Feature extraction for multimodal biometric and study of fusion using Gaussian mixture model

Publication Type:

Conference Proceedings

Authors:

Vivek, S.A.; Aravinth J.; Valarmathy, S.

Source:

IEEE International Conference on Pattern Recognition, Informatics and Medical Engineering(PRIME 2012), Salem, Tamilnadu, p.387-392 (2012)

ISBN:

9781467310376

Accession Number:

12770798

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-84863922952&partnerID=40&md5=be97c7e2b957f896a4defa6784ab3f3b

Keywords:

Algorithms, biomedical engineering, Biometrics, Error rate, Feature extraction, GMM, Information science, Likelihood ratio tests, Multi-modal biometrics, Score-level fusion, Template, Unimodal

Abstract:

Biometrics consists of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. This paper describes the feature extraction techniques for three modalities viz. fingerprint, iris and face. The extracted information from each modality is stored as a template. The information are fused at the match score level using a density based score level fusion, GMM followed by the Likelihood ratio test. GMM parameters are estimated from training data using the iterative Expectation-Maximization (EM) algorithm. © 2012 IEEE.

Notes:

cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@27c75604 ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@fe03503 Through org.apache.xalan.xsltc.dom.DOMAdapter@48b86019; Conference Code:91139

Cite this Research Publication

S. A. Vivek, Aravinth J., and Valarmathy, S., “Feature extraction for multimodal biometric and study of fusion using Gaussian mixture model”, IEEE International Conference on Pattern Recognition, Informatics and Medical Engineering(PRIME 2012). Salem, Tamilnadu, pp. 387-392, 2012.

 
          
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