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.
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
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.