The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Rangkaian saraf konvolusi dalam yang terlatih (DCNN) diguna pakai sebagai pengekstrak ciri untuk mengekstrak perwakilan ciri imej urat untuk pengecaman urat tangan-dorsa. Secara khusus, ciri konvolusi mendalam terpilih novel dicadangkan untuk mendapatkan perwakilan ciri yang lebih representatif dan diskriminatif. Eksperimen yang meluas pada pangkalan data buatan makmal memperoleh hasil pengiktirafan terkini, yang menunjukkan keberkesanan model yang dicadangkan.
Zaiyu PAN
China University of Mining and Technology
Jun WANG
China University of Mining and Technology
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Salinan
Zaiyu PAN, Jun WANG, "Hand-Dorsa Vein Recognition Based on Selective Deep Convolutional Feature" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 6, pp. 1423-1426, June 2020, doi: 10.1587/transinf.2019EDL8204.
Abstract: A pre-trained deep convolutional neural network (DCNN) is adopted as a feature extractor to extract the feature representation of vein images for hand-dorsa vein recognition. In specific, a novel selective deep convolutional feature is proposed to obtain more representative and discriminative feature representation. Extensive experiments on the lab-made database obtain the state-of-the-art recognition result, which demonstrates the effectiveness of the proposed model.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8204/_p
Salinan
@ARTICLE{e103-d_6_1423,
author={Zaiyu PAN, Jun WANG, },
journal={IEICE TRANSACTIONS on Information},
title={Hand-Dorsa Vein Recognition Based on Selective Deep Convolutional Feature},
year={2020},
volume={E103-D},
number={6},
pages={1423-1426},
abstract={A pre-trained deep convolutional neural network (DCNN) is adopted as a feature extractor to extract the feature representation of vein images for hand-dorsa vein recognition. In specific, a novel selective deep convolutional feature is proposed to obtain more representative and discriminative feature representation. Extensive experiments on the lab-made database obtain the state-of-the-art recognition result, which demonstrates the effectiveness of the proposed model.},
keywords={},
doi={10.1587/transinf.2019EDL8204},
ISSN={1745-1361},
month={June},}
Salinan
TY - JOUR
TI - Hand-Dorsa Vein Recognition Based on Selective Deep Convolutional Feature
T2 - IEICE TRANSACTIONS on Information
SP - 1423
EP - 1426
AU - Zaiyu PAN
AU - Jun WANG
PY - 2020
DO - 10.1587/transinf.2019EDL8204
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2020
AB - A pre-trained deep convolutional neural network (DCNN) is adopted as a feature extractor to extract the feature representation of vein images for hand-dorsa vein recognition. In specific, a novel selective deep convolutional feature is proposed to obtain more representative and discriminative feature representation. Extensive experiments on the lab-made database obtain the state-of-the-art recognition result, which demonstrates the effectiveness of the proposed model.
ER -