A Multimodal Biometric Recognition of Touched Fingerprint and Finger-Vein

2011 ◽  
Author(s):  
Young Ho Park ◽  
Dat Nguyen Tien ◽  
Hyeon Chang Lee ◽  
Kang Ryoung Park ◽  
Eui Chul Lee ◽  
...  
Keyword(s):  
Biometric Recognition ◽  
Finger Vein ◽  
Multimodal Biometric Recognition

Multimodal biometric recognition systems using deep learning based on the finger vein and finger knuckle print fusion

2020 ◽  
Vol 14 (15) ◽  
pp. 3859-3868
Author(s):  
Sara Daas ◽  
Amira Yahi ◽  
Toufik Bakir ◽  
Mouna Sedhane ◽  
Mohamed Boughazi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Biometric Recognition ◽  
Finger Vein ◽  
Recognition Systems ◽  
Multimodal Biometric Recognition

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.

scholarly journals Finger Vein Recognition Based on (2D)2PCA and Metric Learning

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Gongping Yang ◽  
Xiaoming Xi ◽  
Yilong Yin
Keyword(s):  
Metric Learning ◽  
Recognition Rate ◽  
Class Imbalance ◽  
Biometric Recognition ◽  
Class Imbalance Problem ◽  
Finger Vein ◽  
Imbalance Problem ◽  
Knn Classifier ◽  
Vein Recognition ◽  
Finger Vein Recognition

Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. In this paper, (2D)2PCA is applied to extract features of finger veins, based on which a new recognition method is proposed in conjunction with metric learning. It learns a KNN classifier for each individual, which is different from the traditional methods where a fixed threshold is employed for all individuals. Besides, the SMOTE technology is adopted to solve the class-imbalance problem. Our experiments show that the proposed method is effective by achieving a recognition rate of 99.17%.

scholarly journals A Hybrid Model Combining Learning Distance Metric and DAG Support Vector Machine for Multimodal Biometric Recognition

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 4784-4796
Author(s):  
Ibrahim Omara ◽  
Ahmed Hagag ◽  
Souleyman Chaib ◽  
Guangzhi Ma ◽  
Fathi E. Abd El-Samie ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hybrid Model ◽  
Support Vector ◽  
Distance Metric ◽  
Biometric Recognition ◽  
Model Combining ◽  
Multimodal Biometric Recognition

A hybrid fusion model of iris, palm vein and finger vein for multi-biometric recognition system

2020 ◽  
Vol 79 (39-40) ◽  
pp. 29021-29042
Author(s):  
Chenyi Zhou ◽  
Jing Huang ◽  
Feng Yang ◽  
Yaqin Liu
Keyword(s):  
Recognition System ◽  
Fusion Model ◽  
Biometric Recognition ◽  
Finger Vein ◽  
Palm Vein ◽  
Hybrid Fusion
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