Face recognition in unconstrained situations continues to be a challenging research domain. This paper have presented an extensive survey of face databases for constrained and unconstrained environments. It focuses mostly on novel databases that are freely available for the research purposes. Most of the popular face databases are concisely introduced and compared. The purpose of this review paper is to assist the young budding researchers in the area of face recognition by compiling the most widely used face databases and the link to download them so as to motivate their further research.
REFERENCES
1 Martinez, A. M, “The AR face database”, CVC Technical Report, 24,
1998.
2 Gross R., “Face Databases”, Handbook of Face Recognition S. Z. LI
and A. K. JAIN. New York: Springer. ISBN 0-387-40595-x, 2005.
3 Face Recognition Hompage www.face-rec.org/databases.
4 Ladislav Lenc, PavelKral, “Unconstrained Facial Images: Database for
Face Recognition under Real-world Conditions”, October 2015, DOI:
10.1007/978-3-319-27101-9_26.
5 W. Zhao, R. chellappa, P. J. Phillips, Face recognition: A literature
survey, “ACM Computing Surveys (CSUR)”, December, 2003.
6 Siddheshwar S. Gangonda, Prashant P. Patavardhan, Kailash J.
Karande, “Analysis of Face Recognition Algorithms for Uncontrolled
Environments”, Third International Conference on Computing,
Communication and Signal Processing (ICCASP-2018), Lonere,
Raigad, Maharashtra. DOI: 10.1007/978-981-13-1513-8_93.
7 Siddheshwar S. Gangonda, Prashant P. Patavardhan, Kailash J.
Karande,”An Extensive Survey of Prominent Researches in Face
Recognition under different Conditions”, Fourth International
Conference on Computing Communication Control and Automation
(ICCUBEA), Pune, 2018. 978-1-5386-5257-2/18/$31.00 ©2018 IEEE.
8 Gary B. Huang1, Marwan Mattar1, Tamara Berg2, and Erik Learned-
Miller, “Labeled Faces in the Wild: A Database for Studying Face
Recognition in Unconstrained Environments”, http://vis-
www.cs.umass.edu/lfw/.
9 Gabriel Castaneda and Taghi M. Khoshgoftaar, “A Review of
Performance Evaluation on 2D Face Databases”, IEEE Third
International Conference on Big Data Computing Service and
Applications, 2017, 978-1-5090-6318-5/17 $31.00 © 2017 IEEE
DOI 10.1109/BigDataService.2017.38
10 S. Z. Li, R. F. Chu, S. C. Liao, and L. Zhang, “Illumination invariant
face recognition using near-infrared images,” IEEE Transactions on
Pattern Analysis and Machine Intelligence, Vol. 29, No. 4, pp. 1-13,
April 2007.
11 Shwetank Arya*, Neeraj Pratap, Karamjit Bhatia, “Future of Face
Recognition: A Review”, Second International Symposium on
Computer Vision and the Internet (VisionNet’15), 2015.
12 http://rvl1.ecn.purdue.edu/~aleix/aleix_face_DB.html
13 http://cvc.yale.edu/projects/yalefaces/yalefaces.html.
14 Muhammad Sharif, Farah Naz, Mussarat Yasmin, Muhammad Alyas
Shahid and Amjad Rehman, “Face Recognition: A Survey”, Journal
Of Engineering Science and Technology Review 10 (2) (2017) 166-
177, ISSN: 1791-2377 © 2017
15 M. Hassaballah, Saleh Aly, “Face recognition: challenges,
achievements and future directions”, The Institution of Engineering
and Technology (IET), ISSN 1751-9632, doi: 10.1049/iet-
cvi.2014.0084.
