Curvelet and Ridgelet-based Multimodal Biometric Recognition System using Weighted Similarity Approach

  • S. Arivazhagan Mepco Schlenk Engineering College, Tamilnadu
  • Jayaram Raja Sekar Mepco Schlenk Engineering College, Tamilnadu
  • S. Shobana Priyadharshini Mepco Schlenk Engineering College, Tamilnadu
Keywords: Multimodal, multi-resolution, curvelet tranform, ridgelet transform, score combination, weighted similarity

Abstract

Biometric security artifacts for establishing the identity of a person with high confidence have evoked enormous interest in security and access control applications for the past few years. Biometric systems based solely on unimodal biometrics often suffer from problems such as noise, intra-class variations and spoof attacks. This paper presents a novel multimodal biometric recognition system by integrating three biometric traits namely iris, fingerprint and face using weighted similarity approach. In this work, the multi-resolution features are extracted independently from query images using curvelet and ridgelet transforms, and are then compared to the enrolled templates stored in the database containing features of each biometric trait. The final decision is made by normalizing the feature vectors, assigning different weights to the modalities and fusing the computed scores using score combination techniques. This system is tested with the public unimodal databases such as CASIA–Iris-V3-Interval, FVC2004, ORL and self-built multimodal databases. Experimental results obtained shows that the designed system achieves an excellent recognition rate of 98.75 per cent and 100 per cent for the public and self-built databases respectively and provides ultra high security than unimodal biometric systems.

Defence Science Journal, 2014, 64(2), pp. 106-114. DOI: http://dx.doi.org/10.14429/dsj.64.3469

Author Biographies

S. Arivazhagan, Mepco Schlenk Engineering College, Tamilnadu

Dr S. Arivazhagan obtained his PhD (Image Processing) from the Manonmaniam Sundaranar University, Tirunelveli in 2005.He is presently the Principal of Mepco Schlenk Engineering College, Sivakasi. He has published more than 150 research papers in refereed journals and conference proceedings in the areas of pattern recognition, image processing and computer vision.His current research interests are in the areas of biometrics, image and video understanding and computer communication. He is a Fellow of IETE and Life Member of ISTE.

Jayaram Raja Sekar, Mepco Schlenk Engineering College, Tamilnadu
Mr J. Raja Sekar obtained his ME (Computer Science and Engineering)  from College of Engineering, guindy, Anna university, Chennai in 2001. he is currently working as Assistant Professor in the Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi. he has published nearly 20 technical papers in International/National Journals/Conferences. his current research interests include biometrics, pattern recognition, and image processing. He is a Life Member in Indian Society for Technical Education (ISTE).
S. Shobana Priyadharshini, Mepco Schlenk Engineering College, Tamilnadu

Ms S. Shobana Priyadharshini obtained her BE (Electronics and Communication Engineering) from Kamaraj College of Engineering and Technology, Virudhunagar in 2010 and ME (Communication Systems)  from Mepco Schlenk Engineering College, Sivakasi in 2012. her areas of interest include image processing and pattern recognition.

Published
2014-03-20
How to Cite
ArivazhaganS., Raja SekarJ., & PriyadharshiniS. (2014). Curvelet and Ridgelet-based Multimodal Biometric Recognition System using Weighted Similarity Approach. Defence Science Journal, 64(2), 106-114. https://doi.org/10.14429/dsj.64.3469
Section
Computers & Systems Studies