PMID- 35349454
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231202
IS  - 2162-2388 (Electronic)
IS  - 2162-237X (Linking)
VI  - 34
IP  - 12
DP  - 2023 Dec
TI  - Toward Efficient Palmprint Feature Extraction by Learning a Single-Layer 
      Convolution Network.
PG  - 9783-9794
LID - 10.1109/TNNLS.2022.3160597 [doi]
AB  - In this article, we propose a collaborative palmprint-specific binary feature 
      learning method and a compact network consisting of a single convolution layer 
      for efficient palmprint feature extraction. Unlike most existing palmprint 
      feature learning methods, such as deep-learning, which usually ignore the 
      inherent characteristics of palmprints and learn features from raw pixels of a 
      massive number of labeled samples, palmprint-specific information, such as the 
      direction and edge of patterns, is characterized by forming two kinds of ordinal 
      measure vectors (OMVs). Then, collaborative binary feature codes are jointly 
      learned by projecting double OMVs into complementary feature spaces in an 
      unsupervised manner. Furthermore, the elements of feature projection functions 
      are integrated into OMV extraction filters to obtain a collection of cascaded 
      convolution templates that form a single-layer convolution network (SLCN) to 
      efficiently obtain the binary feature codes of a new palmprint image within a 
      single-stage convolution operation. Particularly, our proposed method can easily 
      be extended to a general version that can efficiently perform feature extraction 
      with more than two types of OMVs. Experimental results on five benchmark 
      databases show that our proposed method achieves very promising feature 
      extraction efficiency for palmprint recognition.
FAU - Fei, Lunke
AU  - Fei L
FAU - Zhao, Shuping
AU  - Zhao S
FAU - Jia, Wei
AU  - Jia W
FAU - Zhang, Bob
AU  - Zhang B
FAU - Wen, Jie
AU  - Wen J
FAU - Xu, Yong
AU  - Xu Y
LA  - eng
PT  - Journal Article
DEP - 20231130
PL  - United States
TA  - IEEE Trans Neural Netw Learn Syst
JT  - IEEE transactions on neural networks and learning systems
JID - 101616214
SB  - IM
EDAT- 2022/03/30 06:00
MHDA- 2022/03/30 06:01
CRDT- 2022/03/29 17:12
PHST- 2022/03/30 06:01 [medline]
PHST- 2022/03/30 06:00 [pubmed]
PHST- 2022/03/29 17:12 [entrez]
AID - 10.1109/TNNLS.2022.3160597 [doi]
PST - ppublish
SO  - IEEE Trans Neural Netw Learn Syst. 2023 Dec;34(12):9783-9794. doi: 
      10.1109/TNNLS.2022.3160597. Epub 2023 Nov 30.