation over five Hollywood movies proves the effectiveness of our proposed scheme in terms of user satisfaction. Furthermore, the objective evaluation verifies the superiority of the proposed scheme over state\u2010of\u2010the\u2010art movie summarization methods.<\/jats:p>","DOI":"10.1155\/2019\/3581419","type":"journal-article","created":{"date-parts":[[2019,5,6]],"date-time":"2019-05-06T15:00:27Z","timestamp":1557154827000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Personalized Movie Summarization Using Deep CNN\u2010Assisted Facial Expression Recognition"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8201-7372","authenticated-orcid":false,"given":"Ijaz","family":"Ul Haq","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7538-2689","authenticated-orcid":false,"given":"Amin","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4055-7412","authenticated-orcid":false,"given":"Khan","family":"Muhammad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8139-7091","authenticated-orcid":false,"given":"Mi Young","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6678-7788","authenticated-orcid":false,"given":"Sung Wook","family":"Baik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,5,5]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2004.841694"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2007.890857"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2008.2009684"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2009.2030740"},{"key":"e_1_2_8_5_2","doi-asserted-by":"crossref","unstructured":"SangJ.andXuC. 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