Enhancing the Performance
of Face Recognition Systems
Presenter: Dr. Christine Podilchuk
Professors: Richard Mammone, Joe Wilder
Students: Anand Doshi, Aparna Krishnamoorthy, Robert Utama
WISE Lab, CAIP Center
http://www.caip.rutgers.edu/wiselab
Project Description
• Funded by Dept of Defense, Technical Support Working
Group (TSWG)
• Scope of Work: Preprocessing technology to improve
existing state-of-the-art face recognition systems
- commercial system provided by Viisage (technology from
MIT, Media Lab)
- Rutgers technology
• Problems addressed: blur and illumination correction
Preprocessing for Face Recognition
Problem:
Current state-of-the-art face recognition systems degrade significantly in
performance due to variations in illumination and blurring
Solution:
IMAGE
CAPTURE
PREPROCESSING
RESTORATION/
ENHANCEMENT
FACE
RECOGNITION
SYSTEM
DEBLURRING (due to mismatch in camera resolution, image
scale, and motion blur)
ILLUMINATION CORRECTION (due to mismatch in lighting
conditions in both indoor and outdoor environments)
Preprocessing for Face Recognition
Solution:
Projection onto Convex Sets (POCS) framework
• A priori knowledge of the blur, illumination and/or face can
be incorporated into the POCS framework
• Deblurring and illumination correction processes are duals
of each other
- the deblurring process operates in the Fourier domain
- the illumination correction operates in the spatial domain
Resolution Enhancement
Problem: recognition performance drops when image
resolution of training and testing images vary.
Training image Testing image
Testing image
Same resolution Lower resolution
EER: 8%
EER: 23%
Resolution Enhancement
Illumination Correction
Enrollment Failure
(no preprocessing):
44%
Training image A
Testing image B
Enrollment Failure
(with preprocessing):
10%
Preprocessed Image B
Future Work
• Improve algorithms for deblurring and illumination
correction
• Test algorithms on additional databases (varying cameras,
resolutions, viewing angles, lighting conditions)
• Devise models of convex sets for faces, blur models and
illumination models
• Generate ROC curves for performance before and after
preprocessing
• Test our preprocessing algorithms on commercially
available systems
For current updates, visit http://caip.rutgers.edu/wiselab