Unsupervised Palmprint Image Quality Assessment via Pseudo-Label Generation and Ranking Guidance
Author:
Affiliation:
1. School of Automation Science and Engineering, Xi’an Jiaotong University, Shaanxi, Xi’an, China
2. School of Automation Science and Engineering and School of Mathematics and Statistics, Xi’an Jiaotong University, Shaanxi, Xi’an, China
Funder
National Natural Science Foundation of China
Natural Science Foundation of Zhejiang Province
Young Talent Fund of Association for Science and Technology in Shaanxi, China
Xi’an Science and Technology Project
Fundamental Research Funds for the Central Universities
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10158718.pdf?arnumber=10158718
Reference68 articles.
1. Deep Distillation Hashing for Unconstrained Palmprint Recognition
2. Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
3. Towards palmprint verification on smartphones;zhang;arXiv 2003 13266,2020
4. Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation
5. Distribution alignment for cross-device palmprint recognition
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