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BDPCA Plus LDA: A Novel Fast Feature Extraction Technique for Face Recognition
授課教授 : 連震杰 老師
組員 : 黃彥綸 何域禎
W. Zuo, D. Zhang, J. Yang, K. Wang, “BBPCA plus LDA: a novel fast feauture extractiontechnique for face recognition,” IEEE Transactions on Systems, Man, andCybernetics, vol. 36, no. 4, pp. 946-953, Aug. 2006.
Outline
• Introduction• Principal Component Analysis (PCA)• Bidirectional Principal Component Analysis(BDPCA)• Image Reconstruction• BDPCA plus LDA Technique• Experiments
Introduction
• Geometric-based approachesFeature detectionHigh recognition rate.Feature location is difference from people
to people.
Introduction
• Holistic-based approachesRobust recognition performance under
noise, blurring, and partial occlusion.EX:PCA(extract eigenfaces) 、 LDA(has SSS
problem)
Q&A
Q:What is small sample size (SSS) problem?A:In LDA the rank of Sw must be ,
then exist. For example, the ORL database image is 112x92 size, Sw :(112x92)x(112x92), and has 40 classifications, it must has 10344 pictures for training, but there is no a so large database.
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Q&A
Q:How to solve the SSS problem?A:Before using LDA, we first do image
dimensionality reduction, such as PCA, BDPCA.
PCA
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•Feature extraction:eigenfaces method•Data compression•Image dimensionality reduction•Fail to classfication=>LDA
Flowchart
Find LDA projector
Mapping data to BDPCA subspace
Mapping data to LDA subspace
KNN to get the face recognition rate
TestingTraining
Mapping data to LDA subspace
Mapping data to BDPCA subspace
Find BDPCA projector
Image dimensionality reduction
BDPCA• Bidirectional PCA(BDPCA)
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row projection matrix column projection matrix
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Y: Feature matrix
Image Reconstruction
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PCA BDPCAOriginal image
Training
Testing
MSE curves
Training Testing
BDPCA plus LDA Technique
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)det(maxarg
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wb SS Generalized eigendecomposition
yWz TLDA
rTc XWWY Mapping Y into its 1D representation y
mLDAW ,...,2,1
Between-class scatter matrix of y
Within-class scatter matrix of y
The LDA projector
The final feature vector
AdvantagePCA
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BDPCA
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CPU time
On ORL face database
Experiments
• To test the efficacy of BDPCA + LDA, we make use of two face databases, the ORL face database and the FERET database.
• Since our aim is to evaluate the efficacy of feature extraction methods, we use a simple classifier, the nearest neighbor classifier.
Comparisons of the recognition rates
ORL database FERET database
Conclusions
• The BDPCA +LDA has a much faster speed for facial feature extraction.
• The BDPCA + LDA needs less memory requirement because its projector is much smaller than that of the PCA + LDA.
• The BDPCA + LDA has a higher recognition accuracy over the PCA + LDA.