Histogram of Oriented Lines for Palmprint Recognition

Subspace learning methods are very sensitive to the illumination, translation, and rotation variances in image recognition. Thus, they have not obtained promising performance for palmprint recognition so far. In this paper, we propose a new descriptor of palmprint named histogram of oriented lines (...

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Bibliographic Details
Published inIEEE transactions on systems, man, and cybernetics. Systems Vol. 44; no. 3; pp. 385 - 395
Main Authors Jia, Wei, Hu, Rong-Xiang, Lei, Ying-Ke, Zhao, Yang, Gui, Jie
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.03.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptative systems
Applied sciences
Artificial intelligence
Biometric
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Control theory. Systems
Exact sciences and technology
Feature extraction
histogram of oriented gradients (HOG)
Histograms
Illumination
Learning
Learning and adaptive systems
Learning systems
Lighting
palm line
Palmprint recognition
Pattern recognition. Digital image processing. Computational geometry
Principal component analysis
Recognition
Robustness
Software
subspace learning
Subspaces
Transformations
Translations
Biometrics
Computer vision
Biometric
Histogram
Image recognition
Subspace method
High order logic
Hand
Variance
subspace learning
Luminance
palmprint recognition
Palm
Object detection
Illumination
Robustness
palm line
histogram of oriented gradients (HOG)
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Summary:Subspace learning methods are very sensitive to the illumination, translation, and rotation variances in image recognition. Thus, they have not obtained promising performance for palmprint recognition so far. In this paper, we propose a new descriptor of palmprint named histogram of oriented lines (HOL), which is a variant of histogram of oriented gradients (HOG). HOL is not very sensitive to changes of illumination, and has the robustness against small transformations because slight translations and rotations make small histogram value changes. Based on HOL, even some simple subspace learning methods can achieve high recognition rates.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2013.2258010