Review on Various Face Recognition Databases

Khushi Bhoj*, Kuldeep Choksi**, Rishi Kitawat***, Manish Rana****
*-**** Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai, India.
Periodicity:July - December'2022


Face recognition is one of the multimedia items that has seen a remarkable increase in popularity in recent years. Face continues to be the most difficult study topic for experts in the field of computer vision and image processing since it is an item with different properties for detection. We have attempted to handle the most challenging facial aspects in this survey work, including posture invariance, aging, illuminations, and partial occlusion. When applied to facial photographs, they are regarded as essential components of face recognition systems. The most recent face detection methods and techniques are also examined in this paper, including Eigenface, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Gabor Wavelets, Elastic Bunch Graph Matching, 3D Morphable Models, and Hidden Markov Models. Many testing face databases, such as AT & T (ORL), AR, FERET, LFW, YTF, and Yale, also reviewed. However, the purpose of this study is to present a thorough literature assessment on face recognition and its applications.


Face Recognition, Illuminations, Partial Occlusion, Pose Invariance.

How to Cite this Article?

Bhoj, K., Choksi, K., Kitawat, R., and Rana, M. (2022). Review on Various Face Recognition Databases. i-manager’s Journal on Pattern Recognition, 9(2), 17-29.


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