144
Views
18
CrossRef citations to date
0
Altmetric
Original Articles

Integrating GWTM and BAT algorithm for face recognition in low-resolution images

&
Pages 441-452 | Received 17 Apr 2016, Accepted 30 Aug 2016, Published online: 22 Dec 2016
 

Abstract

In biometrics, face recognition is one of the important identification methods with various applications such as, video surveillance, defence, human/computer interactions and many more. The current face recognition systems perform well using the frontal images with high resolution. In contrast, the utilisation of low-resolution (LR) images degrades the performance of face recognition systems. Hence, this paper integrates the Gabor filter + wavelet + texture (GWTM) operator and the BAT algorithm to increase the performance, while deploying the LR images. The proposed algorithm integrates the uniqueness of Gabor features, the robustness of local features and the wavelet features to handle the inter-person and intra-person variations. This paper utilises the spherical SVM classifier to enhance the recognition performance. Finally, the proposed GWTM operator is compared with other existing algorithms such as, GOM, LBP and LGP based on the parameters of accuracy, FAR and FRR. The proposed GWTM operator attains the highest accuracy of 95% and a minimum FAR of 5%. The results prove that the proposed GWTM yields a performance improvement of 5, 3, 4 and 15% over the GOM, LBP, LGP and GWTM, respectively, in the absence of the BAT algorithm.

View correction statement:
Corrigendum

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 305.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.