Data and Decision Level Fusion of Temporal Informationfor Automatic Target Recognition

2000 ◽  
Author(s):  
K Messer ◽  
J Kittler
Keyword(s):  
Target Recognition ◽  
Automatic Target Recognition ◽  
Decision Level ◽  
Decision Level Fusion ◽  
Level Fusion

Survey of approaches and experiments in decision-level fusion of automatic target recognition (ATR) products

2007 ◽  
Author(s):  
Timothy D. Ross ◽  
Doug R. Morgan ◽  
Erik P. Blasch ◽  
Kyle J. Erickson ◽  
Bart D. Kahler
Keyword(s):  
Target Recognition ◽  
Automatic Target Recognition ◽  
Decision Level ◽  
Decision Level Fusion ◽  
Level Fusion

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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