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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, [email protected] 14:00-15:00 Tuesday, 23 June 2009. Face detection. Keypoint registration. Face recognition.

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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,[email protected]

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 probe alone

  • Gallery image helps find keypoints in probe image

  • Localization errors are close to human labelling


Experiment 3 implicit registration
Experiment 3: implicit registration probe alone

  • Marginalizing over keypoint position is better than using MAP keypoint position


Experiment 4 joint and implicit registration
Experiment 4: joint and implicit probe aloneregistration

  • 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 probe alone(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 probe alone

  • Similar results to frontal face recognition & registration

  • Comparable to using manually labeled keypoints.


Concluding remarks
Concluding Remarks probe alone

  • 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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