Cite this DOI

Kumar Reddy, D. R. P., & Dr. S. Kiran (2021). Efficient Face Features Extraction and Recognition Using Principal Component Analysis. *Journal of Science & Technology*, *06*(04), 251–257. https://doi.org/10.46243/jst.2021.v6.i04.pp251-257

@article{kumarreddy2021efficient,
  author    = {Kumar Reddy, Dr. R. Pradeep and Dr. S. Kiran},
  title     = {{Efficient Face Features Extraction and Recognition Using Principal Component Analysis}},
  journal   = {Journal of Science \& Technology},
  year      = {2021},
  month     = {aug},
  volume    = {06},
  number    = {04},
  pages     = {251--257},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2021.v6.i04.pp251-257},
  url       = {https://doi.org/10.46243/jst.2021.v6.i04.pp251-257},
  language  = {en},
  abstract  = {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.}
}

TY  - JOUR
TI  - Efficient Face Features Extraction and Recognition Using Principal Component Analysis
AU  - Kumar Reddy, Dr. R. Pradeep
AU  - Dr. S. Kiran
JO  - Journal of Science & Technology
PY  - 2021
DA  - 2021/08/29/
VL  - 06
IS  - 04
SP  - 251
EP  - 257
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - 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.
DO  - 10.46243/jst.2021.v6.i04.pp251-257
UR  - https://doi.org/10.46243/jst.2021.v6.i04.pp251-257
ER  -

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    "URL": "https://doi.org/10.46243/jst.2021.v6.i04.pp251-257",
    "title": "Efficient Face Features Extraction and Recognition Using Principal Component Analysis",
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    "language": "en",
    "abstract": "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.",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2021.v6.i04.pp251-257 gives all four in one JSON answer.