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Joint and implicit registration for face recognition. Dr. Peng Li and Dr. Simon J.D. Prince Department of Computer Science University College London {p.li, s.prince}@cs.ucl.ac.uk. 14:00-15:00 Tuesday, 23 June 2009. Face detection. Keypoint registration. Face recognition.

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slide1
Joint and implicit registration for face recognition

Dr. Peng Li and Dr. Simon J.D. Prince

Department of Computer Science

University College London

{p.li,s.prince}@cs.ucl.ac.uk

14:00-15:00 Tuesday, 23 June 2009

the face recognition pipeline

Face detection

Keypoint registration

Face recognition

Feature extraction

The face recognition pipeline

Matching

Gallery

Probe

Detected face

Original Image

Result

  • Global approaches
    • Eigenfaces [Turk 1991]
    • Fisherfaces [Belhumeur 1997]
  • Local approaches
    • AAM [Cootes 2001]
    • ASM [Mahoor 2006]
    • EBGM [Wiskott 1997]
  • Distance-based approaches
    • Fisherfaces [Belhumeur1997]
    • Laplacianfaces [He2005]
    • KLDA [Yang2005]
  • Probabilistic approaches
    • Bayesian [Moghaddam 2000]
    • PLDA [Ioffe 2006, Prince 2007]
the face recognition pipeline1

Face detection

Keypoint registration

Face recognition

Feature extraction

The face recognition pipeline

Matching

……

Gallery

Probe

Detected face

Original Image

Result

  • Extract Gabor jet around each keypoint
  • Generative probabilistic model
  • Independent term for each keypoint
hypothesis 1

Probabilistic model

Face detection

Keypoint registration

Keypoint registration

Face recognition

Feature extraction

Feature extraction

Hypothesis 1

H1: We can use the same probabilistic model for registration and recognition.

Matching

……

Gallery

Probe

Detected face

Original Image

Result

hypothesis 2 joint registration
Hypothesis 2: Joint Registration

x

Generic eye

Particular eye

+

+

Probe

Gallery

+

+

H2: We can use the gallery image to help find keypoints in the probe image.

hypothesis 3 implicit registration
Hypothesis 3: Implicit Registration

Probe

tp– keypoint position

*

+

Hidden variable

Posteriordistribution

H3: We do not need to make hard estimates of keypoint positions.

outline
Outline
  • Background
  • Hypotheses
  • Probabilistic face recognition
  • Frontal face recognition

H1: Same model for registration and recognition

H2: Joint registration

H3: Implicit registration

  • Cross-pose face recognition
  • Conclusion
probabilistic linear discriminant analysis prince elder iccv 2007

w1j

h1

G(:,1)

F(:,1)

Image

xij

h2

w2j

mean

m

F(:,2)

G(:,2)

w3j

h3

F(:,3)

G(:,3)

Probabilistic linear discriminant analysis (Prince & Elder,ICCV 2007)

ij

μ

Fhi

Gwij

xij

+

+

+

=

Noise

Signal

i - # of identity

j - # of image

+

+

=

+

Independent per-pixel Gaussian noise, e

Between-individual variation

Within-individual variation

face recognition by model selection

hp

hg

xg

xp

wp

wg

hg

xg

xp

Face recognition by model selection

Observed Variables

Observed Variables

  • Xp - Probe image
  • Xg - Gallery image

Pr(xp, xg |Md)

Md

Hidden Variables

Hidden Variables

Hidden Variables

Hidden Variables

  • No-Match

Choose MAP model

Pr(xp, xg |Ms )

Ms

  • Match

wp

wg

methodology

hp

hg

xg

xp

wp

wg

hg

xg

xp

wp

wg

Methodology

4: Joint and Implicit registration

3: Implicit registration using probe image alone

2: Joint registration by MAP

1: Find keypoint in probe image alone by MAP

tp – keypoint position

tp

+

+

Posterior over keypoint position

Probe

Gallery

experimental setting xm2vts database
Experimental Setting: XM2VTS Database
  • Dataset
    • Training: First 195 identities
    • Test: Last 100 identities
      • Gallery data: 1st image of 1st session
      • Probe data: 1st image of 4th session
  • Feature Extraction: Gabor filter at all possible locations of 13 keypoints
experiment 1 finding keypoints using recognition model in probe alone
Experiment 1: finding keypoints using recognition model in probe alone
  • Recognition
  • First match identification rate
  • Higher is better
  • Registration
  • Average error of all keypoints
  • Lower is better
experiment 2 joint registration
Experiment 2: joint registration
  • Gallery image helps find keypoints in probe image
  • Localization errors are close to human labelling
experiment 3 implicit registration
Experiment 3: implicit registration
  • Marginalizing over keypoint position is better than using MAP keypoint position
experiment 4 joint and implicit registration
Experiment 4: joint and implicit registration
  • Joint and implicit registration performs best.
  • Comparable to using manually labeled keypoints.
cross pose face recognition using tied plda model prince elder 2007
Cross-pose face recognition using tied PLDA model (Prince & Elder, 2007)

ijk

μk

Gkwijk

xijk

Fkhi

+

+

+

=

Key idea: separate within-individual and between- individual variance at each pose

Data: XM2VTS database: with 90° pose difference.

Gallery (frontal face) ↔ Probe (profile face)

Feature extraction: Gabor feature for 6 keypoints

K – Pose Index

  • K = 1

FRONTAL IMAGE

  • K = 2

PROFILE IMAGE

experiment 5 cross pose face recognition and registration
Experiment 5: Cross-pose face recognition and registration
  • Similar results to frontal face recognition & registration
  • Comparable to using manually labeled keypoints.
concluding remarks
Concluding Remarks
  • Three hypotheses
    • Same model for both face registration & recognition.
    • Joint registration for face recognition
    • Implicit registration for face recognition
  • All work well for both frontal & cross-pose face registration & recognition
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