Robust Real-Time Face Detection

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Robust Real-Time Face Detection

International Journal of Computer Vision 57(2), 137–154, 2004

Paul Viola, Michael Jones

授課教授：林信志博士報告者：林宸宇

報告日期：96.12.18

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Outline

• Introduction

• The Boost algorithm for classifier learning

Computer Graphics & Interactive Techniques Lab.

2

learning

– Feature Selection

– Weak learner constructor – The strong classifier

• Result

• Conclusion

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Introduction

• A machine learning approach for visual object detection

– Capable of processing images extremely rapidly – Achieving high detection rates

– Achieving high detection rates

• Three key contributions

– A new image representation Integral Image – A learning algorithm( Based on AdaBoost)

– A combining classifiers method cascade

classifiers

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Feature

• Papageorgiou et al (1998)

4

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Integral Image

• D=4+1-(2+3)

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6

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AdaBoost

• A supervised training process

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8

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AdaBoost

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Attentional Cascade

• Rowley et al.(1998)

• Use two neural networks

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Attentional Cascade

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Attentional Cascade

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Result

• A 38 layer cascaded classifier was trained to detect frontal upright faces

– Training set:

• Face: 4916 hand labeled faces with resolution 24x24.

• Non-face: 9544 images contain no face.

(350 million subwindows within these non-face images)

– Features

• The first five layers of the detector: 1, 10, 25, 25 and 50 features

• Total # of features in all layer 6061

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Result

• Each classifier in the cascade was trained

– Face : 4916 + the vertical mirror image 9832 images

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– Non-face sub-windows: 10,000

(size=24x24)

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Result-outline

• Speed of the final Detector

• Image Processing

• Scanning the Detector

• Integration of Multiple Detector

• Experiments on a Real-World Test Set

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Speed of the final Detector

• The speed is directly related to the

number of features evaluated per scanned sub-window.

• MIT+CMU test set

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• MIT+CMU test set

– An average of 10 features out of a total 6061 are evaluated per sub-window.

• On a 700Mhz PentiumIII, a 384 x 288

pixel image in about .067 seconds

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Image Processing

• Minimize the effect of different lighting- conditions

• Using integral image

• α is standard deviation, m is mean, x is

piexl value

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Scanning the Detector

• The final detector is scanned across the image at multiple scale and locations

• Locations are obtained by shifting the

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• Locations are obtained by shifting the window some pixels △ △ △ △

– If the current scale is s, the window is shifted

by [s △ △ △] △

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Integration of Multiple Detector

• Multiple detections will usually occur

around each face and some types of false positives.

positives.

• A post-process to detected sub-windows in order to combine overlapping

detections into a single detection

– Two detections are in the same subset if their

bounding regions overlap

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Experiments on a Real-World Test Set

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Result

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Result

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Conclusion

• Authors had developed the fastest known face detector for gray scale images

• This paper brings together new algorithms, representations and insights which are quite representations and insights which are quite generic

• The database set includes faces under very wide range of conditions including: illumination,

scale, pose, and camera variation

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Conclusion

• The database set includes faces under very wide range of conditions including:

illumination, scale, pose, and camera

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illumination, scale, pose, and camera

variation

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Thanks !

報告結束 ~

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Introduction

• The attentional operator is trained to

detect examples of a particular class --- a supervised training process

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supervised training process

• Face classifier is constructed

– In the domain of face detection

< 1% false negative

<40% false postivie

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28

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Example

• x

₁

=[1 1] x

₂

=[2 2] x

₃

=[2 1] x

₄

=[3 2]

• y

₁

=1 y

₂

=1 y

₃

=0 y

₄

=0

• t=1~3 (round)

• Initial weight

t=1 (round)

W

_t,i

=[w

_1,1

=1/4, w

_1,2

=1/4, w

_1,3

=1/4, w

_1,4

=1/4]

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Normalize weight

• t=1 (round)

• w

_1,1

=(1/4) / (1/4+1/4+1/4+1/4) = 1/4,

• w

_1,2

=(1/4) / (1/4+1/4+1/4+1/4) = 1/4,

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• w

_1,2

=(1/4) / (1/4+1/4+1/4+1/4) = 1/4,

• w

_1,3

=(1/4) / (1/4+1/4+1/4+1/4) = 1/4,

• w

_1,4

=(1/4) / (1/4+1/4+1/4+1/4) = 1/4,

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• The error is evaluated with respect to ω

_t=1

• ε

₁

= 1/4|1-1|+1/4|0-1|+1/4|0-0|+ 1/4|0-0| = 1/4

• ε

₂₂

= 1/4|0-1|+1/4|1-1|+1/4|0-0|+ 1/4|1-0| = 1/2

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• Choose the lowest error ε

_j

t=1 (round) Choose h

₁

• Update weight

/

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β

₁

= (¼) / (1- (¼)) = 1/3

• W

_2,1

=1/4× β

₁^1-0

= 1/12

• W

_2,2

=1/4× β

₁^1-1

= 1/4

• W

_2,3

=1/4× β

₁^1-0

= 1/12

• W

_2,4

=1/4× β

₁^1-0

= 1/12

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Normalize weight (when t=2)

• W

_2,1

=1/12 / ^{1/2 = 1/6}

• W

_2,2

=1/4 / ^{1/2 = 1/2}

• W =1/12 / ^{1/2 = 1/6}

• W

_2,3

=1/12 / ^{1/2 = 1/6}

• W

_2,4

=1/12 / ^{1/2 = 1/6}

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• The error is evaluated with respect to ω

_t=2

• ε

₁

= 1/6|1-1|+1/2|0-1|+1/6|0-0|+ 1/6|0-0| = 1/2

• ε

₂

= 1/6|0-1|+1/2|1-1|+1/6|0-0|+ 1/6|1-0| = 1/3

34 2

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• Choose the lowest error ε

_j

t=2 (round) Choose h

₂

• Update weight

/

β

₂

= (1/3) / (1- (1/3)) = 1/2

• W

_3,1

=1/6× β

₂^1-1

= 1/6

• W

_3,2

=1/2× β

₂^1-0

= 1/4

• W

_3,3

=1/6× β

₂^1-0

= 1/12

• W

_3,4

=1/6× β

₂^1-1

= 1/6

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Normalize weight (when t=3)

• W

_3,1

=1/6 / ^{2/3 = 1/4}

• W

_3,2

=1/4 / ^{2/3 = 3/8}

• W =1/12 / ^{2/3 = 1/8}

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• W

_3,3

=1/12 / ^{2/3 = 1/8}

• W

_3,4

=1/6 / ^{2/3 = 1/4}

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• The error is evaluated with respect to ω

_t=3

• ε

₁

= 1/4|1-1|+3/8|0-1|+1/8|0-0|+ 1/4|0-0| = 3/8

• ε

₂

= 1/4|0-1|+3/8|1-1|+1/8|0-0|+ 1/4|1-0| = 1/2

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• Choose the lowest error ε

_j

t=3 (round) Choose h

₁

• Update weight

/

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β

₃

= (3/8) / (1- (3/8)) = 3/5

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The final strong classifier

• α

₁

=log3 α

₂

=log2 α

₃

=log(5/3)

• log3×h

₁

(x)+log2×h

₂

(x)+log(5/3) ×h

₁

(x) ≧1/2×1

• 0.4771 0.301 0.2218

• 1 0 1 class1 T

• 0 0 0 class0 T

• 0 1 0 class0 F

Test point (1,100) 1 1 1 => class1

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False positive rate

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