Work place: Kongu Engineering College, Perundurai-638052, Tamilnadu, India

E-mail: skuppu@gmail.com


Research Interests: Computational Science and Engineering, Computational Engineering, Software Engineering, Software Organization and Properties, Computer Architecture and Organization, Network Security


S.Kuppuswami, presently working as Professor and Principal in Kongu Engineering College, Perundurai, Erode, TamilNadu. He has more than 35 years of experience in technical education in India and abroad. He received B.E in ECE in 1975, M.Sc.Engg in Applied Electronics in 1977 and Ph.D in Engg in Computer Science from University of Rennes I, France in 1986. He worked in Anna University, Chennai, University of Rennes I, France, Pondicherry Engineering College and Pondicherry University. His primary research interests are Software Engineering, Software Architecture, Project Management and Network Security. He is a life member of Computer Society of India and Indian Society of Technical Education.

Author Articles
Performance Analysis of Fingerprint Denoising Using Stationary Wavelet Transform

By Usha.S Kuppuswami.S

DOI: https://doi.org/10.5815/ijigsp.2015.11.07, Pub. Date: 8 Oct. 2015

Finger print is the finest and cheapest recognition system because of its easy extraction of unique features like bifurcation and termination. But the quality of fingerprint data are easily degraded by dryness of skin, wet, wound and other types of noises. Hence, denoising of fingerprint image is vital step for automatic fingerprint recognition system. In the proposed paper the removal of noise from fingerprint images by using stationary wavelet transform and adaptive thresholding method is analysed. The proposed algorithm is developed using MATLAB (R2010b) and tested in the fingerprint images collected from FVC2004 database and R303A optical scanner. The performance of the method is analysed by calculating the quality metrics like Peak Signal to Noise Ratio, Universal Quality Index , Structure Similarity and Multi-Scale Structure Similarity (MS-SSIM). The quality of fingerprint image after noise removal using proposed analysis confirms the suggested method is better than the conventional techniques.

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