scholarly journals Score Level Fusion of Multimodal Biometrics based on Entropy Function

2016 ◽  
Vol 142 (9) ◽  
pp. 28-33 ◽  
Author(s):  
Sheikh Moeen ◽  
Moin Uddin ◽  
Jyotsana Grover
Keyword(s):  
Entropy Function ◽  
Multimodal Biometrics ◽  
Score Level Fusion ◽  
Level Fusion

scholarly journals Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5523 ◽  
Author(s):  
Nada Alay ◽  
Heyam H. Al-Baity
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Recognition Performance ◽  
Identification System ◽  
Biometric Recognition ◽  
Multimodal Biometrics ◽  
Score Level Fusion ◽  
Softmax Classifier ◽  
Finger Vein ◽  
Level Fusion

With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion.

Score level fusion of multimodal biometrics using triangular norms

2011 ◽  
Vol 32 (14) ◽  
pp. 1843-1850 ◽  
Author(s):  
Madasu Hanmandlu ◽  
Jyotsana Grover ◽  
Ankit Gureja ◽  
H.M. Gupta
Keyword(s):  
Multimodal Biometrics ◽  
Score Level Fusion ◽  
Triangular Norms ◽  
Level Fusion

Multimodal biometrics: Weighted score level fusion based on non-ideal iris and face images

2014 ◽  
Vol 41 (11) ◽  
pp. 5390-5404 ◽  
Author(s):  
Hiew Moi Sim ◽  
Hishammuddin Asmuni ◽  
Rohayanti Hassan ◽  
Razib M. Othman
Keyword(s):  
Multimodal Biometrics ◽  
Score Level Fusion ◽  
Face Images ◽  
Weighted Score ◽  
Level Fusion
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