Schoenberg Logarithmic Image Similarity in Prewitt-Gabor-Zernike Domain | Science Publications

American Journal of Applied Sciences

Schoenberg Logarithmic Image Similarity in Prewitt-Gabor-Zernike Domain

Ahmed Jebur Ali, Zahir M. Hussain, Mohammed Sahib Mechee and Zainab Ali Khalaf

DOI : 10.3844/ajassp.2017.828.842

American Journal of Applied Sciences

Volume 14, Issue 9

Pages 828-842

Abstract

This paper represents a new approach for face recognition that incorporates Prewitt edge detection, Gabor filter and Zernike moments to transform the image into a unified domain. On this joint domain, five distance metrics are constructed using Schoenberg transform for the purpose of defining efficient similarity measures for holistic face recognition. The proposed Schoenberg similarity applies Schoenberg transform to the logarithm of five existing distance metrics: Minkowski, City-Block, Euclidean, Soergel and Lorentzian metrics. The constructed Schoenberg logarithmic metrics are called SL-Minkowski, SL-City-Block, SL-Euclidean, SL-Soergel and SL-Lorentzian. These distance metrics are utilized as similarity measures after being normalized over the range [0,1] for fair comparison with existing measures. The proposed Schoenberg system can resist three problems: Change in illumination, pose and facial expression. Simulation results show that the proposed distance measures have superior performance as compared to the classical metrics: Structural Similarity Index Measure (SSIM) and Feature-based Similarity Measure (FSIM). Performance criteria are the recognition rate and the recognition confidence, defined as the similarity difference between the best match and the second-best match in the face database.

Copyright

© 2017 Ahmed Jebur Ali, Zahir M. Hussain, Mohammed Sahib Mechee and Zainab Ali Khalaf . This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.