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Convolutional Autoencoder Model for Finger-Vein Verification.
- Source :
-
IEEE Transactions on Instrumentation & Measurement . May2020, Vol. 69 Issue 5, p2067-2074. 8p. - Publication Year :
- 2020
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SUPPORT vector machines
*GABOR filters
Subjects
Details
- Language :
- English
- ISSN :
- 00189456
- Volume :
- 69
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Instrumentation & Measurement
- Publication Type :
- Academic Journal
- Accession number :
- 143313610
- Full Text :
- https://doi.org/10.1109/TIM.2019.2921135