Kernel Metadata Declaration — on demand

What the DOI identifies

Efficient Face Features Extraction and Recognition Using Principal Component Analysis

10.46243/jst.2021.v6.i04.pp251-257 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2021-08-29

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 06 · no. 04 · pp. 251–257

Principal agents

• Dr. R. Pradeep Kumar Reddy (author → Author)
• Dr. S. Kiran (author → Author)
• Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 4 links, 15 references. Source: Crossref (member 25296), registered 2026-09-08, last deposited 2026-09-20.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

The record

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        "value": "Face recognition is a common issue in artificial intelligence. This program was widely used in our daily lives. Several smart phones used facial recognition to open them. Face identification system is intended to protect personal information. When Face book people appear in photos, you may instantly recognize them. Face recognition has already been tackled in a number of ways. Until recently, it has been recommended, but it is still quite tough in the real world Circumstances. A key strategy for distinguishing persons is based on under a variety of conditions, such as partial facial blockage, lighting, and a wide range of postures. The goal of this paper is to create a face recognition system using a machine learning system. A method named principal component analysis (PCA) has been developed to recognize faces. Furthermore, it has been successful tested with 97 percent recognition accuracy by using PCA.",
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            "unstructured": "W. Y. Min, E. Romanova, Y. Lisovec and A. M. San, \"Application of Statistical Data Processing for Solving the Problem of Face Recognition by Using Principal Components Analysis Method,\" 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Saint Petersburg and Moscow, Russia, 2019, pp. 2208-2212"
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            "unstructured": "F. Mahmud, M. T. Khatun, S. T. Zuhori, S. Afroge, M. Aktar and B. Pal, \"Face recognition using Principal Component Analysis and Linear Discriminant Analysis,\" 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), Dhaka, 2015, pp. 1-4. doi: 10.1109/ICEEICT.2015.7307518"
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            "unstructured": "H. Dai, \"Research on SVM improved algorithm for large data classification,\" 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA), Shanghai, 2018, pp. 181-185"
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The Kernel Metadata Declaration

DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation

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