{"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/covariance-pooling-for-facial-expression","title":"Covariance Pooling For Facial Expression Recognition","arxiv_id":"1805.04855","date":"2018-05-13","proceeding":null,"authors":["Dinesh Acharya","Zhiwu Huang","Danda Paudel","Luc van Gool"],"abstract":"Classifying facial expressions into different categories requires capturing\nregional distortions of facial landmarks. We believe that second-order\nstatistics such as covariance is better able to capture such distortions in\nregional facial fea- tures. In this work, we explore the benefits of using a\nman- ifold network structure for covariance pooling to improve facial\nexpression recognition. In particular, we first employ such kind of manifold\nnetworks in conjunction with tradi- tional convolutional networks for spatial\npooling within in- dividual image feature maps in an end-to-end deep learning\nmanner. By doing so, we are able to achieve a recognition accuracy of 58.14% on\nthe validation set of Static Facial Expressions in the Wild (SFEW 2.0) and\n87.0% on the vali- dation set of Real-World Affective Faces (RAF) Database.\nBoth of these results are the best results we are aware of. Besides, we\nleverage covariance pooling to capture the tem- poral evolution of per-frame\nfeatures for video-based facial expression recognition. Our reported results\ndemonstrate the advantage of pooling image-set features temporally by stacking\nthe designed manifold network of covariance pool-ing on top of convolutional\nnetwork layers.","url_abs":"http://arxiv.org/abs/1805.04855v1","url_pdf":"http://arxiv.org/pdf/1805.04855v1.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":"covariance-pooling-for-facial-expression","repo_url":"https://github.com/d-acharya/CovPoolFER","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"facial-expression-recognition-1","task_name":"Facial Expression Recognition"},{"task_slug":"facial-expression-recognition","task_name":"Facial Expression Recognition (FER)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/facial-expression-recognition-on-real-world","task":"Facial Expression Recognition (FER)","dataset":"Real-World Affective Faces","model":"Covariance Pooling","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"87.0%"},"uses_additional_data":false},{"leaderboard":"/sota/facial-expression-recognition-on-static","task":"Facial Expression Recognition (FER)","dataset":"Static Facial Expressions in the Wild","model":"Covariance Pooling","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"58.14%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.04855","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.04855"}},"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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