{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T13:20:42Z","timestamp":1778592042937,"version":"3.51.4"},"reference-count":47,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,23]],"date-time":"2019-09-23T00:00:00Z","timestamp":1569196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China;Shenzhen Fundamental Research fund;the National Science Foundation of China","award":["2017YFB0802300;JCYJ20180305125822769;61703077"],"award-info":[{"award-number":["2017YFB0802300;JCYJ20180305125822769;61703077"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Face recognition using depth data has attracted increasing attention from both academia and industry in the past five years. Previous works show a huge performance gap between high-quality and low-quality depth data. Due to the lack of databases and reasonable evaluations on data quality, very few researchers have focused on boosting depth-based face recognition by enhancing data quality or feature representation. In the paper, we carefully collect a new database including high-quality 3D shapes, low-quality depth images and the corresponding color images of the faces of 902 subjects, which have long been missing in the area. With the database, we make a standard evaluation protocol and propose three strategies to train low-quality depth-based face recognition models with the help of high-quality depth data. Our training strategies could serve as baselines for future research, and their feasibility of boosting low-quality depth-based face recognition is validated by extensive experiments.<\/jats:p>","DOI":"10.3390\/s19194124","type":"journal-article","created":{"date-parts":[[2019,9,25]],"date-time":"2019-09-25T03:51:18Z","timestamp":1569383478000},"page":"4124","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Boosting Depth-Based Face Recognition from a Quality Perspective"],"prefix":"10.3390","volume":"19","author":[{"given":"Zhenguo","family":"Hu","sequence":"first","affiliation":[{"name":"College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Penghui","family":"Gui","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziqing","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qijun","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keren","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1493-5352","authenticated-orcid":false,"given":"Feng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Xueyuan avenue, nanshan district, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengxi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/TPAMI.2009.14","article-title":"3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure","volume":"32","author":"Queirolo","year":"2010","journal-title":"IEEE Trans. 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