Title

Schoenberg logarithmic image similarity in Prewitt-Gabor-Zernike domain

Document Type

Journal Article

Publisher

Science Publications

School

School of Engineering

RAS ID

25777

Comments

Originally published as:

Ali, A., Hussain, Z., Mechee, M., & Khalaf, Z.(1981). Schoenberg logarithmic image similarity in Prewitt-Gabor-Zernike domain. American Journal of Applied Sciences, 14(9), 828-842. doi:10.3844/ajassp.2017.828.842

Original article available here.

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.

DOI

10.3844/ajassp.2017.828.842

Share

 
COinS