0000000000820342

AUTHOR

Sara Daas

showing 1 related works from this author

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

2020

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 …

Image fusionBiometricsbusiness.industryComputer scienceDeep learningFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONWord error rate020206 networking & telecommunicationsPattern recognition02 engineering and technologyConvolutional neural networkSupport vector machineSignal ProcessingSoftmax function0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligenceElectrical and Electronic EngineeringbusinessSoftwareIET Image Processing
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