Open AccessMultimodal biometric recognition systems using deep learning based on the finger vein and finger knuckle print fusion
Author(s) -
Daas Sara,
Yahi Amira,
Bakir Toufik,
Sedhane Mouna,
Boughazi Mohamed,
Bourennane ElBay
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2020.0491
Subject(s) - biometrics , computer science , artificial intelligence , convolutional neural network , softmax function , palm print , pattern recognition (psychology) , deep learning , speech recognition , feature extraction , machine learning
Recognition systems using multimodal biometrics attracts attention because they improve recognition efficiency and high‐security level compared to the unimodal biometrics system. In this study, the authors present a secure multimodal biometrics recognition system based on the deep learning method that uses convolutional neural networks (CNNs). The authors propose two multimodal architectures using the finger knuckle print (FKP) and the finger vein (FV) biometrics with different levels of fusion: the features level fusion and scores level fusion. The features extraction for FKP and FV are performed using transfer learning CNN architectures: AlexNet, VGG16, and ResNet50. The key step aims to select separate features descriptors from each unimodal biometrics modality. After that, the authors combine them using the proposed fusion approaches were support vector machine or Softmax applies as classifiers to increase the proposed system security. The efficiency of the proposed algorithms is tested using publicly available biometrics databases. The experimental results show that the proposed fusion architectures achieve an accuracy of 99.89% and an equal error rate of 0.05%. The obtained results indicate that the biometrics recognition system using deep learning is secure, robust, and reliable.