Convolutional Autoencoder Model for Finger-Vein Verification

This paper presents a novel deep learning-based method that integrates a Convolutional Auto-Encoder (CAE) with support vector machine (SVM) for finger-vein verification. The CAE is used to learn the features from finger-vein images, and the SVM is used to classify finger vein from these learned feat...

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Veröffentlicht in:IEEE transactions on instrumentation and measurement Jg. 69; H. 5; S. 2067 - 2074
Hauptverfasser: Hou, Borui, Yan, Ruqiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Biometrics
Coders
convolutional autoencoder (CAE)
Convolutional codes
Deep learning
Feature extraction
finger-vein
Fingers
Gabor filters
Image classification
Image reconstruction
Machine learning
support vector machine (SVM)
Support vector machines
Veins
Verification
ISSN:0018-9456, 1557-9662
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Beschreibung
Zusammenfassung:This paper presents a novel deep learning-based method that integrates a Convolutional Auto-Encoder (CAE) with support vector machine (SVM) for finger-vein verification. The CAE is used to learn the features from finger-vein images, and the SVM is used to classify finger vein from these learned feature codes. The CAE consists of a finger-vein encoder, which extracts high-level feature representation from raw pixels of the images, and a decoder which outputs reconstruct finger-vein images from high-level feature code. As an effective classifier, SVM is introduced in this paper to classify the feature code which is obtained from CAE. Experiments prove that the proposed deep learning-based approach has superior performance in learning features than traditional method without any prior knowledge, presenting a good potential in the verification of finger vein.
Bibliographie:ObjectType-Article-1
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2019.2921135