{"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/predicting-personal-traits-from-facial-images","title":"Predicting Personal Traits from Facial Images using Convolutional Neural Networks Augmented with Facial Landmark Information","arxiv_id":"1605.09062","date":"2016-05-29","proceeding":null,"authors":["Yoad Lewenberg","Yoram Bachrach","Sukrit Shankar","Antonio Criminisi"],"abstract":"We consider the task of predicting various traits of a person given an image\nof their face. We estimate both objective traits, such as gender, ethnicity and\nhair-color; as well as subjective traits, such as the emotion a person\nexpresses or whether he is humorous or attractive. For sizeable\nexperimentation, we contribute a new Face Attributes Dataset (FAD), having\nroughly 200,000 attribute labels for the above traits, for over 10,000 facial\nimages. Due to the recent surge of research on Deep Convolutional Neural\nNetworks (CNNs), we begin by using a CNN architecture for estimating facial\nattributes and show that they indeed provide an impressive baseline\nperformance. To further improve performance, we propose a novel approach that\nincorporates facial landmark information for input images as an additional\nchannel, helping the CNN learn better attribute-specific features so that the\nlandmarks across various training images hold correspondence. We empirically\nanalyse the performance of our method, showing consistent improvement over the\nbaseline across traits.","url_abs":"http://arxiv.org/abs/1605.09062v1","url_pdf":"http://arxiv.org/pdf/1605.09062v1.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":"attribute","task_name":"Attribute"},{"task_slug":"fad","task_name":"FAD"}],"methods":[],"datasets_introduced":[{"slug":"fad","name":"FAD","full_name":"Face Attributes Dataset"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}