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Viola-Jones Type Face Detection
TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAAAAAAAAA
lecturer: Jiří Matas, [email protected]
authors: Jiří Matas, Ondřej Drbohlav
Czech Technical University, Faculty of Electrical Engineering
Department of Cybernetics, Center for Machine Perception
16/May/2016 Last update: 15/May/2016
Last update
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The task in not simple
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What is/is not a face?
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Face Detection Task Formulation
Typically,
● The user supplies 𝐿𝑜 rectangular sub-windows with faces and 𝐿𝑛
rectangular sub-windows with non-faces
● The task of the detection system is to output a tight rectangular
bounding box (BB) around the detected face
● The BB is considered a good detection when:
{ Eyes, mouth are inside the BB (= face inside BB)
{ Between-eye distance is bigger than 0.5 x larger side of BB
(= BB is tight)
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Errors
1. false negative
2. false positives
3. localization
Problems:
● where is the border between 1. + 2. and 3. ?
? ?
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Viola and Jones Suggested a Brute-Force Search
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Viola – Jones (2001)
Breakthrough #1
• Speed depends on negative
examples only
• This is addressed by
sequential decision making
1 Weak Classifier
5 Weak Classifiers
1200 Weak Classifiers
…
40%
40%
60%
60%
Class 1 (Face)
Class 2 (Non-Face)
Stage 1
Stage 2
Stage N
Input Signal (Image Window)
60%
36%
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Breakthrough #2 – bootstrap (hard negative mining)
• Classifiers further down the cascade are trained on
samples which had not been already discarded by
previous classifiers (by being classified as a non-
face). Their training set is thus harder but orders
of magnitude smaller than training set faced by
preceding classifiers. The classifiers thus can be
designed to be increasingly more complex.
T: classified as a face
(true positives and false
positives), goes further
down the cascade
Viola – Jones (2001)
vs false neg determined by
% False Pos
% D
etec
tion
0 50
0 100
face Image
sub-window
T
F
Receiver operating
characteristic (ROC)
Classifier 1
T
F
non-face
Classifier 2
T
F
non-face
Classifier N …
F: classified as a
non-face (true and false
negatives), dropped
from the pipeline
non-face 10
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Face Detector, Hard Negative Examples
Images classified as faces by early cascade components
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Viola – Jones (2001)
Breakthrough #3 : Fast features
• Gabor filters had been commonly
used as features of choice; they are
nice but expensive to compute.
• Viola - Jones have approximated
Gabors by piecewise constant
functions - Haar wavelets.
Example:
à ( t ) =
8<
:
1 fo r 0 · t < 0 :5
¡ 1 fo r 0 :5 · t < 1
0 o t h e rw is e
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Fast Calculation of Haar Wavelets
row sum : s(x, y) = s(x–1, y) + i(x, y)
integral image : ss(x, y) = ss(x, y−1) + s(x, y)
MATLAB: ss = cumsum(cumsum(double(i)), 2);
ss(x, y-1)
s(x-1, y)
i(x, y)
0 1 1 1
1 2 2 3
1 2 1 1
1 3 1 0
Image
0 1 2 3
1 4 7 11
2 7 11 16
3 11 16 21
Integral image
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Fast Calculation of Haar Wavelets
values at A,B,C,D are read out form the integral image
Sum of the intensities within the rectangle is equal to:
sum = A – B – C + D
Each rectangle requires 3 addtion/subtraction operations!
D B
C A
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Histogram of Oriented Gradients (HOG)
as a Weak Feature
Image courtesy of Dalal & Triggs
● Dalal, Triggs: Histogram of Oriented Gradients for Human Detection, CVPR 2005 15
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Viola – Jones (2001)
Breakthrough #4
• VJ have employed AdaBoost (Schapire a Freund, 1997)
which both trains the classifier and selects the features
• Pros of Adaboost:
• Well understood
• Good detection rate (in many applications)
• Easy to implement (“just 10 lines of code” [R. Schapire])
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AdaBoost: Algorithm
Weights of incorrectly classified training
examples are increased such that the next
classifier puts more focus on these.
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AdaBoost – Example 1
Taken from “A Tutorial on Boosting” by Yoav Freund and Rob Schapire
Data 1 2 3 4 5 6 7 8 9 10
Class + + + + + - - - - -
𝐷1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
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Example 1 – Iteration 1
Data 1 2 3 4 5 6 7 8 9 10
Class + + + + + - - - - -
𝐷2 ⋅ 𝑍2 0.07 0.07 0.15 0.15 0.15 0.07 0.07 0.07 0.07 0.07
𝛼1 = 1 2 log 1 − 𝜖1 𝜖1
𝜖1 … error 𝐷2 ≈ 𝐷1 𝜖1 (1 − 𝜖1 ) for corr. class.
𝑍2 = 0.92
𝐷2 ≈ 𝐷1 (1 − 𝜖1)/𝜖1 for wrongly class.
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Example 1 – Iteration 2
Data 1 2 3 4 5 6 7 8 9 10
Class + + + + + - - - - -
𝐷3 ⋅ 𝑍3 0.04 0.04 0.09 0.09 0.09 0.04 0.14 0.14 0.14 0.04
20
𝑍3 = 0.82
𝜖2 … error 𝐷3 ≈ 𝐷2 𝜖2 (1 − 𝜖2 ) for corr. class.
𝐷3 ≈ 𝐷2 (1 − 𝜖2)/𝜖2 for wrongly class.
𝛼2 = 1 2 log 1 − 𝜖2 𝜖2
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Example 1 – Iteration 3
Data 1 2 3 4 5 6 7 8 9 10
Class + + + + + - - - - -
𝐷4 ⋅ 𝑍4 0.11 0.11 0.04 0.04 0.04 0.11 0.07 0.07 0.07 0.02
21
𝑍4 = 0.68
𝜖3 … error 𝐷4 ≈ 𝐷3 𝜖3 (1 − 𝜖3 ) for corr. class.
𝐷4 ≈ 𝐷3 (1 − 𝜖3)/𝜖3 for wrongly class.
𝛼3 = 1 2 log 1 − 𝜖3 𝜖3
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Example 1 – Final Classifier after Iter. 3
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AdaBoost – Example 2
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Example 2, Weak Classifier
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Example 2, Weak Classifier Set
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Example 2, Weak Classifier Set
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Example 2, Iteration 1
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Example 2, Iteration 2
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Example 2, Iteration 3
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Example 2, Iteration 8
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Example 2, Iteration 9
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Example 2, Iteration 15
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Example 2, Iteration 21
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Example 2, Iteration 100
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Example 2, N=3 Proj. Directions, Iteration 100
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Example 3 - Adaboost Detector
The first two selected classifiers:
The two features have 100% detection rate and 50%
false alarm rate
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Sampling of Test Window Space
Not every image sub-window must be tested by the classifier.
It is sufficient to use:
● shifts by cca 10% window side
● window side size increments of 15%
● window rotation by +/- 15 deg
37/25
Note: Total number of sub-windows (thus speed) is determined by the size of smallest face to be detected. Total detection time is the geometric series sum with q=1/1.152 ; s ¼ 4t0
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Historical perspective
● VJ published in 2001.
● Many improvements since then.
● In 2009, implemented in many digital cameras.
● E. g. Waldboost (developed here on CTU) improves the
method by addressing the problem of trade-off between speed
and accuracy of the Adaboost classifier.
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Other improvements
● Dollar et al: Fast Pyramids for Object Detection. PAMI 2014.
Contributions (among others): Speed up by using less pyramid
levels, interpolation of features from octave-spaced pyramid
scales
● Benenson et al: Pedestrian detection at 100 frames per second. CVPR 2012. Contributions (among others):
Computation of HOG without need of explicitly resizing the
image => speedup
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Object Proposals Presented by Yao Lu
10-03-2014
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Intro to Object Proposals
Motivation
Sliding window based object detection
For EACH CLASS
Enumerate over window size, aspect ratio, and location
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Intro to Object Proposals
Goal
• Fast execution
• High recall with low # of candidate boxes
• Unsupervised/weakly supervised
Difference with image saliency
ImageFeature
ExtractionClassificaiton
Object Proposal
Slide Credit: Yao Lu
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Selective Search
Selective search
K. Van de Sande et al. Segmentation as selective search for object recognition. ICCV 2011.
Merge of multiple segmentation to propose candidate box
1536 boxes = 96.7 recall
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BING
M. Cheng et al. “BING: Binarized normed gradients for
objectnetss estimation at 300fps”, CVPR 2014.
• Resize images to different size & aspect ratio
• Train an 8x8 template using a linear SVM
• Use linear combination to integrate predictions.
• Binarize the template to speed-up
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BING
Pascal VOC 07
1000 => 0.95 recall
Speed
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Geodesic Object Proposal
P. Krahenbuhl and V. Koltun. Geodesic Object
Proposals. ECCV 2014.
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Edge Boxes
C. Lawrence Zitnick and P. Dollar, “Edge Boxes: Locating
Object Proposals from Edges”, ECCV 2014.
# of contours wholly within in a box
indicates the objectness
Method
• Edge detection. (m, 𝜃)
• Group edges using connectivity and
orientation. Affinity between edge groups:
• Rank wb on sliding window
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Edge Boxes
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Edge Boxes
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Conclusion
Object proposal greatly enhances object detection efficiency.
Current methods have very simple intuitions
• Selective search
• BING
• Geodesic object proposal
• Edge boxes
Future goal of object proposal:
• Less # of boxes
• Higher recall
• Faster speed
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The future: integrated CNN detecion