{"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/training-deep-networks-for-facial-expression","title":"Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution","arxiv_id":"1608.01041","date":"2016-08-03","proceeding":null,"authors":["Emad Barsoum","Cha Zhang","Cristian Canton Ferrer","Zhengyou Zhang"],"abstract":"Crowd sourcing has become a widely adopted scheme to collect ground truth\nlabels. However, it is a well-known problem that these labels can be very\nnoisy. In this paper, we demonstrate how to learn a deep convolutional neural\nnetwork (DCNN) from noisy labels, using facial expression recognition as an\nexample. More specifically, we have 10 taggers to label each input image, and\ncompare four different approaches to utilizing the multiple labels: majority\nvoting, multi-label learning, probabilistic label drawing, and cross-entropy\nloss. We show that the traditional majority voting scheme does not perform as\nwell as the last two approaches that fully leverage the label distribution. An\nenhanced FER+ data set with multiple labels for each face image will also be\nshared with the research community.","url_abs":"http://arxiv.org/abs/1608.01041v2","url_pdf":"http://arxiv.org/pdf/1608.01041v2.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":"training-deep-networks-for-facial-expression","repo_url":"https://github.com/Microsoft/FERPlus","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"training-deep-networks-for-facial-expression","repo_url":"https://github.com/NathanHouwaart/EmotionRecognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"training-deep-networks-for-facial-expression","repo_url":"https://github.com/holman57/AI_Emotion_Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"training-deep-networks-for-facial-expression","repo_url":"https://github.com/modelhub-ai/emotion-fer-plus","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"training-deep-networks-for-facial-expression","repo_url":"https://github.com/prakharbhardwaj1/emotion-recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"training-deep-networks-for-facial-expression","repo_url":"https://github.com/vicksam/fer-model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"training-deep-networks-for-facial-expression","repo_url":"https://github.com/wonsu0513/ros2_foxy_facial_expressions","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","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":"multi-label-learning","task_name":"Multi-Label Learning"}],"methods":[],"datasets_introduced":[{"slug":"fer","name":"FER+","full_name":"Face Expression Recognition Plus dataset"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1608.01041","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1608.01041"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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