Multimodal Biometric Authentication System for Smartphone Based on Face and Voice Using Matching Level Fusion

2018 ◽  
Author(s):  
Xinman Zhang ◽  
Dongxu Cheng ◽  
Yixuan Dai ◽  
Xuebin Xu
Keyword(s):  
Biometric Authentication ◽  
Authentication System ◽  
Level Fusion

A Novel Biometric Authentication System with Score Level Fusion

2017 ◽  
Vol 4 (3) ◽  
pp. 383-404
Author(s):  
Ramesh Naidu Balaka ◽  
Prasad Babu Maddali Surendra
Keyword(s):  
Biometric Authentication ◽  
Score Level Fusion ◽  
Authentication System ◽  
Level Fusion

Fusion Time Reduction of a Feature Level Based Multimodal Biometric Authentication System

2020 ◽  
Vol 12 (1) ◽  
pp. 67-83
Author(s):  
Rasha O. Mahmoud ◽  
Mazen M. Selim ◽  
Omar A. Muhi
Keyword(s):  
Canonical Correlation ◽  
Recognition Accuracy ◽  
Biometric Authentication ◽  
Equal Error Rate ◽  
Histogram Of Oriented Gradient ◽  
The Face ◽  
Left And Right ◽  
Authentication System ◽  
Correlation Process ◽  
Level Fusion

In the present study, a multimodal biometric authentication method is presented to confirm the identity of a person based on his face and iris features. This method depends on multiple biometric techniques that combine face and iris (left and right) features to recognize. The authors have designed and applied a system to identify people. It depends on extracting the features of the face using Rectangle Histogram of Oriented Gradient (R-HOG). The study applies a feature-level fusion using a novel fusion method which employs both the canonical correlation process and the proposed serial concatenation. A deep belief network was used for the recognition process. The performance of the proposed systems was validated and evaluated through a set of experiments on SDUMLA-HMT database. The results were compared with others, and have shown that the fusion time has been reduced by about 34.5%. The proposed system has also succeeded in achieving a lower equal error rate (EER), and a recognition accuracy up to 99%.

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