Title: Enhancing the Performance of Face Recognition Systems
1Enhancing the Performanceof 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
2Project 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
3Problem
Preprocessing for Face Recognition
- Current state-of-the-art face recognition
systems degrade significantly in performance due
to variations in illumination and blurring
Solution
PREPROCESSING RESTORATION/ ENHANCEMENT
FACE RECOGNITION SYSTEM
IMAGE CAPTURE
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)
4Preprocessing 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
5Resolution Enhancement
Problem recognition performance drops when
image resolution of training and testing images
vary.
Training image
Testing image Same resolution EER 8
Testing image Lower resolution EER 23
6Resolution Enhancement
7Illumination Correction
Enrollment Failure (no preprocessing) 44
Training image A
Testing image B
Enrollment Failure (with preprocessing) 10
Preprocessed Image B
8Future 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