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Chiplunkar, \"Comparative study of machine learning techniques in sentimental analysis,\" 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, 2017, pp. 216-221, doi: 10.1109/ICICCT.2017.7975191" }, { "path": "references.12.doi", "from": null, "to": "10.1109/iceeict.2015.7307518" }, { "path": "references.12.key", "from": null, "to": "ref13" }, { "path": "references.12.unstructured", "from": null, "to": "F. Mahmud, M. T. Khatun, S. T. Zuhori, S. Afroge, M. Aktar and B. 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Milanova, \"Face recognition based on subject dependent Hidden Markov Models,\" 2016 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Varna, 2016, pp. 1-5" }, { "path": "references.4.doi", "from": null, "to": "10.1109/tsp.2018.8441452" }, { "path": "references.4.key", "from": null, "to": "ref5" }, { "path": "references.4.unstructured", "from": null, "to": "Z. B. Lahaw, D. Essaidani and H. Seddik, \"Robust Face Recognition Approaches Using PCA, ICA, LDA Based on DWT, and SVM Algorithms,\" 2018 41st International Conference on Telecommunications and Signal Processing (TSP), Athens, 2018, pp. 1- 5. doi: 10.1109/TSP.2018.8441452. [5 ] N. Sabri et al., \"A Comparison of Face Detection Classifier using Facial Geometry Distance Measure,\" 2018 9th IEEE Control and System Graduate Research Colloquium (ICSGRC), Shah Alam, Malaysia, 2018, pp. 116- 120.doi: 10.1109/ICSGRC.2018.8657592" }, { "path": "references.5.doi", "from": null, "to": "10.1109/ssd.2019.8893214" }, { "path": "references.5.key", "from": null, "to": "ref6" }, { "path": "references.5.unstructured", "from": null, "to": "A. Adouani, W. M. Ben Henia and Z. Lachiri, \"Comparison of Haarlike, HOG and LBP approaches for face detection in video sequences,\" 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD), Istanbul, Turkey, 2019, pp. 266-271. doi: 10.1109/SSD.2019.8893214" }, { "path": "references.6.doi", "from": null, "to": "10.1109/acpr.2017.123" }, { "path": "references.6.key", "from": null, "to": "ref7" }, { "path": "references.6.unstructured", "from": null, "to": "J. Fan, Q. Ye and N. 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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.", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2021-08-29", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1109/socpar.2010.5686495", "unstructured": "R. Ebrahimpour, N. Sadeghnejad, A. Amiri and A. Moshtagh, \"Low resolution face recognition using combination of diverse classifiers,\" 2010 International Conference of Soft Computing and Pattern Recognition,Paris,2010,pp.265-268.doi:10.1109/SOCPAR.2010.5686495" }, { "key": "ref2", "doi": "10.1109/iccisci.2019.8716452", "unstructured": "H. Baqeel and S. Saeed, \"Face detection authentication on Smart phones: End Users Usability Assessment Experiences,\" 2019 International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia, 2019, pp.1-6.doi: 10.1109/ICCISci.2019.8716452" }, { "key": "ref3", "doi": "10.1109/iccisci.2019.8716452", "unstructured": "P. Dinkova, P. Georgieva, A. Manolova and M. Milanova, \"Face recognition based on subject dependent Hidden Markov Models,\" 2016 IEEE International Black Sea Conference Information Sciences (ICCIS), Sakaka, Saudi Arabia, 2019, pp.1-6.doi: 10.1109/ICCISci.2019.8716452" }, { "key": "ref4", "doi": "10.1109/blackseacom.2016.7901570", "unstructured": "P. Dinkova, P. Georgieva, A. Manolova and M. Milanova, \"Face recognition based on subject dependent Hidden Markov Models,\" 2016 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Varna, 2016, pp. 1-5" }, { "key": "ref5", "doi": "10.1109/tsp.2018.8441452", "unstructured": "Z. B. Lahaw, D. Essaidani and H. Seddik, \"Robust Face Recognition Approaches Using PCA, ICA, LDA Based on DWT, and SVM Algorithms,\" 2018 41st International Conference on Telecommunications and Signal Processing (TSP), Athens, 2018, pp. 1- 5. doi: 10.1109/TSP.2018.8441452. [5 ] N. Sabri et al., \"A Comparison of Face Detection Classifier using Facial Geometry Distance Measure,\" 2018 9th IEEE Control and System Graduate Research Colloquium (ICSGRC), Shah Alam, Malaysia, 2018, pp. 116- 120.doi: 10.1109/ICSGRC.2018.8657592" }, { "key": "ref6", "doi": "10.1109/ssd.2019.8893214", "unstructured": "A. Adouani, W. M. Ben Henia and Z. Lachiri, \"Comparison of Haarlike, HOG and LBP approaches for face detection in video sequences,\" 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD), Istanbul, Turkey, 2019, pp. 266-271. doi: 10.1109/SSD.2019.8893214" }, { "key": "ref7", "doi": "10.1109/acpr.2017.123", "unstructured": "J. Fan, Q. Ye and N. Ye, \"Enhanced Adaptive Locality Preserving Projections for Face Recognition,\" 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR), Nanjing, 2017, pp. 594-598. doi: 10.1109/ACPR.2017.123" }, { "key": "ref8", "unstructured": "Sujata G. Bhele and V.H. Mankar, A Review Paper on Face Recognition Techniques, in The International Journal of Advanced Research in Computer Engineering and Technology (IJARCET) vol 1, Issue 8, October 2012" }, { "key": "ref9", "doi": "10.1109/icce-asia.2017.8307832", "unstructured": "H. S. Karthik and J. Manikandan, \"Evaluation of relevance vector machine classifier for a real- time face recognition system,\" 2017 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), Bangalore,2017, pp. 26-30.doi: 10.1109/ICCE-ASIA.2017.8307832 Efficient Face Features Extraction and Recognition Using Principal Component Analysis" }, { "key": "ref10", "doi": "10.1080/14786440109462720", "unstructured": "K. Pearson, “On Lines and Planes of Closest Fit to Systems of Points in Space”, Philosophical Magazine 2, 1901,pp 559–572, http://pbil.univlyon1.fr/R/pearson1901.pdf" }, { "key": "ref11", "doi": "10.1109/eiconrus.2019.8657240", "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" }, { "key": "ref12", "doi": "10.1109/icicct.2017.7975191", "unstructured": "B. K. Bhavitha, A. P. Rodrigues and N. N. Chiplunkar, \"Comparative study of machine learning techniques in sentimental analysis,\" 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, 2017, pp. 216-221, doi: 10.1109/ICICCT.2017.7975191" }, { "key": "ref13", "doi": "10.1109/iceeict.2015.7307518", "unstructured": "F. Mahmud, M. T. Khatun, S. T. Zuhori, S. Afroge, M. Aktar and B. 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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" } ], "record": { "registrant": "Longman Publishers", "registered": "2026-09-08", "updated": "2026-09-20", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "url_after": "https://www.jst.org.in/index.php/pub/article/view/467", "sha256_before": null, "sha256_after": "68befeb8137f3c2aae4a67b0a981051579104d3ba74a53effad06f1201636d78" }, { "seq": 2, "kind": "update", "at": "2026-09-29 23:59:57", "by": "Administrator (admin)", "by_kind": "user", "note": "record re-read from api.crossref.org", "changes": [ { "path": "container.titles.0.value", "from": "Journal of Science & Technology", "to": "Journal of Science & Technology" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2021.v6.i04.pp251-257", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "Efficient Face Features Extraction and Recognition Using Principal Component Analysis", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2021.v6.i04.pp251-257" } ], "agents": [ { "role": "author", "name": { "given": "Dr. R. 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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.", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2021-08-29", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1109/socpar.2010.5686495", "unstructured": "R. Ebrahimpour, N. Sadeghnejad, A. Amiri and A. Moshtagh, \"Low resolution face recognition using combination of diverse classifiers,\" 2010 International Conference of Soft Computing and Pattern Recognition,Paris,2010,pp.265-268.doi:10.1109/SOCPAR.2010.5686495" }, { "key": "ref2", "doi": "10.1109/iccisci.2019.8716452", "unstructured": "H. Baqeel and S. Saeed, \"Face detection authentication on Smart phones: End Users Usability Assessment Experiences,\" 2019 International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia, 2019, pp.1-6.doi: 10.1109/ICCISci.2019.8716452" }, { "key": "ref3", "doi": "10.1109/iccisci.2019.8716452", "unstructured": "P. 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