Face Detection and Recognition Using Image Processing

8/3/2019 Face Detection and Recognition Using Image Processing

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Face detection andrecognition using image

processing

Group 11


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Group Members

Jam Zia-ul-Haq

Hafiz Nisar Ahmad

Muhammad Yasir


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contents

Introduction

Face detection

Face recognition

Implementation


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Introduction

Face interface

Face detection

Face recognition

Face detection Face recognitionMr.Chan

Prof..Cheng

Face database

Output:


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What is Face Detection?

Given an image, tellwhether there is

any human face, ifthere is, where isit(or where theyare).

Click to edit Master text stylesSecond level

Third level Fourth level

Fifth level



Fifth level


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Importance of Face Detection

The first step for any automatic face recognitionsystem system

First step in many Human Computer Interactionsystems

Expression Recognition

Cognitive State/Emotional State Recogntion

First step in many surveillance systems

Tracking: Face is a highly non rigid objectA step towards Automatic TargetRecognition(ATR) or generic objectdetection/recognition

Video coding


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Face Detection: current state

State-of-the-art:

Front-view face detection can be done at >15

frames per second on 320x240 black-and-whiteimages on a 700MHz PC with ~95% accuracy.

Detection of faces is faster than detection ofedges!

Side view face detection remains to be difficult.


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Face Detection: challenges

Out-of-Plane Rotation: frontal, 45 degree, profile,upside down

Presence of beard, mustache, glasses etc

Facial Expressions

Occlusions by long hair, hand

In-Plane Rotation

Image conditions:

Size

Lighting condition

Distortion

Noise

Compression


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Different Approaches

Knowledge-based methods:

Encode what constitutes a typical face, e.g., therelationship between facial features

Feature invariant approaches:

Aim to find structure features of a face that exist evenwhen pose, viewpoint or lighting conditions vary

Template matching:

Several standard patterns stored to describe the face asa whole or the facial features separately

Appearance-based methods:

The models are learned from a set of training imagesthat capture the representative variability of faces.


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Knowledge-Based Methods

Top Top-down approach: Represent aface using a set of human-coded rulesExample:

The center part of face has uniform intensityvalues

The difference between the average intensityvalues of the center part and the upper part issignificant

A face often appears with two eyes that aresymmetric to each other, a nose and a mouth

Use these rules to guide the searchprocess


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Knowledge-Based Method: [Yang andHuang 94]

Level 1 (lowest resolution):

apply the rule the center part of the face has4 cells with a basically uniform intensity to

search for candidatesLevel 2: local histogram equalizationfollowed by edge equalization followedby edge detection

Level 3: search for eye and mouthfeatures for validation


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Knowledge-based Methods: Summary

Pros:

Easy to come up with simple rules

Based on the coded rules, facial features in an inputimage are extracted first, and face candidates are

identified

Work well for face localization in uncluttered background

Cons:

Difficult to translate human knowledge into rulesprecisely: detailed rules fail to detect faces and generalrules may find many false positives

Difficult to extend this approach to detect faces indifferent poses: implausible to enumerate all thepossible cases


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Feature-Based Methods

Bottom-up approach: Detect facial features(eyes, nose, mouth, etc) first

Facial features: edge, intensity, shape, texture,

color, etcAim to detect invariant features

Group features into candidates and verify them


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Feature-Based Methods: Summary

Pros: Features are invariant to pose andorientation change

Cons:

Difficult to locate facial features due to severalcorruption (illumination, noise, occlusion)

Difficult to detect features in complexbackground


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Template Matching Methods

Store a template

Predefined: based on edges orregions

Deformable: based onfacial contours (e.g.,Snakes)

Templates are hand-coded(not learned)

Use correlation to locatefaces



Fifth level


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Template-Based Methods: Summary

Pros:

Simple

Cons:

Templates needs to be initialized near the faceimages

Difficult to enumerate templates for different

poses (similar to knowledge-based methods)


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Image Features

Rectangle filters

Rectangle_Feature_value f=

(pixels in white area) (pixels in shaded area)


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Example

Find theRectangle_Feature_value (f) of the box

enclosed by the dottedline

Rectangle_Feature_value f=

(pixels in white area)

(pixels in shaded area)f=(8+7)-(0+1)

=15-1= 14

1 2 3 3

3 0 1 3

5 8 7 1

0 2 3 6

E l A i l f


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Example: A simple facedetection method using one

feature

Result

This is a face:T he eye-part is dark,the nose-part is brightSo f is large, hence it is face

This is not a face.Because f is small

qRectangle_Feature_value fqf= (pixels in white area) (pixels in shadedarea)

qIf (f) is large it is face ,i.e.qif (f)>threshold, thenq faceqElseq non-face


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Why do we need to find pixel sum ofrectangles?

Answer: We want to get face featuresYou may consider thesefeatures as face features

Two eyes= (Area_A-Area_B)

Nose =(Area_C+Area_E-Area_D)

Mouth =(Area_F+Area_H-Area_G)

They can be differentsizes, polarity and aspectratios

AB

CDE

FGH


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Face feature and example

-1+2IntegralImage

A face

Shaded area

White areaF=Feat_val =pixel sum in shared area - pixel sum in whiteareaExamplePixel sum in white area=216+102+78+129+210+111=846

Pixel sum in shared area=10+20+4+7+45+7=93

Feat_val=F=846-93If F>threshold,feature=+1

Elsefeature=-1 End if;

We can choose threshold =768 , so feature is+1.

10 20 4

7 45 7

216 102 78

129 210 111

Pixel values insideThe areas


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4 basic types of featuresfor white_area-gray_area

Type) Rows xcolumns

Type 1) 1x2

Type 2) 2x1

Type 3) 1x3

Type 4) 3x1

Each basic type canhave differencesizes and aspectratios.


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Feature selection

For a 24x24detection region, thenumber of possiblerectangle features is~160,000!

Some examples and their typesFill in the types for the 2nd, 3rd rows

2 5 1 43

Type

1)

2)

3)

4)

5)


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The detection challenge

Use 24x24 base window

For y=1;y


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Solution to make it efficient

The whole 162,336 feature set is too large

Solution: select good features to make it moreefficient

Use: Boosting

Boosting

Combine many small weak classifiers tobecome a strong classifier.

Training is needed


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Boosting for face detection Define weak learners based on rectangle

features

window

value of rectangle feature

Pt=polarity{+1,-1}

threshold


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AdaBoost training

E.g. Collect 5000 faces, and 9400 non-faces.Different scales.

Use AdaBoost for training to build a strongclassifier.

Pick suitable features of different scales andpositions, pick the best few. (Take months todo , details is in [Viola 2004] paper)

Testing

Scan through the image, pick a window andrescale it to 24x24,

Pass it to the strong classifier for detection.

Report face, if the output is positive

Face detection using Adaboost


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Boosting for face detection [viola2004] In the paper it shows that the following two features

(obtained after training) in cascaded picked by AdaBoost

have 100% detection rate and 50% false positive rate

But 50% false positive rate is not good enough

Approach [viola2004] :Attentional cascade

type2type3

Pick a window in theimage and rescale it to24x24 as image

I.e.H(face)=Sign{1h1(image)+2h2(image)}

H(face)=+1 faceH(face)=-1 non-face

h1(image)h2(image)


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Boosting for face detection A 200-feature classifier can yield 95% detection rate and a false

positive rate of 1 in 14084 (Still not god enough)

Recall: False positive rate

The detector output is positive but it is false (there is actually noface). Definition of False positive: A result that is erroneouslypositive when a situation is normal. An example of a falsepositive: a particular test designed to detect cancer of the toenailis positive but the person does not have toenail cancer.(http://www.medterms.com/script/main/art.asp?articlekey=3377)

Still notgoodenough!

False positive rate

CorrectDetection

rate

X10-3


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To improve false positive rate:Attentional cascade

Cascade of many AdaBoost strongclassifiers

Begin with with simple classifiers to rejectmany negative sub-windows

Many non-faces are rejected at the first fewstages.

Hence the system is efficient enough for

real time processing.AdaboostClassifier1

AdaboostClassifier2

AdaboostClassifier3

True True TrueFacefound

Non-face Non-face

Input image

False False FalseNon-face


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An example

More features for later stages in the cascade

2 features 10 features 25 features 50 features

type3type2

AdaboostClassifier1

AdaboostClassifier2

AdaboostClassifier3


Non-face Non-face

Input image



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Attentional cascade Chain classifiers that are

progressively more complex andhave lower false positive rates:

vsfalsenegdetermined by

% False Pos

%

Detection

0 50

0

100

Receiver operating

characteristic

AdaboostClassifier1

AdaboostClassifier2

AdaboostClassifier3


Non-face Non-face

Input image


False positive rate


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Attentional cascade

Detection rate for each stage is 0.99 ,

for 10 stages, overall detection rate is 0.9910 0.9

False positive rate at each stage is 0.3,for 10 stages

false positive rate =0.310 610-6)

AdaboostClassifier1

AdaboostClassifier2

AdaboostClassifier3


Non-face Non-face

Input image



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Detection process in practice

Use 24x24 sub-window

Scaling

scale the detection (not the input image)

Features evaluated at scales by factors of 1.25at each level

Location : move detector around the image (1pixel increments)

Final detections

A real face may result in multiple nearbydetections (merge them to become the finalresult)


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Face Recognition


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Face Recognition byHumansPerformed routinely and effortlessly by humans

Enormous interest in automatic processing of digitalimages and videos due to wide availability of powerful

and low-cost desktop embedded computing

Applications:

biometric authentication,surveillance,

human-computer interaction

multimedia management


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Classification

A face recognition system is expected to identify faces present in images

and videos automatically. It can operate in either or both of two

modes:

Face verification (or authentication): involves a one-to-one matchthat compares a query face image against a template face image

whose identity is being claimed.

Face identification (or recognition): involves one-to-manymatches that compares a query face image against all the templateimages in the database to determine the identity of the query face.

First automatic face recognition system was developed by Kanade 1973.


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Face recognition processing

Face recognition is a visual pattern recognitionproblem.

A face is a three-dimensional object subject to varying

illumination, pose, expression is to be identified basedon its two-dimensional image ( or three- dimensionalimages obtained by laser scan).

A face recognition system generally consists of 4

modules - detection, alignment, feature extraction, andmatching.

Localization and normalization (face detection andalignment) are processing steps before face recognition(facial feature extraction and matching) is performed.


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Face detection segments the face areas from thebackground.

In the case of video, the detected faces may need tobe tracked using a face tracking component.

Face alignment is aimed at achieving more accurate

localization and at normalizing faces, whereas facedetection provides coarse estimates of the locationand scale of each face.


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Facial components and facial outline are located;based on the location points,

The input face image is normalized in respect togeometrical properties, such as size and pose, usinggeometrical transforms or morphing,

The face is further normalized with respect tophotometrical properties such as illumination andgray scale.


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After a face is normalized, feature extractionis performed to provide effective informationthat is useful for distinguishing between facesof different persons and stable with respect tothe geometrical and photometrical variations.

For face matching, the extracted featurevector of the input face is matched againstthose of enrolled faces in the database; itoutputs the identity of the face when a matchis found with sufficient confidence or indicatesan unknown face otherwise.


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Fifth level

Face recognition processing flow.

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Face Detection and Recognition Using Image Processing

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