1. FACE RECOGNITION
AUTHOR: Łukasz Przywarty

2. Table of contents Introduction Recognition process Face detection
Feature extraction
Face recognition
Application example
Summary
Literature
Face recognition – 2/18

3. Introduction Why? Face recognition – 3/18 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 surveillance
Suspect identification
Suspect tracking (investigation)
Simulated aging
Forensic Reconstruction of faces from remains
Personal security
Home video surveillance systems
Expression interpretation (driver monitoring system)
Entertainment - Leisure
Home video game systems
Photo camera applications
Face recognition – 3/18

4. 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 – 4/18

5. Introduction Problems? Pose variations
Observation conditions (angle, light, shadows, reflections etc.)
Ageing
Facial expression
Facial occulsion: make-up, hair style, accesories
Face recognition – 5/18

6. Identification or verification
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 – 6/18

7. 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 – 7/18

8. 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 – 8/18

9. Recognition process How to standarize image? Histogram modification
Image filtration
Geometrical transformation
Rotate
Scale
Move
Resize
Desaturation or color modification
Face recognition – 9/18

10. 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 – 10/18

11. Division of face recognition systems
Feature-based approach
Used methods:
Geometric Matching
Bunch Graph Matching
Hidden Markov Model Techniques
Face recognition – 11/18

12. 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 – 12/18

13. 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 – 13/18

14. 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 – 14/18

15. Application example Picasa 3.5 Static images Luxand FaceSDK
66 feature points
degrees head rotation support
faces per second
Verilook 5.1
Multiface processing
Live face detection
Tolerance to face posture (near 360 degrees)
faces per second
Multiple samples of same face
Face recognition – 15/18

16. 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 – 16/18

17. Literature
E. Bagherian, R. Wirza O.K. Rahmat. „Facial feature extraction for face recognition: a review”
C. Iancu, P. Corcoran, G. Costache . „A review of face recognition techniques for in-camera applications”
M. Smiatacz, W. Malina. „Automatic face recognition – methods, problems and applications”
K. Ślot. „Rozpoznawanie biometryczne”
K. Ślot. „Wybrane zagadnienia biometrii”
Face recognition – 17/18

18. FACE RECOGNITION
Thank you for your attention!