{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T12:22:37Z","timestamp":1778156557250,"version":"3.51.4"},"reference-count":100,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,7]],"date-time":"2020-01-07T00:00:00Z","timestamp":1578355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2019R1F1A1058548"],"award-info":[{"award-number":["2019R1F1A1058548"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human face image analysis is an active research area within computer vision. In this paper we propose a framework for face image analysis, addressing three challenging problems of race, age, and gender recognition through face parsing. We manually labeled face images for training an end-to-end face parsing model through Deep Convolutional Neural Networks. The deep learning-based segmentation model parses a face image into seven dense classes. We use the probabilistic classification method and created probability maps for each face class. The probability maps are used as feature descriptors. We trained another Convolutional Neural Network model by extracting features from probability maps of the corresponding class for each demographic task (race, age, and gender). We perform extensive experiments on state-of-the-art datasets and obtained much better results as compared to previous results.<\/jats:p>","DOI":"10.3390\/s20020328","type":"journal-article","created":{"date-parts":[[2020,1,8]],"date-time":"2020-01-08T03:59:57Z","timestamp":1578455997000},"page":"328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0864-5255","authenticated-orcid":false,"given":"Khalil","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan"},{"name":"Intelligent Analytics Group (IAG), College of Computer, Qassim University, Al-Mulida 51431, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7237-180X","authenticated-orcid":false,"given":"Muhammad","family":"Attique","sequence":"additional","affiliation":[{"name":"Department of Software, Sejong University, Seoul 05006, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3323-2732","authenticated-orcid":false,"given":"Rehan Ullah","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer, Qassim University, Al-Mulida 51431, Saudi Arabia"},{"name":"Intelligent Analytics Group (IAG), College of Computer, Qassim University, Al-Mulida 51431, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ikram","family":"Syed","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The Superior College, Lahore 54000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tae-Sun","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Ajou University, Ajou 16499, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.soncn.2008.08.005","article-title":"Social and ethical implications of genomics, race, ethnicity, and health inequities","volume":"24","author":"Bonham","year":"2008","journal-title":"Semin. 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