{"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/do-deep-neural-networks-learn-facial-action","title":"Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?","arxiv_id":"1510.02969","date":"2015-10-10","proceeding":null,"authors":["Pooya Khorrami","Tom Le Paine","Thomas S. Huang"],"abstract":"Despite being the appearance-based classifier of choice in recent years,\nrelatively few works have examined how much convolutional neural networks\n(CNNs) can improve performance on accepted expression recognition benchmarks\nand, more importantly, examine what it is they actually learn. In this work,\nnot only do we show that CNNs can achieve strong performance, but we also\nintroduce an approach to decipher which portions of the face influence the\nCNN's predictions. First, we train a zero-bias CNN on facial expression data\nand achieve, to our knowledge, state-of-the-art performance on two expression\nrecognition benchmarks: the extended Cohn-Kanade (CK+) dataset and the Toronto\nFace Dataset (TFD). We then qualitatively analyze the network by visualizing\nthe spatial patterns that maximally excite different neurons in the\nconvolutional layers and show how they resemble Facial Action Units (FAUs).\nFinally, we use the FAU labels provided in the CK+ dataset to verify that the\nFAUs observed in our filter visualizations indeed align with the subject's\nfacial movements.","url_abs":"http://arxiv.org/abs/1510.02969v3","url_pdf":"http://arxiv.org/pdf/1510.02969v3.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":"do-deep-neural-networks-learn-facial-action","repo_url":"https://github.com/SJHNJU/facial_expression_recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1510.02969","atlas_url":"https://app.syntology.ai/?focus=1510.02969","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}