{"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/deep-learning-for-smile-recognition","title":"Deep Learning For Smile Recognition","arxiv_id":"1602.00172","date":"2016-01-30","proceeding":null,"authors":["Patrick O. Glauner"],"abstract":"Inspired by recent successes of deep learning in computer vision, we propose\na novel application of deep convolutional neural networks to facial expression\nrecognition, in particular smile recognition. A smile recognition test accuracy\nof 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action\n(DISFA) database, significantly outperforming existing approaches based on\nhand-crafted features with accuracies ranging from 65.55% to 79.67%. The\nnovelty of this approach includes a comprehensive model selection of the\narchitecture parameters, allowing to find an appropriate architecture for each\nexpression such as smile. This is feasible because all experiments were run on\na Tesla K40c GPU, allowing a speedup of factor 10 over traditional computations\non a CPU.","url_abs":"http://arxiv.org/abs/1602.00172v2","url_pdf":"http://arxiv.org/pdf/1602.00172v2.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":[],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"facial-expression-recognition-1","task_name":"Facial Expression Recognition"},{"task_slug":"facial-expression-recognition","task_name":"Facial Expression Recognition (FER)"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"model-selection","task_name":"Model Selection"},{"task_slug":"smile-recognition","task_name":"Smile Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/smile-recognition-on-disfa","task":"Smile Recognition","dataset":"DISFA","model":"Deep CNN","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"99.45%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}