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Cited by 623Face and Expression RecognitionFace recognition and analysisBiometric Identification and Security

The DOI matches its Crossref record; 128 of the 128 references it cites were found in a registry or an index.

Face Recognition Systems: A Survey

Open access
Kortli, Yassin, Jridi, Maher, Al Falou, Ayman, Atri, Mohamed·2020·Sensors
DOI 10.3390/s20020342PMC PMC7013584
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Abstract

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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Figures

Figure 1 Face recognition structure [3,23].
Figure 1 Face recognition structure [3,23].CC BY · PubMed Central
Figure 2 Face recognition methods. SIFT, scale-invariant feature transform; SURF, scale-invariant feature transform; BRIEF, binary robust independent elementary features; LBP, local binary pattern; HOG, histogram of oriented gradients; LPQ, local phase quantization; PCA, principal component analysis; LDA, linear discriminant analysis; KPCA, kernel PCA; CNN, convolutional neural network; SVM, support vector machine.
Figure 2 Face recognition methods. SIFT, scale-invariant feature transform; SURF, scale-invariant feature transform; BRIEF, binary robust independent elementary features; LBP, local binary pattern; HOG, histogram of oriented gradients; LPQ, local phase quantization; PCA, principal component analysis; LDA, linear discriminant analysis; KPCA, kernel PCA; CNN, convolutional neural network; SVM, support vector machine.CC BY · PubMed Central
Figure 3 The local binary pattern (LBP) descriptor [19].
Figure 3 The local binary pattern (LBP) descriptor [19].CC BY · PubMed Central
Figure 4 All “4f” optical configuration [37].
Figure 4 All “4f” optical configuration [37].CC BY · PubMed Central
Figure 5 Flowchart of the VanderLugt correlator (VLC) technique [4]. FFT, fast Fourier transform; POF, phase-only filter.
Figure 5 Flowchart of the VanderLugt correlator (VLC) technique [4]. FFT, fast Fourier transform; POF, phase-only filter.CC BY · PubMed Central
Figure 6 (a) Creation of the 3D face of a person, (b) results of the detection of 29 landmarks of a face using the active shape model, (c) results of the detection of 26 landmarks of a face [3].
Figure 6 (a) Creation of the 3D face of a person, (b) results of the detection of 29 landmarks of a face using the active shape model, (c) results of the detection of 26 landmarks of a face [3].CC BY · PubMed Central
Figure 7 Face recognition based on the speeded-up robust features (SURF) descriptor [58]: recognition using fast library for approximate nearest neighbors (FLANN) distance.
Figure 7 Face recognition based on the speeded-up robust features (SURF) descriptor [58]: recognition using fast library for approximate nearest neighbors (FLANN) distance.CC BY · PubMed Central
Figure 8 Fast retina keypoint (FREAK) descriptor used 43 sampling patterns [19].
Figure 8 Fast retina keypoint (FREAK) descriptor used 43 sampling patterns [19].CC BY · PubMed Central
Figure 9 Example of dimensional reduction when applying principal component analysis (PCA) [62].
Figure 9 Example of dimensional reduction when applying principal component analysis (PCA) [62].CC BY · PubMed Central
Figure 10 The first five Eigenfaces built from the ORL database [63].
Figure 10 The first five Eigenfaces built from the ORL database [63].CC BY · PubMed Central
Figure 11 The first five Fisherfaces obtained from the ORL database [63].
Figure 11 The first five Fisherfaces obtained from the ORL database [63].CC BY · PubMed Central
Figure 12 Flowchart of the proposed multimodal deep face representation (MM-DFR) technique [95]. CNN, convolutional neural network.
Figure 12 Flowchart of the proposed multimodal deep face representation (MM-DFR) technique [95]. CNN, convolutional neural network.CC BY · PubMed Central
Figure 13 The proposed CNN–LSTM–ELM [103].
Figure 13 The proposed CNN–LSTM–ELM [103].CC BY · PubMed Central
Figure 14 Optimal hyperplane, support vectors, and maximum margin.
Figure 14 Optimal hyperplane, support vectors, and maximum margin.CC BY · PubMed Central
Figure 15 Artificial neural network.
Figure 15 Artificial neural network.CC BY · PubMed Central

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Citations

Cites

Show all 128 references

Research works printed but not linked: 3

  1. Perlibakas, V. (2006). Face recognition using principal component analysis and log-gabor filters. arXiv.
  2. Sufyanu, 2016, Enhanced Face Recognition Using Discrete Cosine Transform, Eng. Lett., 24, 52
  3. Ouerhani, 2013, Graphics processor unit implementation of correlation technique using a segmented phase only composite filter, Opt. Commun., 289, 33, 10.1016/j.optcom.2012.09.074 · DOI

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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
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2020-01-07
Volume
20
Issue
2
Pages
342
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s20020342
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
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

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