Score level fusion based Multimodal biometric identification using Thepade's Sorted Ternary Block Truncation coding with variod proportion of Iris, Palmprint, Left Fingerprint & Right Fingerprint with asorted similarity measures & different Colorspaces

Abstract

Biometric is used to automate the measurement of biological data. The measurement and recording of the physical characteristics of an individual for the use in subsequent personal identification. Multimodal biometric authentication is system which uses two or more biometric trait. It provides more immunity against spoofing. To reduce feature vector size and to improve genuine acceptance rate proposed technique is developed. In this paper fusion of Left Fingerprint, Right Fingerprint, Iris, Palmprint are experimented. Multimodal biometrics having different level fusion such as score level fusion, feature level fusion, decision level fusion. In this paper score level fusion is considered for experimentation. Score level fusion contains more gratified and worthful information. Here different score proportions are experimented and performance efficiency is measured using genuine acceptance ratio (GAR). True acceptance rate of recognition is increased because of multiple biometrics characters. In proposed technique features are extracted using Thepade's sorted ternary block truncation coding. Using TSTBTC and matching score proportion Iris: Palm print: Left Fingerprint: Right Fingerprint(40∶2∶1∶1) gives better performance as indicated by higher GAR values observed by 71.86%.