{"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/joint-estimation-of-age-and-gender-from","title":"Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile Applications","arxiv_id":"1806.02023","date":"2018-06-06","proceeding":null,"authors":["Jia-Hong Lee","Yi-Ming Chan","Ting-Yen Chen","Chu-Song Chen"],"abstract":"Automatic age and gender classification based on unconstrained images has\nbecome essential techniques on mobile devices. With limited computing power,\nhow to develop a robust system becomes a challenging task. In this paper, we\npresent an efficient convolutional neural network (CNN) called lightweight\nmulti-task CNN for simultaneous age and gender classification. Lightweight\nmulti-task CNN uses depthwise separable convolution to reduce the model size\nand save the inference time. On the public challenging Adience dataset, the\naccuracy of age and gender classification is better than baseline multi-task\nCNN methods.","url_abs":"http://arxiv.org/abs/1806.02023v1","url_pdf":"http://arxiv.org/pdf/1806.02023v1.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":"joint-estimation-of-age-and-gender-from","repo_url":"https://github.com/ivclab/agegenderLMTCNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"age-and-gender-classification","task_name":"Age And Gender Classification"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"gender-classification","task_name":"Gender Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"depthwise-convolution","method_name":"Depthwise Convolution"},{"method_slug":"depthwise-separable-convolution","method_name":"Depthwise Separable Convolution"},{"method_slug":"pointwise-convolution","method_name":"Pointwise Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/age-and-gender-classification-on-adience-age","task":"Age And Gender Classification","dataset":"Adience Age","model":"LMTCNN-2-1 (single crop, tensorflow)","rank_in_archive_order":15,"of":16,"metrics":{"Accuracy (5-fold)":"44.26"},"uses_additional_data":false},{"leaderboard":"/sota/age-and-gender-classification-on-adience","task":"Age And Gender Classification","dataset":"Adience Gender","model":"LMTCNN-2-1 (single crop, tensorflow)","rank_in_archive_order":9,"of":10,"metrics":{"Accuracy (5-fold)":"85.16"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}