1. Facial Recognition
By Lisa Tomko

2. Overview What is facial recognition? History
The Face Recognition Technology Evaluation
The Face Recognition Vender Test
Facial Recognition Grand Challenge
Principle Components Analysis
Linear Description Analysis
Elastic Bunch Graph Matching
Applications
Research

3. What is facial recognition?
Given a still or video image of a scene, Identify or verify one or more persons in the scene using a stored database of faces

4. A Brief History 1960’s First semi-automated system
Programs designed by Woody Bledsoe, Helen Chan Wolf and Charles Bisson.
1988
Linear algebra technique implemented
Less than 100 values needed to align and normalize a face.
1991
Turk and Pentland
Real time automated face recognition
Implemented in 2001 super bowl

5. FERET 1993-1997 The Face Recognition Technology Evaluation
Sponsored by Defense Advances Research Products Agency
Encouraged development of face recognition algorithms

6. FRVT 2000, 2002, and 2006 The Face Recognition Vender Test
Evaluate work of FERET
Assess commercial facial recognition
Educate public
In % verification and 1% false accept rates

7. FRGC Facial Recognition Grand Challenge
Evaluated the latest in face recognition algorithms
Used:
High Resolution photos
3D Face Scans
Iris images
10x more effective then 2002
100x more effective than 1995

8. PCA Pioneered by Kirby and Sirivich in 1988
Images must all be the same size and normalized
Uses Data compression to reduce the detentions of the data and removes information that is not useful
Decomposes facial structure into orthogonal components known as eigenfaces, stored in a 1D array
Pro: Only needs 1/1000 of data presented
Con: Needs full frontal face

9. LDA Each face is represented by a large number of pixel values
Used to reduce number of features to a more manageable number before classification
Fishers Faces
Maximize between-class variance
Minimize within-class variance

10. EBGM Relies on concept of nonlinear features Lighting Pose Expression
Creates a dynamic link architecture that projects the face onto a grid
Garbor jet is a node which describes image behaviors around pixels
Garbor filter extracts shapes and detects features
Accurate land mark localization is needed

11. Applications Law enforcement
Facial recognition using various databases
Mobile applications
SocialCamera
SceneTap
FaceR Celebrity
Entertainment
TV set top box

12. Areas of Research
MIT-multidimensional morphable models, view-based human face detection, cortex-like mechanisms, and object detection by components.
MSU-data clustering, statistical pattern recognition, face detection in color images, the use of faces and fingerprints for personal identification, and kernel principal component analysis.
UCSD- use of shape contexts for object recognition, slow feature analysis, classifying facial actions, and face recognition using independent component analysis.

13. Questions