Schoenberg Logarithmic Image Similarity in Prewitt-GaborZernike Domain
الباحث الأول:
Ahmed Jebur Ali
الباحثين الآخرين:
Zahir M. Hussain, Mohammed Sahib Mechee and Zainab Ali Khalaf
المجلة:
American Journal of Applied Sciences
تاريخ النشر:
None
مختصر البحث:
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 S…
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.
