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
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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
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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!