Open AccessSparsifying transform learning for face image classification
Author(s) -
Qudaimat A.,
Demirel H.
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2018.0524
Subject(s) - pattern recognition (psychology) , artificial intelligence , sparse approximation , computer science , image (mathematics) , facial recognition system , face (sociological concept) , transformation (genetics) , class (philosophy) , process (computing) , identification (biology) , norm (philosophy) , political science , law , social science , biochemistry , chemistry , botany , sociology , biology , gene , operating system
Sparse signal representation showed promising results in the field of face recognition in the past few years. An algorithm based on a sparsifying transform is considered. It mainly learns a dictionary that can transform the image into sparse vectors. In the transformation domain, the images of the same class should have similar non‐zero coefficients pattern that can be used for identification. The classification process of this method only requires to transform the image and make norm comparisons to determine the class of the image. The proposed method shows a comparable performance with the other known methods in the literature by means of accuracy. A novel method in sparsity‐based image identification that uses analysis dictionaries is proposed, unlike the conventional sparsity‐based methods. One advantage of the proposed algorithm is the low computational cost of the classification process.