{"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/feratt-facial-expression-recognition-with","title":"FERAtt: Facial Expression Recognition with Attention Net","arxiv_id":"1902.03284","date":"2019-02-08","proceeding":null,"authors":["Pedro D. Marrero Fernandez","Fidel A. Guerrero Peña","Tsang Ing Ren","Alexandre Cunha"],"abstract":"We present a new end-to-end network architecture for facial expression\nrecognition with an attention model. It focuses attention in the human face and\nuses a Gaussian space representation for expression recognition. We devise this\narchitecture based on two fundamental complementary components: (1) facial\nimage correction and attention and (2) facial expression representation and\nclassification. The first component uses an encoder-decoder style network and a\nconvolutional feature extractor that are pixel-wise multiplied to obtain a\nfeature attention map. The second component is responsible for obtaining an\nembedded representation and classification of the facial expression. We propose\na loss function that creates a Gaussian structure on the representation space.\nTo demonstrate the proposed method, we create two larger and more comprehensive\nsynthetic datasets using the traditional BU3DFE and CK+ facial datasets. We\ncompared results with the PreActResNet18 baseline. Our experiments on these\ndatasets have shown the superiority of our approach in recognizing facial\nexpressions.","url_abs":"http://arxiv.org/abs/1902.03284v1","url_pdf":"http://arxiv.org/pdf/1902.03284v1.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":"feratt-facial-expression-recognition-with","repo_url":"https://github.com/pedrodiamel/ferattention","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"facial-expression-recognition-1","task_name":"Facial Expression Recognition"},{"task_slug":"facial-expression-recognition","task_name":"Facial Expression Recognition (FER)"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}