{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-facial-expression-recognition-a-survey","title":"Deep Facial Expression Recognition: A Survey","arxiv_id":"1804.08348","date":"2018-04-23","proceeding":null,"authors":["Shan Li","Weihong Deng"],"abstract":"With the transition of facial expression recognition (FER) from\nlaboratory-controlled to challenging in-the-wild conditions and the recent\nsuccess of deep learning techniques in various fields, deep neural networks\nhave increasingly been leveraged to learn discriminative representations for\nautomatic FER. Recent deep FER systems generally focus on two important issues:\noverfitting caused by a lack of sufficient training data and\nexpression-unrelated variations, such as illumination, head pose and identity\nbias. In this paper, we provide a comprehensive survey on deep FER, including\ndatasets and algorithms that provide insights into these intrinsic problems.\nFirst, we describe the standard pipeline of a deep FER system with the related\nbackground knowledge and suggestions of applicable implementations for each\nstage. We then introduce the available datasets that are widely used in the\nliterature and provide accepted data selection and evaluation principles for\nthese datasets. For the state of the art in deep FER, we review existing novel\ndeep neural networks and related training strategies that are designed for FER\nbased on both static images and dynamic image sequences, and discuss their\nadvantages and limitations. Competitive performances on widely used benchmarks\nare also summarized in this section. We then extend our survey to additional\nrelated issues and application scenarios. Finally, we review the remaining\nchallenges and corresponding opportunities in this field as well as future\ndirections for the design of robust deep FER systems.","url_abs":"http://arxiv.org/abs/1804.08348v2","url_pdf":"http://arxiv.org/pdf/1804.08348v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-facial-expression-recognition-a-survey","repo_url":"https://github.com/AmrElsersy/Emotions-Recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"deep-facial-expression-recognition-a-survey","repo_url":"https://github.com/NPilis/Facial-Expression-Recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"deep-facial-expression-recognition-a-survey","repo_url":"https://github.com/anshu123priya/Real-v-s-Fake-Emotion-Challenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"deep-facial-expression-recognition-a-survey","repo_url":"https://github.com/anshu123priya/Real-vs-Fake-Emotion-Challenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"deep-facial-expression-recognition-a-survey","repo_url":"https://github.com/cem8301/EmotionDetector","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"deep-facial-expression-recognition-a-survey","repo_url":"https://github.com/yijiazh/DFER_Summer2019","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"facial-expression-recognition-1","task_name":"Facial Expression Recognition"},{"task_slug":"facial-expression-recognition","task_name":"Facial Expression Recognition (FER)"},{"task_slug":"survey","task_name":"Survey"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1804.08348","atlas_url":"https://app.syntology.ai/?focus=1804.08348","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}