{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T10:25:21Z","timestamp":1780482321977,"version":"3.54.1"},"reference-count":39,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T00:00:00Z","timestamp":1603756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Holtek Semiconductor (Taiwan) Inc.","award":["108K0007"],"award-info":[{"award-number":["108K0007"]}]},{"name":"Ministry of Science and Technology, Taiwan, R.O.C.","award":["MOST 107-2221-E-003-024-MY3 and MOST 109-2634-F-003-006"],"award-info":[{"award-number":["MOST 107-2221-E-003-024-MY3 and MOST 109-2634-F-003-006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Despite considerable progress in face recognition technology in recent years, deep learning (DL) and convolutional neural networks (CNN) have revealed commendable recognition effects with the advent of artificial intelligence and big data. FaceNet was presented in 2015 and is able to significantly improve the accuracy of face recognition, while also being powerfully built to counteract several common issues, such as occlusion, blur, illumination change, and different angles of head pose. However, not all hardware can sustain the heavy computing load in the execution of the FaceNet model. In applications in the security industry, lightweight and efficient face recognition are two key points for facilitating the deployment of DL and CNN models directly in field devices, due to their limited edge computing capability and low equipment cost. To this end, this paper provides a lightweight learning network improved from FaceNet, which is called FN13, to break through the hardware limitation of constrained computational resources. The proposed FN13 takes the advantage of center loss to reduce the variations of the between-class features and enlarge the difference of the within-class features, instead of the triplet loss by using FaceNet. The resulting model reduces the number of parameters and maintains a high degree of accuracy, only requiring few grayscale reference images per subject. The validity of FN13 is demonstrated by conducting experiments on the Labeled Faces in the Wild (LFW) dataset, as well as an analytical discussion regarding specific disguise problems.<\/jats:p>","DOI":"10.3390\/s20216114","type":"journal-article","created":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T09:22:45Z","timestamp":1603790565000},"page":"6114","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Lightweight and Resource-Constrained Learning Network for Face Recognition with Performance Optimization"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4527-9730","authenticated-orcid":false,"given":"Hsiao-Chi","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, Fu Jen Catholic University, No. 510, Zhongzheng Road, Xinzhuang District, New Taipei City 242, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zong-Yue","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taiwan Normal University, No 162, Sec. 1, Heping East Road, Da\u2019an District, Taipei City 106, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0795-0490","authenticated-orcid":false,"given":"Hsin-Han","family":"Chiang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taiwan Normal University, No 162, Sec. 1, Heping East Road, Da\u2019an District, Taipei City 106, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jose, E., Greeshma, M., TP, M.H., and Supriya, M.H. 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