Performance Evaluation of Different Feature Extractors and Classifiers for Recognition of Human Faces with Low Resolution Images
DOI:
https://doi.org/10.18201/ijisae.28949Keywords:
Face recognition, Feature Extraction, Classification, Interpolation, Dimensionality ReductionAbstract
Face recognition is an effective biometric identification technique used in many applications such as law enforcement, document validation and video surveillance. In this paper the effect of low resolution images which are captured in real world applications, on the performance of different feature extraction techniques combined with a variety of classification approaches is evaluated. Gabor features and its combination with local phase quantization histogram (GLPQH) are dimensionality reduced by principal component analysis (PCA), linear discriminant analysis (LDA), locally sensitive discriminant analysis (LSDA) and neighbourhood preserving embedding (NPE) to extract discriminant image characteristics and the class label is attributed using the extreme learning machine (ELM), sparse classifier (SC), fuzzy nearest neighbour (FNN) or regularized discriminant classifier (RDC). ORL and AR databases are utilized and the results show that ELM and RDC have better performance and stability against resolution reduction, especially on Gabor-PCA and Gabor-LDA techniques. Among the interpolation approaches that we employed to enhance the image resolution, nearest neighbour outperforms other methods.Downloads
Downloads
Published
How to Cite
Issue
Section
License
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.