{ "ok": true, "doi": "10.46243/jst.2021.v6.i04.pp383-387", "doi_display": "10.46243/jst.2021.v6.i04.pp383-387", "doi_url": "https://doi.org/10.46243/jst.2021.v6.i04.pp383-387", "state": "registered", "url": "https://www.jst.org.in/index.php/pub/article/view/675", "title": "Facial Recognition Attendance System", "version": 2, "registered_via": "crossref", "prefix": { "prefix": "10.46243", "status": "live" }, "registrant": { "name": "Longman Publishers", "kind": "publisher", "country": "India" }, "reserved_at": null, "registered_at": "2026-09-29 22:00:37", "updated_at": "2026-09-29 23:59:57", "withdrawn_at": null, "withdrawn_reason": null, "record": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2021.v6.i04.pp383-387", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "Facial Recognition Attendance System", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2021.v6.i04.pp383-387" } ], "agents": [ { "role": "author", "name": { "given": "Ayush", "family": "Chirde" }, "sequence": "first" }, { "role": "author", "name": { "given": "Payal", "family": "Kamthe" }, "sequence": "additional" }, { "role": "author", "name": { "given": "Aishwarya", "family": "Somvanshi" }, "sequence": "additional" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2021-08-16", "date_type": "PublicationDate", "online": "2021-08-16" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "06", "issue": "01", "pages": { "first": "383", "last": "387" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/675", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/675/604", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/675/2735", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://www.jst.org.in/index.php/pub/article/view/675/604", "purpose": "similarity-checking" } ], "abstract": { "value": "In this digital era, face recognition system plays a very important role in nearly every sector. Face recognition is one of the mostly used natural science. it'll used for security, authentication, identification, and has got a lot of blessings. Despite of obtaining low accuracy once compared to iris recognition and fingerprint recognition, it is being wide used due to its contactless and non-invasive technique. what's a lot of, face recognition system can even be used for attending marking in colleges, colleges, offices, etc. This system aims to make a class attending system that uses the thought of face recognition as existing manual attending system is time overwhelming and cumbersome to stay up. And there's conjointly prospects of proxy attending. Thus, the requirement for this technique can increase. this technique consists of four phases- data creation, face detection, face recognition, attending updating. Data is created by the pictures of the students in class. Face detection and recognition is performed exploitation Haar-Cascade classifier and native Binary Pattern chart algorithmic program severally. Faces unit detected and recognized from live streaming video of the room. attending are armored to the individual faculty at the tip of the session. it's standard that marking attending of the scholars is associate degree obligatory half in academe. standard technique of marking the attending is being followed by numerous establishments and Universities with several manual interventions. to scale back time consumption and human effort, the employment of associate degree automatic method of marking attending supported image process may be implemented. Authors have projected a sensible attending observance system through face detection and recognition techniques supported their face expression. a group of pictures of the scholars are antecedently fed to the system against that the live pictures of the scholars are compared and attending would be recorded supported facial characteristics. The projected approach uses CNN rule for coaching the pictures and LBPH visual descriptor for image classification. This models are going to be capable of providing higher degree of accuracy compared to already existing literature work. Authors have compared their experimental results with the present approaches and located satisfactory", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0", "start": "2021-08-16", "applies_to": "vor" }, "references": [ { "key": "ref1", "unstructured": "K. R. Gutierrez, D. C. Perez, J. O. Mercado, M. N. Miyatake, and Hector Perez-Meana, A face" }, { "key": "ref2", "unstructured": "recognition algorithmic rule exploitation chemist phases and bar chart exploit, International Journal" }, { "key": "ref3", "doi": "10.25291/vr/34-vr-41", "unstructured": "of Computers, Vol. 5, No.1,pp.34-41,2011" }, { "key": "ref4", "unstructured": "Bromby, Michael C., At Face Value (February 28, 2003). New Law Journal Expert Witness" }, { "key": "ref5", "doi": "10.1055/s-2003-45513", "unstructured": "Supplement, pp. 301-303, February 28, 2003" }, { "key": "ref6", "unstructured": "Vytautas Perlibakas, ”Face Recognition using Principal Component Analysis and Log-Gabor Filters”, March 2005. 23 pages" }, { "key": "ref7", "unstructured": "Kyungnam Kim, ”Face recognition usi ng principal component analysis”, International Conference on Computer Vision and pattern recognition, 1998" }, { "key": "ref8", "doi": "10.1016/j.patcog.2006.12.002", "unstructured": "Zhang, Xiaoxun & Jia, Yunde. (2007). A linear discriminant analysis framework based on a random subspace for face recognition. Pattern Recognition. 40. 2585-2591. 10.1016/j.patcog.2006.12.002" }, { "key": "ref9", "unstructured": "Changjun Zhou, Xiaopeng Wei, Qiang Zhang, Xiaoyong Fang,” Fisher's linear discriminant (FLD) and support vector machine (SVM) in non-negative matrix factorization (NMF) residual space for face recognition”, Optica Applicata, Vol. XL, No. 3, 2010" }, { "key": "ref10", "doi": "10.1109/ictss.2014.7013165", "unstructured": "Adrian Rhesa Septian Siswanto, Anto Satriyo Nugroho, Maulahikmah Galinium, Implementation of Face Recognition Algorithm for Biometrics-Based Time Attendance System, Bandung, Indonesia, 19 January 2015" }, { "key": "ref11", "doi": "10.7763/ijcce.2012.v1.28", "unstructured": "Nirmalya Kar, Mrinal Kanti Debbarma, Ashim Saha, and Dwijen Rudra Pal, ”Study of Implementing Automated Attendance System Using Face Recognition Techniques”, International Journal of Computer and Communication Engineering, Vol. 1, No. 2, July 2012" } ], "record": { "registrant": "Longman Publishers", "registered": "2026-09-10", "updated": "2026-09-20", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "record_sha256": "98f70b434d6f903a58d5a51ade0c539e21fc2e7a05bf5ccc43f78f65606eaca8", "handle": { "synced_at": "2026-10-01 18:13:50", "url": "https://www.jst.org.in/index.php/pub/article/view/675" }, "links": { "record_page": "https://registry.smartscholars.in/record.php?doi=10.46243%2Fjst.2021.v6.i04.pp383-387", "system_metadata": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2021.v6.i04.pp383-387&as=system", "history": "https://registry.smartscholars.in/api.php?action=history&doi=10.46243%2Fjst.2021.v6.i04.pp383-387", "kernel_xml": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2021.v6.i04.pp383-387&as=xml" } }