Paper Info
Title
Gabor-feature-based local generic representation for face recognition with single sample per person
Abstract
This paper presents an approach called Gabor-feature-based Local Generic Representation (G-LGR), which take advantages of the sparse representation properties of face recognition in biometric applications. In this work, the main problem is that if only one training subject per class is available. One of the novelties of our new algorithm is to produce virtual samples of each subject; the new sample generic of a gallery set is used in order to generate the intra-personal variations of different individuals. We compare our approach against different state-of-the-art techniques using the AR face database.
Year
DOI
Venue
2017
10.1109/SERA.2017.7965722
2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA)
Keywords
Field
DocType
Gabor-feature-based local generic representation,face recognition,G-LGR,sparse representation,biometric applications,intra-personal variations
Kernel (linear algebra),Facial recognition system,Computer vision,Pattern recognition,Computer science,Sparse approximation,Artificial intelligence,Feature based,Biometrics
Conference
ISBN
Citations 
PageRank 
978-1-5090-5757-3
0
0.34
References 
Authors
9
3
Name
Order
Citations
PageRank
Taher Khadhraoui131.40
Faouzi Benzarti2158.94
Hamid Amiri38619.36