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Enhancing the Performance of Face Recognition Systems

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

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  1. 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

  2. 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

  3. 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)

  4. 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

  5. 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%

  6. Resolution Enhancement

  7. Illumination Correction Enrollment Failure (no preprocessing): 44% Training image A Testing image B Enrollment Failure (with preprocessing): 10% Preprocessed Image B

  8. 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

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