%0 Journal Article
	%A N. B. Mahesh Kumar and  K. Premalatha
	%D 2015
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 97, 2015
	%T Personal Authentication Using FDOST in Finger Knuckle-Print Biometrics
	%U https://publications.waset.org/pdf/10000660
	%V 97
	%X The inherent skin patterns created at the joints in the
finger exterior are referred as finger knuckle-print. It is exploited to
identify a person in a unique manner because the finger knuckle print
is greatly affluent in textures. In biometric system, the region of
interest is utilized for the feature extraction algorithm. In this paper,
local and global features are extracted separately. Fast Discrete
Orthonormal Stockwell Transform is exploited to extract the local
features. Global feature is attained by escalating the size of Fast
Discrete Orthonormal Stockwell Transform to infinity. Two features
are fused to increase the recognition accuracy. A matching distance is
calculated for both the features individually. Then two distances are
merged mutually to acquire the final matching distance. The
proposed scheme gives the better performance in terms of equal error
rate and correct recognition rate.

	%P 323 - 328