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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.

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enhancing the performance of face recognition systems
Enhancing 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

slide2

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
problem

Preprocessing for Face Recognition

Problem:

Solution:

Current state-of-the-art face recognition systems degrade significantly in performance due to variations in illumination and blurring

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)

slide4

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
slide5

Resolution 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%

slide7

Illumination Correction

Enrollment Failure

(no preprocessing):

44%

Training image A

Testing image B

Enrollment Failure

(with preprocessing):

10%

Preprocessed Image B

future work
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