Viola Jones

7/27/2019 Viola Jones

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Robust Real-time Face

Detectionby

Paul Viola and Michael Jones, 2002

Presentation by Kostantina Palla & Alfredo Kalaitzis

School of InformaticsUniversity of Edinburgh

February 20, 2009


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Overview

Robust very high Detection Rate (True-Positive

Rate) & very low False-Positive Rate always.

Real Time For practical applications at least 2

frames per second must be processed.

Face Detection not recognition. The goal is to

distinguish faces from non-faces (face detection is the

first step in the identification process)


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Three goals & a conlcusion

1. Feature Computation: what features? And howcan they be computed as quickly as possible

2. Feature Selection: select the most discriminatingfeatures

3. Real-timeliness: must focus on potentiallypositive areas (that contain faces)

4. Conclusion: presentation of results anddiscussion of detection issues.

How did Viola & Jones deal with these challenges?


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1. Feature Computation

The Integral image representation

2. Feature SelectionThe AdaBoost training algorithm

3. Real-timeliness

A cascade of classifiers

Three solutions


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Features Can a simple feature (i.e. a value) indicate

the existence of a face?

All faces share some similar properties

The eyes region is darker than theupper-cheeks.

The nose bridge region is brighter thanthe eyes.

That is useful domain knowledge

Need for encoding of Domain Knowledge:

Locat ion - Size:eyes & nose bridgeregion

Value:darker / brighter

Overview| Integral Image | AdaBoost | Cascade


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Rectangle features Rectangle features:

Value = (pixels in black area) - (pixels in white area)

Three types: two-, three-, four-rectangles,Viola&Jones used two-rectangle features

For example: the difference in brightnessbetween the white &black rectangles overa specific area

Each feature is related to a speciallocation in the sub-window

Each feature may have any size

Why not pixels instead of features? Features encode domain knowledge

Feature based systems operate faster

Overview | Integral Image | AdaBoost | Cascade


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Integral Image Representation(also check back-up slide #1)

Given a detection resolution of 24x24(smallest sub-window), the set ofdifferent rectangle features is

~160,000 !

Need for speed

Introducing Integral ImageRepresentation

Definition: The integral image at

location (x,y), is the sum of thepixels above and to the left of(x,y), inclusive

The Integral image can be computedin a single pass and only once foreach sub-window!

' , '

formal definition:

, ', '

Recursive definition:

, , 1 ,

, 1, ,

x x y y

ii x y i x y

s x y s x y i x y

ii x y ii x y s x y


y

x


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back-up slide #1


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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Three goals1. Feature Computation: features must be

computed as quickly as possible

2. Feature Selection: select the mostdiscriminating features

3. Real-timeliness: must focus on potentially

positive image areas (that contain faces)




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Feature selection Problem: Too many features

In a sub-window (24x24) there are~160,000 features (all possiblecombinations of orientation, locationand scale of these feature types)

impractical to compute all of them(computationally expensive)

We have to select a subset of relevantfeatures which are informative - tomodel a face Hypothesis: A very small subset of

features can be combined to form aneffective classifier

How? AdaBoost algorithm

Overview | Integral Image| AdaBoost | Cascade

Relevant feature Irrelevant feature


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AdaBoost

Stands for Adaptive boost

Constructs a strong classifier as a

linear combination of weighted simpleweak classifiers


Strong

classifier

Weak classifier

WeightImage


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AdaBoost - Characteristics Features as weak classifiers

Each single rectangle feature may be regardedas a simple weak classifier

An iterative algorithmAdaBoost performs a series of trials, each time

selecting a new weak classifier

Weights are being applied over the set ofthe example images

During each iteration, each example/imagereceives a weight determining its importance



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AdaBoost - Getting the idea

Given: example images labeled +/-

Initially, all weights set equally

Repeat T times

Step 1: choose the most efficient weak classifier that will be acomponent of the final strong classifier (Problem! Remember the hugenumber of features)

Step 2: Update the weights to emphasize the examples which wereincorrectly classified

This makes the next weak classifier to focus on harder examples

Final (strong) classifier is a weighted combination of the T weak classifiers

Weighted according to their accuracy


otherwise

xxh

T

t

T

t ttth

02

1)(1

)( 1 1

(pseudo-code at back-up slide #2)


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AdaBoostFeature SelectionProblem

On each round, large set of possible weak classifiers (each simpleclassifier consists of a single feature) Which one to choose?

choose the most efficient (the one that best separates theexamples the lowest error)

choice of a classifier corresponds to choice of a feature

At the end, the strong classifier consists of T features

Conclusion

AdaBoost searches for a small number of good classifiers features(feature selection)

adaptively constructs a final strong classifier taking into account thefailures of each one of the chosen weak classifiers (weight appliance)

AdaBoost is used to both select a small set of features and train astrong classifier



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Now we have a good face detector We can build a 200-feature

classifier!

Experiments showed that a 200-feature classifier achieves:

95% detection rate 0.14x10-3 FP rate (1 in 14084)

Scans all sub-windows of a384x288 pixel image in 0.7seconds (on Intel PIII 700MHz)

The more the better (?) Gain in classifier performance

Lose in CPU time

Verdict: good & fast, but notenough Competitors achieve close to 1 in

a1.000.000 FP rate!

0.7 sec / frame IS NOT real-time.



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Three goals1. Feature Computation: features must be

computed as quickly as possible

2. Feature Selection: select the mostdiscriminating features

3. Real-timeliness: must focus on potentially

positive image areas(that contain faces)




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The attentional cascade

On average only 0.01% of all sub-windows are positive (are faces)

Status Quo: equal computation time isspent on all sub-windows

Must spend most time only onpotentially positive sub-windows.

A simple 2-feature classifier canachieve almost 100% detection ratewith 50% FP rate.

That classifier can act as a 1st layer ofa series to filter out most negativewindows

2nd

layer with 10 features can tackleharder negative-windows whichsurvived the 1stlayer, and so on

A cascade of gradually more complexclassifiers achieves even betterdetection rates.


On average, much fewer

features are computed per

sub-window (i.e. speed x 10)


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Training a cascade of classifiers


Strong classifier definition:

otherwise

xxh

T

tt

T

ttth

02

1)(1

)(11 ,

where )1

log(t

t , 1 tt

t

Keep in mind: Competitors achieved 95% TP rate,10-6 FP rate

These are the goals. Final cascade must do better!

Given the goals, to design a cascade we must choose:

Number of layers in cascade (strong classifiers)

Number of features of each strong classifier (the T in definition)

Threshold of each strong classifier (the in definition)

Optimization problem: Can we find optimum combination?

T

t t12

1

TREMENDOUSLY

DIFFICULT

PROBLEM


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A simple framework for cascade training


Do not despair. Viola & Jones suggested a heuristic algorithm forthe cascade training: (pseudo-code at backup slide # 3) does not guarantee optimality

but produces a effective cascade that meets previous goals

Manual Tweaking: overall training outcome is highly depended on users choices

select fi (Maximum Acceptable False Positive rate / layer)

select di (Minimum Acceptable True Positive rate / layer)

select Ftarget (Target Overall FP rate)

possible repeat trial & error process for a given training set

Until Ftarget is met: Add new layer:

Until fi , di rates are met for this layer Increase feature number & train new strong classifier with AdaBoost

Determine rates of layer on validation set


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backup slide #3User selects values forf, the maximum acceptable false positive rate per layer and d,

the minimum acceptable detection rate per layer.

User selects target overall false positive rateFtarget.P= set of positive examplesN= set of negative examplesF

0= 1.0;D

0= 1.0; i = 0

WhileFi >Ftargeti++

ni = 0;Fi=Fi-1

whileFi >fxFi-1oni++

oUsePandNto train a classifier with nifeatures using AdaBoost

oEvaluate current cascaded classifier on validation set to determineFiandDioDecrease threshold for the ith classifier until the current cascaded classifier has

a detection rate of at least dxDi-1 (this also affectsFi)

N= IfFi >Ftargetthen evaluate the current cascaded detector on the set of non-face

images and put any false detections into the setN.



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Training

Set(sub-windows)

Integral

Representation

Feature

computation

AdaBoostFeature Selection

Cascade trainerTesting phaseTraining phase

Strong Classifier 1

(cascade stage 1)

Strong Classifier N

(cascade stage N)

Classifier cascade

framework

Strong Classifier 2

(cascade stage 2)

FACE IDENTIFIED



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pros Extremely fast feature computation

Efficient feature selection

Scale and location invariant detector Instead of scaling the image itself (e.g. pyramid-filters), we scale the

features.

Such a generic detection scheme can be trained for detection ofother types of objects (e.g. cars, hands)

and cons Detector is most effective only on frontal images of faces

can hardly cope with 45o face rotation Sensitive to lighting conditions

We might get multiple detections of the same face, due tooverlapping sub-windows.


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Results(detailed results at back-up slide #4)


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Results (Cont.)


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Viola & Jones prepared their final Detector cascade: 38 layers, 6060 total features included

1st classifier- layer, 2-features 50% FP rate, 99.9% TP rate

2nd classifier- layer, 10-features 20% FP rate, 99.9% TP rate

next 2 layers 25-features each, next 3 layers 50-features each

and so on

Tested on the MIT+MCU test set

a 384x288 pixel image on an PC (dated 2001) took about 0.067seconds

Detector 10 31 50 65 78 95 167 422

Viola-Jones 76.1% 88.4% 91.4% 92.0% 92.1% 92.9% 93.9% 94.1%

Rowley-Baluja-Kanade 83.2% 86.0% - - 89.2% 89.2% 90.1% 89.9%

Schneiderman-Kanade - - - 94.4% - - - -

Roth-Yang-Ajuha - - - - - - - -

False detections

Detection rates for various numbers of false positives on the MIT+MCU test set containing 130images and 507 faces (Viola & Jones 2002)

backup slide #4


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Thank you for listening!

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Viola Jones

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