{"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/facial-emotion-recognition-a-multi-task","title":"Facial Emotion Recognition: A multi-task approach using deep learning","arxiv_id":"2110.15028","date":"2021-10-28","proceeding":null,"authors":["Aakash Saroop","Pathik Ghugare","Sashank Mathamsetty","Vaibhav Vasani"],"abstract":"Facial Emotion Recognition is an inherently difficult problem, due to vast differences in facial structures of individuals and ambiguity in the emotion displayed by a person. Recently, a lot of work is being done in the field of Facial Emotion Recognition, and the performance of the CNNs for this task has been inferior compared to the results achieved by CNNs in other fields like Object detection, Facial recognition etc. In this paper, we propose a multi-task learning algorithm, in which a single CNN detects gender, age and race of the subject along with their emotion. We validate this proposed methodology using two datasets containing real-world images. The results show that this approach is significantly better than the current State of the art algorithms for this task.","url_abs":"https://arxiv.org/abs/2110.15028v1","url_pdf":"https://arxiv.org/pdf/2110.15028v1.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":"facial-emotion-recognition-a-multi-task","repo_url":"https://github.com/sashankmvv/Emotion-recognition","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"facial-emotion-recognition","task_name":"Facial Emotion Recognition"},{"task_slug":"facial-expression-recognition","task_name":"Facial Expression Recognition (FER)"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"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":"Multi Label Output","rank_in_archive_order":2,"of":2,"metrics":{"Accuracy":"79.26%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}