以區域二元圖樣與部分比對為基礎之 人臉辨識 face recognition with local binary...
TRANSCRIPT
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以區域二元圖樣與部分比對為基礎之人臉辨識Face Recognition with Local Binary
Patterns and Partial Matching
Presenter: 施佩汝Advisor: 歐陽明 教授
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Outlines
• Motivation• Implementation• Result• Conclusion
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MOTIVATION
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Motivation
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Publication
• Che-Hua Yeh, Pei-Ruu Shih, Kuan-Ting Liu, Yin-Tzu Lin, Huang-Ming Chang, Ming Ouhyoung. A Comparison of Three Methods of Face Recognition for Home Photos. ACM Siggraph, poster, 2009.
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Problem Statement
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Main Contribution
• Improve Local Binary Patterns by using Partial Matching Metric
• Better Performance in Home Photos
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IMPLEMENTATION
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System Overview
Pre-Processing
Build Descriptor
Images Descriptors
ClusteringCalculate
LBPBuild
Descriptor
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System Overview
Pre-Processing
Build Descriptor
Images Descriptors
ClusteringCalculate
LBPBuild
Descriptor
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Pre-Processing
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System Overview
Pre-Processing
Build Descriptor
Images Descriptors
ClusteringCalculate
LBPBuild
Descriptor
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System Overview
Pre-Processing
Build Descriptor
Images Descriptors
ClusteringCalculate
LBPBuild
Descriptor
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Local Binary Patterns [PAMI2006]
• An operator to encode the relationship of a pixel and its neighbors. Z1 Z2 Z3
Z8 Z0 Z4
Z7 Z6 Z5
58 99 21
54 54 86
67 12 13
1
otherwise ,0
0 if ,1
28
10
xxs
ZZsLBP p
pp
14
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Local Binary Patterns [PAMI2006]
• An operator to encode the relationship of a pixel and its neighbors.
58 99 21
54 54 86
67 12 13
1 1
otherwise ,0
0 if ,1
28
10
xxs
ZZsLBP p
pp
Z1 Z2 Z3
Z8 Z0 Z4
Z7 Z6 Z5
15
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Local Binary Patterns [PAMI2006]
• An operator to encode the relationship of a pixel and its neighbors.
58 99 21
54 54 86
67 12 13
1 1 0
otherwise ,0
0 if ,1
28
10
xxs
ZZsLBP p
pp
Z1 Z2 Z3
Z8 Z0 Z4
Z7 Z6 Z5
16
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Local Binary Patterns [PAMI2006]
• An operator to encode the relationship of a pixel and its neighbors.
58 99 21
54 54 86
67 12 13
1 1 0
1
otherwise ,0
0 if ,1
28
10
xxs
ZZsLBP p
pp
Z1 Z2 Z3
Z8 Z0 Z4
Z7 Z6 Z5
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Local Binary Patterns [PAMI2006]
• An operator to encode the relationship of a pixel and its neighbors.
58 99 21
54 54 86
67 12 13
1 1 0
1
0
otherwise ,0
0 if ,1
28
10
xxs
ZZsLBP p
pp
Z1 Z2 Z3
Z8 Z0 Z4
Z7 Z6 Z5
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Local Binary Patterns [PAMI2006]
• An operator to encode the relationship of a pixel and its neighbors.
58 99 21
54 54 86
67 12 13
1 1 0
1
0 0
otherwise ,0
0 if ,1
28
10
xxs
ZZsLBP p
pp
Z1 Z2 Z3
Z8 Z0 Z4
Z7 Z6 Z5
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Local Binary Patterns [PAMI2006]
• An operator to encode the relationship of a pixel and its neighbors.
58 99 21
54 54 86
67 12 13
1 1 0
1
1 0 0
otherwise ,0
0 if ,1
28
10
xxs
ZZsLBP p
pp
Z1 Z2 Z3
Z8 Z0 Z4
Z7 Z6 Z5
20
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Local Binary Patterns [PAMI2006]
• An operator to encode the relationship of a pixel and its neighbors.
58 99 21
54 54 86
67 12 13
1 1 0
1 1
1 0 0
otherwise ,0
0 if ,1
28
10
xxs
ZZsLBP p
pp
Z1 Z2 Z3
Z8 Z0 Z4
Z7 Z6 Z5
21
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Local Binary Patterns [PAMI2006]
• An operator to encode the relationship of a pixel and its neighbors.
58 99 21
54 54 86
67 12 13
1 1 0
1 1
1 0 0
otherwise ,0
0 if ,1
28
10
xxs
ZZsLBP p
pp
Z1 Z2 Z3
Z8 Z0 Z4
Z7 Z6 Z5
LBP = 1101001122
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System Overview
Prepared-Works
Build Descriptor
Images Descriptors
ClusteringCalculate
LBPBuild
Descriptor
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Facial Image Descriptor
• They use Spatially Enhanced Histogram in original Local Binary Pattern. [PAMI2006]
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Local Patches
• We sample a patch for every s pixels.• There are S patches for one image.
mm
s s
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Spatial Block [CVPR2007]
• We use three concentric circles to describe a patch.
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Descriptor
• Build a descriptor for one face.
27
KKKK
K
K
fff
fff
fff
21
22221
11211
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System Overview
Pre-Processing
Build Descriptor
Images Descriptors
ClusteringCalculate
LBPBuild
Descriptor
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System Overview
Pre-Processing
Build Descriptor
Images Descriptors
Complete-Linkage Clustering
Calculate LBP
Build Descriptor
Compute all the similarities
Clustering
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System Overview
Pre-Processing
Build Descriptor
Images Descriptors
Complete-Linkage Clustering
Calculate LBP
Build Descriptor
Compute all the similarities
Clustering
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Similarity
• They use the weighted Chi-Square distance in original Local Binary Pattern. [PAMI2006]
•
j ijij
ijij
iiw MS
MSwMS
2
2 ,
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Partial Matching [ICCV2009]
• Step1:– Compute the similarity of each patch from one
image with the nearby patches in another image.
,
Image 1: I(1) Image 2: I(2)
)2()1(||;|:|,
)1( ,min klijrslsjrsksilkij ffsimfd
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Partial Matching [ICCV2009]
• Step2:– Sort the similarities of all patches.
– dαS is the similarity of I(1) to I(2). K
jiijsS fdSortdddd1,
)1(21 ,...,,...,,
SdIId )( )2()1(
33
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Partial Matching [ICCV2009]
• Step3:– Calculate the similarity of I(2) to I(1)
)( )1()2( IId
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Partial Matching [ICCV2009]
• Step4:– Use the maximum of two similarity
)(),(max),( )1()2()2()1()2()1( IIdIIdIID
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System Overview
Pre-Processing
Build Descriptor
Images Descriptors
Complete-Linkage Clustering
Calculate LBP
Build Descriptor
Compute all the similarities
Clustering
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Hierarchical Clustering
• Build a tree-based hierarchical taxonomy (dendrogram) from a set of documents.
Material Selected from Introduction to Information Retrieval, Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, 2008.
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Hierarchical Clustering
• Clustering obtained by cutting the dendrogram at a desired level: each connected connected component forms a cluster.
Material Selected from Introduction to Information Retrieval, Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, 2008.
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Hierarchical Complete-Linkage Clustering
• Similarity of the “furthest” points.• Makes “tighter,” spherical clusters that are
typically preferable.
Material Selected from Introduction to Information Retrieval, Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, 2008.
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Performance Optimization
• 4 threads in Quad-Core system
Pre-Processing
Build Descriptor
Images Descriptors
Complete-Link Clustering
Calculate LBP
Build Descriptor
Compute all the similarities
Clustering
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Performance Optimization
• 4 threads in Quad-Core system– 3 times faster than single thread.
• 73 minutes to 24 minute for 309 images.
Pre-Processing
Build Descriptor
Images Descriptors
Complete-Link Clustering
Calculate LBP
Build Descriptor
Compute all the similarities
Clustering
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RESULT
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FERET Result
• fa: gallery, 994 images• fb: alternative facial expression, 992 images• dup1: the photos taken after later, 736 images• dup2: the photos taken at least one year after
the gallery, 228 images
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FERET Result
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Accuracy fb dup1 dup2
LBP [PAMI2006] 95.67% 59.92% 45.61%
Our Result 98.89% 71.33% 68.42%
Time※ Register fb dup1 dup2
LBP [PAMI2006] 46 seconds 102 seconds 67 seconds 21 seconds
Our Result 4 minutes 224 minutes 168 minutes 5 minutes
※ The time results are computed in multithreads version.
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Experiments
• Home Photo Dataset I– 309 images, 5 subjects
• Home Photo Dataset II– 838 images, 8 subjects
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Evaluation
• Cluster Number• Unknown Number• Pair-wise Precision• Pair-wise Rand Index• Executing time
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Unknown Number
• The number of clusters which contain only one component.
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Precision/Rand Index
fptp
tpprecision
fntp
tprecall
tnfpfntp
tntpindexrand
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Assigned
Same Cluster
Different Clusters
Ground Truth
Same Cluster tp fn
Different Clusters fp tn
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Dataset-I Result
#Clusters #Unknown Precision Rand Index
Time ※
Picasa Web 94 73 99.92% 0.829816 10 seconds
Picasa PC 99 75 100% 0.865002 3 minutes
LBP [PAMI2006] 100 31 90.39% 0.811856 4 seconds
LID_PM
[ICCV2009]
99 37 98.78% - 11 minutes
Our Result 100 39 99.46% 0.816290 24 minutes
LID+PM
[Chang2010]
100 43 99.24% - 25 minutes
LBP: Local Binary Pattern, PM: Partial Matching, SB: Spatial Block
※ The time results are computed in multithreads version.49
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Dataset-I Result
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LBP Our Result
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Dataset-I Result
• Wrong clustering result in LBP
• The clusters in our result
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Dataset-II Result
#Clusters #Unknown Precision Rand Index
Time ※
Picasa Web 195 47 99.49% 0.876229 10 seconds
Picasa PC 253 150 100% 0.888492 10 minutes
LBP [PAMI2006] 253 45 91.70% 0.70261 17 seconds
LID_PM [ICCV2009] 253 79 97.88% - 51 minutes
Our Result 253 64 99.59% 0.871187 163 minutes
LID+PM
[Chang2010]
253 89 99.82% - 136 minutes
LBP: Local Binary Pattern, PM: Partial Matching, SB: Spatial Block
※ The time results are computed in multithreads version.52
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Dataset-II Result
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LBP Our Result
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Dataset-II Result
• Wrong clustering result in LBP
• Clusters in our result
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Demo
• Face Recognition with web camera
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CONCLUSION
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Conclusion
• LBP is an efficient algorithm for face recognition.
• Partial Matching is good for the different facial expression or different illumination in facial images.
• Our system has better performance than LBP.
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Future Works
• Improve performance by GPU.
• Use other extension of LBP combined with Partial Matching.
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Thank You for Your Attention!
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Result
LBP: Local Binary Pattern, PM: Partial Matching, SB: Spatial Block
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20.700000000000001
0.750000000000001
0.800000000000001
0.850000000000001
0.900000000000001
0.950000000000001
1
Home Photo Dataset I
LBP LBP+SB LBP+PM LBP+LID+SB
Recall
Pre
cisi
on
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20.700000000000001
0.750000000000001
0.800000000000001
0.850000000000001
0.900000000000001
0.950000000000001
1
Home Photo Dataset II
LBP LBP+SB LBP+PM LBP+SB+PM
Recall
Pre
cisi
on
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