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As applied to face recognition

Principal Components Analysis

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As applied to face recognition. Principal Components Analysis. video. Face Recognition. Detection vs. Recognition. Face Recognition. Identification vs. Verification. Face Recognition. Components: Face Detection Face Alignment Feature Extraction Matching. Face Recognition. Components: - PowerPoint PPT Presentation

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Page 1: Principal Components Analysis

As applied to face recognition

Page 2: Principal Components Analysis
Page 3: Principal Components Analysis

Detection vs. Recognition

Page 4: Principal Components Analysis

Identification vs. Verification

Page 5: Principal Components Analysis

Components: Face Detection Face Alignment Feature Extraction Matching

Page 6: Principal Components Analysis

Components: Face Detection Face Alignment Feature Extraction Matching

Page 7: Principal Components Analysis
Page 8: Principal Components Analysis

Dimensionality Reduction

Page 9: Principal Components Analysis

“Eigenface” analysis

Page 10: Principal Components Analysis

Unordered Observations

LightTemp.

2.5 2.4

0.5 0.7

2.2 2.9

1.9 2.2

3.1 3

2.3 2.7

2 1.6

1 1.1

1.5 1.6

1.1 0.9

Page 11: Principal Components Analysis
Page 12: Principal Components Analysis
Page 13: Principal Components Analysis

Turns 4096 dimensions -> 40 or less dimensions

Page 14: Principal Components Analysis

1.81 1.91

2.5 2.4

0.5 0.7

2.2 2.9

1.9 2.2

3.1 3

2.3 2.7

2 1.6

1 1.1

1.5 1.6

1.1 0.9

Page 15: Principal Components Analysis

1.81 1.91

2.5 2.4

0.5 0.7

2.2 2.9

1.9 2.2

3.1 3

2.3 2.7

2 1.6

1 1.1

1.5 1.6

1.1 0.9

0.69 0.49

-1.31 -1.21

0.39 0.99

0.09 0.29

1.29 1.09

0.49 0.79

0.19 -0.31

-0.81 -0.81

-0.31 -0.31

-0.71 -1.01

Page 16: Principal Components Analysis
Page 17: Principal Components Analysis

0.69 0.49

-1.31 -1.21

0.39 0.99

0.09 0.29

1.29 1.09

0.49 0.79

0.19 -0.31

-0.81 -0.81

-0.31 -0.31

-0.71 -1.01

.69 -1.31

.39 .09 1.29

.49 .19 -.81 -.31 -.71

.49 -1.21

.99 .29 1.09

.79 -.31 -.81 -.31 -1.01

Page 18: Principal Components Analysis

.69 -1.31

.39 .09 1.29

.49 .19 -.81 -.31 -.71

.49 -1.21

.99 .29 1.09

.79 -.31 -.81 -.31 -1.01

0.61655556 0.61544444

0.61544444 0.71655556

Page 19: Principal Components Analysis

0.0490834 1.28402771

-.73517866 -0.6778734

0.6778734 -0.73517866

EigenvaluesEigenvector 1 Eigenvector 2

Page 20: Principal Components Analysis

“Characteristic”

Page 21: Principal Components Analysis

“Characteristic”Vector characterizing a feature of

the matrix

Page 22: Principal Components Analysis

“Characteristic”Vector characterizing a feature of

the matrixEigenvalue = strength

Page 23: Principal Components Analysis

-.73517866 -0.6778734

0.6778734 -0.73517866

Eigenvalues

Eigenvector 1 Eigenvector 2

0.0490834 1.28402771

Page 24: Principal Components Analysis
Page 25: Principal Components Analysis

-.73517866 -0.6778734

0.6778734 -0.73517866

-.73517866 0.6778734

-0.6778734 -0.73517866

.69 -1.31

.39 .09 1.29

.49 .19 -.81 -.31 -.71

.49 -1.21

.99 .29 1.09

.79 -.31 -.81 -.31 -1.01

Page 26: Principal Components Analysis

-.828

1.78

-.992

-.27

-1.67

-.912

.099

1.144

.438

1.22

2.5 2.4

0.5 0.7

2.2 2.9

1.9 2.2

3.1 3

2.3 2.7

2 1.6

1 1.1

1.5 1.6

1.1 0.9

Page 27: Principal Components Analysis
Page 28: Principal Components Analysis
Page 29: Principal Components Analysis

[0,0,0,127, 55, 234, 255, 123, 98… n] n = width * height

Page 30: Principal Components Analysis

Image1

Image2

Image3

Image4

0 0 0 127

55 234

255

123

98 65

23 15 67 125

76 209

132

64 92 22

76 234

200

98 11o 85 145

97 44 32

209

53 99 198

39 201

38 220

77 92

Page 31: Principal Components Analysis

Average

Page 32: Principal Components Analysis

0 0 0 127

55 234

255

123

98 65

23 15 67 125

76 209

132

64 92 22

76 234

200

98 11o

85 145

97 44 32

209

53 99 198

39 201

38 220

77 92

-77 -75.5 -91.5 -10 -1.67 51.75 112.5 -3 20.25 12.25

-54 -60.5 -24.5 -12 19.3 26.75 -10.5 -62 14.25 -30.75

-1 158.5 108.5 -39 53.3 -97.25 2.5 -29 -33.75 -20.75

132 -22.5 7.5 61 -17.67 18.75 -104.5 94 -0.75 39.25

77 75.5 91.5 137 56.67 182.3 142.5 126 77.75 52.75

Page 33: Principal Components Analysis
Page 34: Principal Components Analysis

Eigenvalues

Eigenvectors

.000064 50.97 84.828 173.8 213.018

-.24 -.05 -.17 .13 .33

-.24 -.001 -.034 .462 .317

-.24 -.367 -.1 .006 .134

-.24 -.222 .412 .082 -.308

-.24 .0008 .048 -.057 .192

Principal component

Page 35: Principal Components Analysis
Page 36: Principal Components Analysis
Page 37: Principal Components Analysis

Animation of reconstruction

Page 38: Principal Components Analysis

.5 .2 .1

.03 .005

Page 39: Principal Components Analysis

Demo