Deep Representation-Based Feature Extraction and Recovering for Finger-Vein Verification

Finger-vein biometrics has been extensively investigated for personal verification. Despite recent advances in finger-vein verification, current solutions completely depend on domain knowledge and still lack the robustness to extract finger-vein features from raw images. This paper proposes a deep l...

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Veröffentlicht in:	IEEE transactions on information forensics and security Jg. 12; H. 8; S. 1816 - 1829
Hauptverfasser:	Qin, Huafeng, El-Yacoubi, Mounim A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Schlagworte:	Artificial neural networks Biometric recognition systems Computer Science Computer Vision and Pattern Recognition convolutional autoencoder convolutional neural network deep learning Feature extraction finger-vein verification Hand biometrics Image segmentation Iris recognition Machine learning Neural and Evolutionary Computing Neural networks representation learning Representations Signal and Image Processing Veins Representation learning Deep learning Hand biometrics Finger-vein verification Convolutional autoencoder Convolutional neural network
ISSN:	1556-6013, 1556-6021
Online-Zugang:	Volltext
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