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Patterns of Weber magnitude and orientation for uncontrolled face representation and recognition
Jiang Y.3; Wang B.3; Zhou Y.2; Li W.3; Liao Q.3
2015
Source PublicationNeurocomputing
ISSN18728286 09252312
Volume165Pages:190-201
Abstract

Robust, discriminative and computationally efficient feature extraction is vital for a successful real-world face recognition system. To this end, we present a novel local feature descriptor, named patterns of Weber magnitude and orientation (PWMO), for face representation and recognition. Instead of merely taking advantage of the pixel intensity which is sensitive to variant impact factors such as illumination variations and noises, we describe a pixel with two robust statistic attributes of the local patch centered around it: the histogram of Weber magnitude and the dominant Weber orientation. By encoding them in a self-similarity manner with patch-based local binary patterns (p-LBP) and patch-based local XOR patterns (p-LXP) respectively, we obtain a robust representation for face images. To further enhance the discriminative power, we extend PWMO to its multi-scale version, and apply the block-based Fisher's linear discriminant (BFLD) to reduce the dimensionality and select the most discriminative features. The Fisher separation criterion (FSC) based block weighting scheme is incorporated for discriminative classification. We evaluate the proposed face representation method on two publicly available face databases: FERET and FRGC version 2.0 experiment 4 (FRGC-204). The recognition results demonstrate that the proposed method performs much better than most of the state-of-the-arts, and achieves comparable recognition performance with the recently proposed state-of-the-art algorithm based on the fusion of Gabor magnitude and phase, with only 1/7 storage requirement and 1/10 computational cost.

KeywordFace Recognition Feature Extraction Fisher's Linear Discriminant (Fld) Local Binary Patterns (Lbp) Local Xor Patterns (Lxp) Weber's Law
DOIhttp://doi.org/10.1016/j.neucom.2015.03.009
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000356747700024
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Cited Times [WOS]:11   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorJiang Y.; Wang B.; Zhou Y.; Li W.; Liao Q.
Affiliation1.Shenzhen Key Laboratory of Quantum Science and Engineering
2.Universidade de Macau
3.Tsinghua University
Recommended Citation
GB/T 7714
Jiang Y.,Wang B.,Zhou Y.,et al. Patterns of Weber magnitude and orientation for uncontrolled face representation and recognition[J]. Neurocomputing,2015,165:190-201.
APA Jiang Y.,Wang B.,Zhou Y.,Li W.,&Liao Q..(2015).Patterns of Weber magnitude and orientation for uncontrolled face representation and recognition.Neurocomputing,165,190-201.
MLA Jiang Y.,et al."Patterns of Weber magnitude and orientation for uncontrolled face representation and recognition".Neurocomputing 165(2015):190-201.
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