Open AccessSparse representation for face recognition: A review paper
Author(s) -
Madarkar Jitendra,
Sharma Poonam,
Singh Rimjhim Padam
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12155
Subject(s) - facial recognition system , computer science , face (sociological concept) , artificial intelligence , sparse approximation , three dimensional face recognition , representation (politics) , variation (astronomy) , pattern recognition (psychology) , face detection , computer vision , basis (linear algebra) , machine learning , mathematics , social science , physics , geometry , sociology , politics , law , political science , astrophysics
With the increasing use of surveillance cameras, face recognition is being studied by many researchers for security purposes. Although high accuracy has been achieved for frontal faces, the existing methods have shown poor performance for occluded and corrupt images. Recently, sparse representation based classification (SRC) has shown the state‐of‐the‐art result in face recognition on corrupt and occluded face images. Several researchers have developed extended SRC methods in the last decade. This paper mainly focuses on SRC and its extended methods of face recognition. SRC methods have been compared on the basis of five issues of face recognition such as linear variation, non‐linear variation, undersampled, pose variation, and low resolution. Detailed analysis of SRC methods for issues of face recognition have been discussed based on experimental results and execution time. Finally, the limitation of SRC methods have been listed to help the researchers to extend the work of existing methods to resolve the unsolved issues.