TY - JOUR
T1 - Few-Shot Learning for Palmprint Recognition via Meta-Siamese Network
AU - Shao, Huikai
AU - Zhong, Dexing
AU - Du, Xuefeng
AU - Du, Shaoyi
AU - Veldhuis, Raymond N.J.
N1 - Funding Information:
Manuscript received March 16, 2021; revised April 19, 2021; accepted April 24, 2021. Date of publication April 30, 2021; date of current version May 14, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61105021, in part by the Natural Science Foundation of Zhejiang Province under Grant LGF19F030002, in part by the Natural Science Foundation of Shaanxi Province under Grant 2020JM-073, in part by the Fundamental Research Funds for the Central Universities under Grant xzy022020051, and in part by the China Scholarship Council. The Associate Editor coordinating the review process was Hongrui Wang. (Corresponding author: Dexing Zhong.) Huikai Shao is with the School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China (e-mail: [email protected]).
Publisher Copyright:
© 1963-2012 IEEE.
PY - 2021
Y1 - 2021
N2 - Palmprint is one of the discriminant biometric modalities of humans. Recently, deep learning-based palmprint recognition algorithms have improved the accuracy and robustness of recognition results to a new level. Most of them require a large amount of labeled training samples to guarantee satisfactory performance. However, getting enough labeled data is difficult due to time consumption and privacy issues. Therefore, in this article, a novel meta-Siamese network (MSN) is proposed to exploit few-shot learning for small-sample palmprint recognition. During each episode-based training iteration, a few images are selected as sample and query sets to simulate the support and testing sets in the test set. Specifically, the model is trained episodically with a flexible framework to learn both the feature embedding and deep similarity metric function. In addition, two distance-based losses are introduced to assist the optimization. After training, the model can learn the ability to get similarity scores between two images for few-shot testing. Adequate experiments conducted on several constrained and unconstrained benchmark palmprint databases show that MSN can obtain competitive improvements compared with baseline methods, where the best accuracy can be up to 100%.
AB - Palmprint is one of the discriminant biometric modalities of humans. Recently, deep learning-based palmprint recognition algorithms have improved the accuracy and robustness of recognition results to a new level. Most of them require a large amount of labeled training samples to guarantee satisfactory performance. However, getting enough labeled data is difficult due to time consumption and privacy issues. Therefore, in this article, a novel meta-Siamese network (MSN) is proposed to exploit few-shot learning for small-sample palmprint recognition. During each episode-based training iteration, a few images are selected as sample and query sets to simulate the support and testing sets in the test set. Specifically, the model is trained episodically with a flexible framework to learn both the feature embedding and deep similarity metric function. In addition, two distance-based losses are introduced to assist the optimization. After training, the model can learn the ability to get similarity scores between two images for few-shot testing. Adequate experiments conducted on several constrained and unconstrained benchmark palmprint databases show that MSN can obtain competitive improvements compared with baseline methods, where the best accuracy can be up to 100%.
KW - 2022 OA procedure
KW - Biometrics
KW - Computational modeling
KW - Databases
KW - Feature extraction
KW - Few-shot learning
KW - Information security
KW - Meta-learning
KW - Palmprint recognition
KW - Task analysis
KW - Training
KW - Benchmark testing
UR - http://www.scopus.com/inward/record.url?scp=85105052331&partnerID=8YFLogxK
U2 - 10.1109/TIM.2021.3076850
DO - 10.1109/TIM.2021.3076850
M3 - Article
AN - SCOPUS:85105052331
SN - 0018-9456
VL - 70
JO - IEEE transactions on instrumentation and measurement
JF - IEEE transactions on instrumentation and measurement
M1 - 9420130
ER -