Multimodal biometric system based on fusion techniques: a review

2021 ◽  
pp. 1-49
Author(s):  
Neeru Bala ◽  
Rashmi Gupta ◽  
Anil Kumar
Keyword(s):  
Biometric System ◽  
Multimodal Biometric System

Multimodal biometric system based on information set theory and refined scores

2016 ◽  
Vol 21 (17) ◽  
pp. 5133-5144 ◽  
Author(s):  
Parul Arora ◽  
Sandeep Bhargava ◽  
Smriti Srivastava ◽  
Madasu Hanmandlu
Keyword(s):  
Set Theory ◽  
Biometric System ◽  
Multimodal Biometric System ◽  
Information Set

scholarly journals Feature and Decision Level Fusion in Children Multimodal Biometrics

2020 ◽  
Vol 8 (5) ◽  
pp. 2522-2527
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
Identification Accuracy ◽  
Support Vector ◽  
Biometric System ◽  
K Nearest Neighbor ◽  
Decision Level ◽  
Multimodal Biometric System ◽  
Decision Level Fusion ◽  
Level Fusion

In this paper, we design method for recognition of fingerprint and IRIS using feature level fusion and decision level fusion in Children multimodal biometric system. Initially, Histogram of Gradients (HOG), Gabour and Maximum filter response are extracted from both the domains of fingerprint and IRIS and considered for identification accuracy. The combination of feature vector of all the possible features is recommended by biometrics traits of fusion. For fusion vector the Principal Component Analysis (PCA) is used to select features. The reduced features are fed into fusion classifier of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Navie Bayes(NB). For children multimodal biometric system the suitable combination of features and fusion classifiers is identified. The experimentation conducted on children’s fingerprint and IRIS database and results reveal that fusion combination outperforms individual. In addition the proposed model advances the unimodal biometrics system.

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