scholarly journals Robust Face Recognition Using the Deep C2D-CNN Model Based on Decision-Level Fusion

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2080 ◽  
Author(s):  
Jing Li ◽  
Tao Qiu ◽  
Chang Wen ◽  
Kai Xie ◽  
Fang-Qing Wen
Keyword(s):  
Face Recognition ◽  
Decision Level ◽  
Model Based ◽  
Decision Level Fusion ◽  
Robust Face Recognition ◽  
Level Fusion

Decision Level Fusion

2011 ◽  
pp. 328-348 ◽  
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Face Recognition ◽  
Fusion Rule ◽  
Fusion Rules ◽  
Decision Level ◽  
Classifier Selection ◽  
Selection Approach ◽  
Decision Level Fusion ◽  
Level Fusion ◽  
Biometric Fusion

With this chapter, we first present a variety of decision level fusion rules and classifier selection approaches, and then show a case study of face recognition based on decision level fusion, and finally offer a summary of three levels of biometric fusion technologies. In a multi-biometric system, classifier selection techniques may be associated with the decision level fusion as follows: classifier selection is first carried out to select a number of classifiers from all classifier candidates. Then the selected classifiers make their own decisions and the decision level fusion rule is used to integrate the multiple decisions to produce the final decision. As a result, in this chapter, we also introduce classifier selection by showing a classifier selection approach based on correlation analysis. This chapter is organized as follows. Section 15.1 provides an introduction to decision level fusion. Section 15.2 presents several simple and popular decision level fusion rules such as the AND, OR, RANDOM, Voting rules, as well as the weighted majority decision rule. Section 15.3 introduces a classifier selection approach based on correlations between classifiers. Section 15.4 presents a case study of group decision-based face recognition. Finally, Section 15.5 offers some comments on three levels of biometric 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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