Proceedings ArticleDOI
LBP Discriminant Analysis for Face Verification
Jiali Zhao,Haitao Wang,Haibing Ren,Seok-Cheol Kee +3 more
- pp 167-167
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TLDR
A novel Local Binary Pattern based Kernel Fisher Discriminant Analysis (KFDA) approach by integrating the LBP descriptor of face images and the KFDA method for face classifier for improved face verification performance is presented.Abstract:
This paper presents a novel Local Binary Pattern (LBP) based Kernel Fisher Discriminant Analysis (KFDA) approach by integrating the LBP descriptor of face images and the KFDA method for face classifier. LBP extracts desirable facial features which consider both shape and texture information to cope with the variation due to facial expression and illumination changes. The KFDA method is then extended to take all the advantages of LBP descriptor for improved face verification performance. We introduce the kernel function by using Chi square statistic distance and RBF as inner product for KFDA classifier. The effectiveness of the LBP based KFDA method with Chi square statistic distance as inner product is shown in terms of comparison with original LBP and KFDA methods on FRGC database.read more
Citations
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Journal ArticleDOI
Local Binary Patterns and Its Application to Facial Image Analysis: A Survey
TL;DR: As a typical application of the LBP approach, LBP-based facial image analysis is extensively reviewed, while its successful extensions, which deal with various tasks of facial imageAnalysis, are also highlighted.
Book ChapterDOI
Multi-scale local binary pattern histograms for face recognition
TL;DR: In this paper, a discriminative face representation derived by the Linear Discriminant Analysis (LDA) of multi-scale local binary pattern histograms is proposed for face recognition.
Journal ArticleDOI
Recognition of faces in unconstrained environments: a comparative study
TL;DR: There is a large dependence of the methods on the amount of face and background information that is included in the face's images, and the performance of all methods decreases largely with outdoor-illumination, but LBP-based methods are an excellent election if the authors need real-time operation as well as high recognition rates.
Journal ArticleDOI
Multiscale Local Phase Quantization for Robust Component-Based Face Recognition Using Kernel Fusion of Multiple Descriptors
TL;DR: A novel blur-robust face image descriptor based on Local Phase Quantization is proposed and extended to a multiscale framework (MLPQ) to increase its effectiveness and provide a new insight into the merits of various face representation and fusion methods, as well as their role in dealing with variable lighting and blur degradation.
References
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