Facial Recognition Databases: Recent Developments and Review of Methods

Waleed Ayad; Siraj Qays; Asanka G. Perera; Ali Al-Naji

doi:10.51173/jt.v5i4.1729

Authors

Waleed Ayad Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.
Siraj Qays Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.
Asanka G. Perera University of New South Wales, Canberra, ACT 2610, Australia
Ali Al-Naji School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia

DOI:

https://doi.org/10.51173/jt.v5i4.1729

Keywords:

Face Recognition, 2D Face Datasets, Database

Abstract

Facial recognition is one of the most important biometrics that many researchers are increasingly studying, as it is used in various applications such as surveillance, security, law enforcement, information, person identification, smart cards, access control, etc. There is a fundamental relationship between the development of facial recognition algorithms and the possibility of the existence of databases of different faces that influence the appearance of the face in a constrained manner. Standard datasets of images of appropriate size for a subject should be accessible to the public to compare the performance and assessment of identification or verification of a facial recognition system. This paper aims to present a review of the most popular 2D unmasked and masked face datasets available in the current century that are accessible for free download or can be certified with an acceptable effort, where these databases are suitable for training and testing 2D face recognition approaches. Also, this review discussed the evaluation metrics for face recognition and their two types of tasks (identification and verification).

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Author Biographies

Waleed Ayad, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.

Siraj Qays, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.

Asanka G. Perera, University of New South Wales, Canberra, ACT 2610, Australia

School of Engineering and Information Technology

Ali Al-Naji, School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia

References

D. N. Parmar and B. B. Mehta, "Face recognition methods & applications," arXiv preprint arXiv:1403.0485, 2014, DOI: https://doi.org/10.48550/arXiv.1403.0485.

Q. Wang, A. Alfalou, and C. Brosseau, "New perspectives in face correlation research: a tutorial," Advances in Optics and Photonics, vol. 9, no. 1, pp. 1-78, 2017, DOI: https://doi.org/10.1364/AOP.9.000001.

F. Keinert, D. Lazzaro, and S. Morigi, "A robust group-sparse representation variational method with applications to face recognition," IEEE Transactions on Image Processing, vol. 28, no. 6, pp. 2785-2798, 2019, DOI: http://dx.doi.org/10.1109/TIP.2018.2890312.

J. Zhang, X. Yan, Z. Cheng, and X. Shen, "A face recognition algorithm based on feature fusion," Concurrency and Computation: Practice and Experience, p. e5748, 2020, DOI: https://doi.org/10.1002/cpe.5748.

Y. Xu, Z. Zhang, G. Lu, and J. Yang, "Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification," Pattern Recognition, vol. 54, pp. 68-82, 2016, DOI: https://doi.org/10.1016/j.patcog.2015.12.017.

A. S. Amsalam, A. Al-Naji, A. Y. Daeef, and J. Chahl, "Computer Vision System for Facial Palsy Detection," Journal of Techniques, vol. 5, no. 1, pp. 44-51, 2023, DOI: https://doi.org/10.51173/jt.v5i1.1133.

A. J. Shepley, "Deep learning for face recognition: a critical analysis," arXiv preprint arXiv:1907.12739, 2019, DOI: https://doi.org/10.48550/arXiv.1907.12739.

S. Gupta, K. Thakur, and M. Kumar, "2D-human face recognition using SIFT and SURF descriptors of face’s feature regions," The Visual Computer, vol. 37, no. 3, pp. 447-456, 2021, DOI: https://doi.org/10.1007/s00371-020-01814-8.

M. Chihaoui, A. Elkefi, W. Bellil, and C. Ben Amar, "A survey of 2D face recognition techniques," Computers, vol. 5, no. 4, p. 21, 2016, DOI: https://doi.org/10.3390/computers5040021.

E. Varadharajan, R. Dharani, S. Jeevitha, B. Kavinmathi, and S. Hemalatha, "Automatic attendance management system using face detection," in 2016 Online international conference on green engineering and technologies (IC-GET), 2016: IEEE, pp. 1-3, DOI: 10.1109/GET.2016.7916753.

H. Zhang, Z. Qu, L. Yuan, and G. Li, "A face recognition method based on LBP feature for CNN," in 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2017: IEEE, pp. 544-547, DOI: https://doi.org/10.1007/978-981-13-1328-8_29.

S. Adnan, F. Ali, and A. A. Abdulmunem, "Facial feature extraction for face recognition," in Journal of Physics: Conference Series, 2020, vol. 1664, no. 1: IOP Publishing, p. 012050, DOI: 10.1088/1742-6596/1664/1/012050.

S. R. Benedict and J. S. Kumar, "Geometric shaped facial feature extraction for face recognition," in 2016 IEEE International Conference on Advances in Computer Applications (ICACA), 2016: IEEE, pp. 275-278, DOI: 10.1109/ICACA.2016.7887965.

P. Grother, M. Ngan, and K. Hanaoka, "Ongoing face recognition vendor test (frvt) part 1: Verification," National Institute of Standards and Technology, 2018, DOI: https://www.nist.gov/system/files/documents/2018/02/15/frvt_report_2018_02_15.pdf.

P. Grother, P. Grother, M. Ngan, and K. Hanaoka, "Face recognition vendor test (frvt) part 2: Identification," ed: US Department of Commerce, National Institute of Standards and Technology, 2019, DOI: https://doi.org/10.6028/NIST.IR.8271.

Y. Kortli, M. Jridi, A. Al Falou, and M. Atri, "Face recognition systems: A survey," Sensors, vol. 20, no. 2, p. 342, 2020, DOI: https://doi.org/10.3390/s20020342.

E. Bailly-Bailliére et al., "The BANCA database and evaluation protocol," in International conference on Audio-and video-based biometric person authentication, 2003: Springer, pp. 625-638, DOI: https://doi.org/10.1007/3-540-44887-X_74.

P. J. Phillips et al., "Overview of the face recognition grand challenge," in 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), 2005, vol. 1: IEEE, pp. 947-954, DOI: 10.1109/CVPR.2005.268.

G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, "Labeled faces in the wild: A database for studying face recognition in unconstrained environments," in Workshop on faces in'Real-Life'Images: detection, alignment, and recognition, 2008, DOI: https://inria.hal.science/inria-00321923/.

R. Gross, I. Matthews, J. Cohn, T. Kanade, and S. Baker, "Multi-pie," Image and vision computing, vol. 28, no. 5, pp. 807-813, 2010, DOI: https://doi.org/10.1016/j.imavis.2009.08.002.

D. Yi, Z. Lei, S. Liao, and S. Z. Li, "Learning face representation from scratch," arXiv preprint arXiv:1411.7923, 2014, DOI: https://doi.org/10.48550/arXiv.1411.7923.

B. F. Klare et al., "Pushing the frontiers of unconstrained face detection and recognition: Iarpa janus benchmark a," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1931-1939 , DOI: 10.1109/CVPR.2015.7298803.

I. Kemelmacher-Shlizerman, S. M. Seitz, D. Miller, and E. Brossard, "The megaface benchmark: 1 million faces for recognition at scale," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 4873-4882, DOI: 10.1109/CVPR.2016.527.

I. Kemelmacher-Shlizerman, S. Suwajanakorn, and S. M. Seitz, "Illumination-aware age progression," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 3334-3341, DOI: 10.1109/CVPR.2014.426.

H.-W. Ng and S. Winkler, "A data-driven approach to cleaning large face datasets," in 2014 IEEE international conference on image processing (ICIP), 2014: IEEE, pp. 343-347, DOI: 10.1109/ICIP.2014.7025068.

S. Sengupta, J.-C. Chen, C. Castillo, V. M. Patel, R. Chellappa, and D. W. Jacobs, "Frontal to profile face verification in the wild," in 2016 IEEE winter conference on applications of computer vision (WACV), 2016: IEEE, pp. 1-9, DOI: 10.1109/WACV.2016.7477558.

Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao, "Ms-celeb-1m: A dataset and benchmark for large-scale face recognition," in European conference on computer vision, 2016: Springer, pp. 87-102, DOI: https://doi.org/10.1007/978-3-319-46487-9_6.

T. Y. Wang and A. Kumar, "Recognizing human faces under disguise and makeup," in 2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), 2016: IEEE, pp. 1-7, DOI: 10.1109/ISBA.2016.7477243.

O. M. Parkhi, A. Vedaldi, and A. Zisserman, "Deep face recognition," 2015, DOI: https://dx.doi.org/10.5244/C.29.41.

Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, "Vggface2: A dataset for recognising faces across pose and age," in 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018), 2018: IEEE, pp. 67-74, DOI: 10.1109/FG.2018.00020.

C. Whitelam et al., "Iarpa janus benchmark-b face dataset," in proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2017, pp. 90-98, DOI: 10.1109/CVPRW.2017.87.

A. Nech and I. Kemelmacher-Shlizerman, "Level playing field for million scale face recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 7044-7053, DOI: https://doi.org/10.48550/arXiv.1705.00393.

V. Kushwaha, M. Singh, R. Singh, M. Vatsa, N. Ratha, and R. Chellappa, "Disguised faces in the wild," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018, pp. 1-9, DOI: https://doi.org/10.48550/arXiv.1811.08837.

B. Maze et al., "Iarpa janus benchmark-c: Face dataset and protocol," in 2018 international conference on biometrics (ICB), 2018: IEEE, pp. 158-165, DOI: 10.1109/ICB2018.2018.00033.

O. Elharrouss, N. Almaadeed, and S. Al-Maadeed, "LFR face dataset: Left-Front-Right dataset for pose-invariant face recognition in the wild," in 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), 2020: IEEE, pp. 124-130, DOI: 10.1109/ICIoT48696.2020.9089530.

Z. Zhu et al., "Webface260m: A benchmark unveiling the power of million-scale deep face recognition," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 10492-10502, DOI: https://doi.org/10.48550/arXiv.2103.04098.

Z. Wang, B. Huang, G. Wang, P. Yi, and K. Jiang, "Masked face recognition dataset and application," IEEE Transactions on Biometrics, Behavior, and Identity Science, 2023, DOI: https://doi.org/10.48550/arXiv.2003.09093.

M. Geng, P. Peng, Y. Huang, and Y. Tian, "Masked face recognition with generative data augmentation and domain constrained ranking," in Proceedings of the 28th ACM International Conference on Multimedia, 2020, pp. 2246-2254, DOI: https://doi.org/10.1145/3394171.3413723.

W. Ayad, S. Qays, and A. Al-Naji, "Generating and Improving a Dataset of Masked Faces Using Data Augmentation," Journal of Techniques, vol. 5, no. 2, pp. 46-51, 2023, DOI: https://doi.org/10.51173/jt.v5i2.1140.