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Face Recognition Systems: A Survey
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Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.
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“The presence of noise in images can act as a deterrent in the course of various tasks in image processing, like facial recognition [1], form processing [2], environmental remote sensing [3], etc.”
From Median Filter Aided CNN Based Image Denoising: An Ensemble Approach (2021) · cites this paper as [1]
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Metadata
- Authors
- Kortli, Y., Jridi, M., Al Falou, A., & Atri, M.
- Affiliations
- University of Monastir (TN)
- AI-ED Department, Yncrea Ouest, 20 rue du Cuirassé de Bretagne, 29200 Brest, France
- King Khalid University (SA)
- Corresponding author
- Kortli, Yassin
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-01-07
- Volume
- 20
- Issue
- 2
- Pages
- 342
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7013584
- OpenAlex
- W2999711171
- Cited by
- 623
- References
- 128
- Citations by year
- 2026: 44 · 2025: 113 · 2024: 118 · 2023: 145 · 2022: 107 · 2021: 81 · 2020: 15
- FWCI
- 33.82
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- face recognition, holistic face recognition, face recognition databases, video surveillance, access control, unmanned vehicles, recognition robustness
- MeSH terms
- Facial Recognition, Algorithms, Humans, Pattern Recognition, Automated, Surveys and Questionnaires, Neural Networks, Computer, Databases as Topic, Imaging, Three-Dimensional, Principal Component Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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