An Adaptive Score Level Fusion Scheme for Multimodal Biometric Systems

2015 ◽  
pp. 119-131 ◽  
Author(s):  
Kamlesh Tiwari ◽  
Phalguni Gupta
Keyword(s):  
Score Level Fusion ◽  
Biometric Systems ◽  
Fusion Scheme ◽  
Level Fusion ◽  
Multimodal Biometric Systems

Performance evaluation of score level fusion in multimodal biometric systems

2010 ◽  
Vol 43 (5) ◽  
pp. 1789-1800 ◽  
Author(s):  
Mingxing He ◽  
Shi-Jinn Horng ◽  
Pingzhi Fan ◽  
Ray-Shine Run ◽  
Rong-Jian Chen ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Score Level Fusion ◽  
Biometric Systems ◽  
Level Fusion ◽  
Multimodal Biometric Systems

Score Level Fusion Scheme in Hybrid Multibiometric System

2015 ◽  
pp. 166-177 ◽  
Author(s):  
Saliha Artabaz ◽  
Layth Sliman ◽  
Karima Benatchba ◽  
Hachemi Nabil Dellys ◽  
Mouloud Koudil
Keyword(s):  
Score Level Fusion ◽  
Fusion Scheme ◽  
Level Fusion

Differential evolution based score level fusion for multi-modal biometric systems

2014 ◽  
Author(s):  
Satrajit Mukherjee ◽  
Kunal Pal ◽  
Bodhisattwa Prasad Majumder ◽  
Chiranjib Saha ◽  
B. K. Panigrahi ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Score Level Fusion ◽  
Biometric Systems ◽  
Level Fusion

scholarly journals Incorporating Metadata in Multibiometric Score-Level Fusion: an Optimized Architecture

2019 ◽  
Vol 9 (1) ◽  
pp. 5290-5305
Keyword(s):  
Skin Color ◽  
Affective Computing ◽  
Biometric Data ◽  
Score Level Fusion ◽  
New Concepts ◽  
Biometric Systems ◽  
The Face ◽  
Ancillary Information ◽  
Biometric Information ◽  
Level Fusion

This manuscript presents a review on multibiometrics using ancillary information, in addition to the main biometric data. The proposed method involves taking non-biometric information into account in the biometric recognition process to improve system performance. This ancillary information can come from the user (the skin color), the sensor (the camera flash, etc.) or the operating environment (the ambient noise). Moreover, the paper presents an extension of the adapted sequential fusion framework through a complete description of the method used for the score-level fusion architecture presented at the IEEE BioSmart 2019 Proceedings. An optimized score-level fusion architecture is proposed. An introduction of new concepts (namely “biochemical features” and “multi origin biometrics”) is also made. The first part of the paper highlights the various biometric systems developed up to now, their architecture and characteristics. Then, the manuscript discussed about multibiometrics through its advantages, its diversity and the different levels of fusion. An attention was paid to the score-level fusion before addressing the consideration of ancillary information (or metadata) in multibiometrics. Dealing with the affective computing, the influence of emotion on the performance of biometric systems is explored. Finally, a typology of biometric adaptation is discussed. As an application, the proposed methodology will implement a multibiometric system using the face, contactless fingerprint and skin color. A single sensor will be used (a camera) with two shots while the skin color will be extracted automatically from the facial image.

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