{"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/a-compact-embedding-for-facial-expression","title":"A Compact Embedding for Facial Expression Similarity","arxiv_id":"1811.11283","date":"2018-11-27","proceeding":"CVPR 2019 6","authors":["Raviteja Vemulapalli","Aseem Agarwala"],"abstract":"Most of the existing work on automatic facial expression analysis focuses on\ndiscrete emotion recognition, or facial action unit detection. However, facial\nexpressions do not always fall neatly into pre-defined semantic categories.\nAlso, the similarity between expressions measured in the action unit space need\nnot correspond to how humans perceive expression similarity. Different from\nprevious work, our goal is to describe facial expressions in a continuous\nfashion using a compact embedding space that mimics human visual preferences.\nTo achieve this goal, we collect a large-scale faces-in-the-wild dataset with\nhuman annotations in the form: Expressions A and B are visually more similar\nwhen compared to expression C, and use this dataset to train a neural network\nthat produces a compact (16-dimensional) expression embedding. We\nexperimentally demonstrate that the learned embedding can be successfully used\nfor various applications such as expression retrieval, photo album\nsummarization, and emotion recognition. We also show that the embedding learned\nusing the proposed dataset performs better than several other embeddings\nlearned using existing emotion or action unit datasets.","url_abs":"http://arxiv.org/abs/1811.11283v2","url_pdf":"http://arxiv.org/pdf/1811.11283v2.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":"a-compact-embedding-for-facial-expression","repo_url":"https://github.com/AmirSh15/FECNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"a-compact-embedding-for-facial-expression","repo_url":"https://github.com/GerardLiu96/FECNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"action-unit-detection","task_name":"Action Unit Detection"},{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"facial-expression-recognition","task_name":"Facial Expression Recognition (FER)"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.11283","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.11283"}},"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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