Blurred Palmprint Recognition Based on Relative Invariant Structure Feature

2015 ◽  
Author(s):  
Gang Wang ◽  
Weibo Wei ◽  
Zhenkuan Pan
Keyword(s):  
Palmprint Recognition ◽  
Invariant Structure ◽  
Structure Feature ◽  
Relative Invariant

Carbide precipitation and chromium depletion on coherent twin boundaries in deformed 304 stainless steels

1992 ◽  
Vol 50 (2) ◽  
pp. 1216-1217
Author(s):  
A.H. Advani ◽  
L.E. Murr ◽  
D.J. Matlock ◽  
W.W. Fisher ◽  
P.M. Tarin ◽  
...  
Keyword(s):  
Grain Boundaries ◽  
Stainless Steels ◽  
Interfacial Free Energy ◽  
Carbide Precipitation ◽  
Twin Boundaries ◽  
Invariant Structure ◽  
Heat Treated ◽  
Constant Structure ◽  
Cr Depletion

Coherent annealing-twin boundaries are constant structure and energy interfaces with an average interfacial free energy of ∼19mJ/m2 versus ∼210 and ∼835mJ/m2 for incoherent twins and “regular” grain boundaries respectively in 304 stainless steels (SS). Due to their low energy, coherent twins form carbides about a factor of 100 slower than grain boundaries, and limited work has also shown differences in Cr-depletion (sensitization) between twin versus grain boundaries. Plastic deformation, may, however, alter the kinetics and thermodynamics of twin-sensitization which is not well understood. The objective of this work was to understand the mechanisms of carbide precipitation and Cr-depletion on coherent twin boundaries in deformed SS. The research is directed toward using this invariant structure and energy interface to understand and model the role of interfacial characteristics on deformation-induced sensitization in SS. Carbides and Cr-depletion were examined on a 20%-strain, 0.051%C-304SS, heat treated to 625°C-4.5h, as described elsewhere.

An Answer Extraction Algorithm Based on Syntax Structure Feature Parsing and Classification

2009 ◽  
Vol 31 (4) ◽  
pp. 662-676 ◽  
Author(s):  
Bao-Shun HU ◽  
Da-Ling WANG ◽  
Ge YU ◽  
Ting MA
Keyword(s):  
Structure Feature ◽  
Answer Extraction ◽  
Extraction Algorithm

scholarly journals 2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search

2021 ◽  
Author(s):  
Wei Jia ◽  
Wei Xia ◽  
Yang Zhao ◽  
Hai Min ◽  
Yan-Xiang Chen
Keyword(s):  
Deep Learning ◽  
Recognition Performance ◽  
Research Direction ◽  
Palmprint Recognition ◽  
Neural Architecture ◽  
Development Direction ◽  
Vein Recognition ◽  
Palm Vein ◽  
2D And 3D ◽  
Important Research Direction

AbstractPalmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition and have achieved impressive results. In recent years, in the field of artificial intelligence, deep learning has gradually become the mainstream recognition technology because of its excellent recognition performance. Some researchers have tried to use convolutional neural networks (CNNs) for palmprint recognition and palm vein recognition. However, the architectures of these CNNs have mostly been developed manually by human experts, which is a time-consuming and error-prone process. In order to overcome some shortcomings of manually designed CNN, neural architecture search (NAS) technology has become an important research direction of deep learning. The significance of NAS is to solve the deep learning model’s parameter adjustment problem, which is a cross-study combining optimization and machine learning. NAS technology represents the future development direction of deep learning. However, up to now, NAS technology has not been well studied for palmprint recognition and palm vein recognition. In this paper, in order to investigate the problem of NAS-based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct a performance evaluation of twenty representative NAS methods on five 2D palmprint databases, two palm vein databases, and one 3D palmprint database. Experimental results show that some NAS methods can achieve promising recognition results. Remarkably, among different evaluated NAS methods, ProxylessNAS achieves the best recognition performance.

scholarly journals Structure-Feature based Graph Self-adaptive Pooling

2020 ◽  
Author(s):  
Liang Zhang ◽  
Xudong Wang ◽  
Hongsheng Li ◽  
Guangming Zhu ◽  
Peiyi Shen ◽  
...  
Keyword(s):  
Structure Feature ◽  
Feature Based ◽  
Self Adaptive

Jointly Learning Multiple Curvature Descriptor for 3D Palmprint Recognition

2021 ◽  
Author(s):  
Lunke Fei ◽  
Jianyang Qin ◽  
Peng Liu ◽  
Jie Wen ◽  
Chunwei Tian ◽  
...  
Keyword(s):  
Palmprint Recognition
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