FACE RECOGNITIONAUTHOR: Łukasz Przywarty - 171018

Face recognition – 2/18

Table of contents

1. Introduction

2. Recognition process

• Face detection

• Feature extraction

• Face recognition

3. Application example

4. Summary

5. Literature

Face recognition – 3/18

Introduction

Why?Areas Applications

Information Security Access security (OS, data bases) Data privacy (e.g. medical records)

User authentication (trading, on line banking)

Access management Secure access authentication (restricted facilities) Permission based systems

Access log or audit trails

Biometrics Person identification (national IDs, Passports, voter registrations, driver licenses)

Automated identity verification (border controls)

Law Enforcement Video surveillanceSuspect identification

Suspect tracking (investigation)Simulated aging

Forensic Reconstruction of faces from remains

Personal security Home video surveillance systemsExpression interpretation (driver monitoring system)

Entertainment - Leisure Home video game systemsPhoto camera applications

Face recognition – 4/18

Introduction

Since when?• 1960’s – semi-automated system: required the administrator to

locate face coordinates; computer used this for recognition

• 1970’s – Goldstein, Harmon, Lesk: vector containing 21 features

e.g eyebrow weight, nose length as the basis to recognize faces

(pattern classification)

• 1986 – Kirby, Sirovich: methods based on PCA (Principal

Component Analysis); goal: represent image in lower dimension

without losing much information; dominant approach in following

years

Face recognition – 5/18

Introduction

Problems?• Pose variations

• Observation conditions (angle, light, shadows, reflections etc.)

• Ageing

• Facial expression

• Facial occulsion: make-up, hair style, accesories

Face recognition – 6/18

Recognition process

How to do it?

How to detect face?• Detection depending on scenario:

• Controlled environment – simple edge detection techniques

• Color images – skin colors can be used to find faces

• Images in motion – e.g blink detection

Input Face detection

Feature extraction

Face recognition

Identification or verification

Face recognition – 7/18

Recognition process

How to detect face?• Detection methods:

• Knowledge –based methods :

• they try to capture our knowledge of faces and translate

them into set of rules (face has two symmetric eyes, the

eye area is darker than the cheeks etc),

• facial features could be the distance between eyes or color

intensity difference.

• Feature-invariant methods:

• algorithms that try to find invariant features of a face

despite it’s angle or position

Face recognition – 8/18

Recognition process

How to detect face?• for example: algorithms that detect face-like textures or

the color of human skin.

• Template matching

• try to define face as a function and find a standard

template of all the faces,

• template colud be: face contour, relation between face

regions in terms of brightness and darkness,

• limited to faces that are frontal.

• Appearance-based methods

• statistical analysis.

Face recognition – 9/18

Recognition process

How to standarize image?• Histogram modification

• Image filtration

• Geometrical transformation

• Rotate

• Scale

• Move

• Resize

• Desaturation or color modification

Face recognition – 10/18

Division of face recognition systems

Feature-based approach• First, most intuitive idea

• First step: localization of points on face images:

• eyes centre points

• nose start-end points etc.

• Next step: measuring:

• face, nose width, height etc.

• distances between eyes centres, nose and eyes etc.

• Problems

• Accurate points localization

Face recognition – 11/18

Division of face recognition systems

Feature-based approach• Used methods:

• Geometric Matching

• Bunch Graph Matching

• Hidden Markov Model Techniques

Face recognition – 12/18

Division of face recognition systems

Holistic approach• Whole face analysis

• Methods based on:

• Correlation:

• simple method operating on input image pixels,

• direct comparision to a pattern in database,

• works if images were taken in almost the same conditions

• PCA (Principal Component Analysis ) and eigenfaces concept:

• feature dimension reduction (converts two dimensional

vectors into one dimensional vector)

• extracts the features of face which vary the most,

Face recognition – 13/18

Division of face recognition systems

Holistic approach• problem: image must be the same size and normalized;

pose and illumination variation in not acceptable,

• rate od recognition: 95%

• LDA (Linear Discriminate Analysis) and Fisherface concept

Face recognition – 14/18

Division of face recognition systems

Hybrid approach• Both local feature and whole face

• Methods based on:

• AAM (Active Appearance Model)

• integrated statistical model which combines a model of

shape variation and apperance with new image,

• built during a training phase,

• compares both whole face shape and pixels brightness

around feature.

Face recognition – 15/18

Application example

• Picasa 3.5

• Static images

• Luxand FaceSDK

• 66 feature points

• -30-30 degrees head rotation support

• 49 700 faces per second

• Verilook 5.1

• Multiface processing

• Live face detection

• Tolerance to face posture (near 360 degrees)

• 44 000 faces per second

• Multiple samples of same face

Face recognition – 16/18

Final word

Summary?• Despite of 40 years development still unreliable

• 12% of biometric technologies (2nd place, after print)

• Low effectiveness in pilot projects (UK: Newham, USA: Tampa)

• Failed trial in airports

Face recognition – 17/18

Literature

1. E. Bagherian, R. Wirza O.K. Rahmat. „Facial feature extraction for face

recognition:

a review”

2. C. Iancu, P. Corcoran, G. Costache . „A review of face recognition

techniques for in-camera applications”

3. M. Smiatacz, W. Malina. „Automatic face recognition – methods,

problems and applications”

4. K. Ślot. „Rozpoznawanie biometryczne”

5. K. Ślot. „Wybrane zagadnienia biometrii”

FACE RECOGNITIONThank you for your attention!