Adaptive Rigid Multi-region Selection for 3D face recognition K. Chang, K. Bowyer, P. Flynn - PowerPoint PPT Presentation

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Adaptive Rigid Multi-region Selection for 3D face recognition K. Chang, K. Bowyer, P. Flynn

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Title: Adaptive Rigid Multi-region Selection for 3D face recognition K. Chang, K. Bowyer, P. Flynn


1
Adaptive Rigid Multi-region Selectionfor 3D face
recognition K. Chang, K. Bowyer, P. Flynn
  • Paper presentation
  • Kin-chung (Ryan) Wong
  • 2006/7/27

2
The ARMS algorithm
  • ARMS stands for Adaptive Rigid Multi-region
    Selection
  • The result of first-hand knowledge
  • Face Recognition Grand Challenge, versions 1 and
    2
  • Kevin Bowyer, Kyong Chang, Patrick Flynn the
    same authors of the Notre Dame survey on 3D and
    3D2D face recognition (2004-2006).

3
Main objectives
  • Use 3D shape information alone
  • Based on state-of-the-art methods
  • In their survey, Iterative Closest Point (ICP),
    along with Linear Discriminant Analysis (LDA) are
    reported as the best performing algorithms for 3D
    face recognition
  • Curvatures are used to locate landmark points
  • Able to handle expressions
  • Should perform well on FRGC v2

4
Issues in 3D face recognition
  • Expressions small
  • Even when told to maintain neutral expressions,
    there will be small movements in 3D face
    surface.
  • Expressions large
  • Some parts of face are more rigid than others.
  • Comparing non-rigid 3D facial surfaces across
    expressions is still an unsolved problem.
  • Solution
  • use rigid parts only
  • use robust surface registration method

5
Curvature alone is not enough for recognition
6
Preprocessing
  • Face surface is down-sampled to reduce
    computations with little effect on accuracy
  • Use skin color detection on 2D image to detect
    face
  • Use curvature to segment face surface and detect
    landmark points
  • Use landmark points to normalize pose and
    initialize ICP
  • Many techniques for preprocessing exist, but
    these are the more robust ones

7
Landmark detection with curvatures
8
Multiple regions and Fusion
  • Use multiple regions to compute similarity, and
    combine them later
  • Use the nose region
  • Relatively more rigid than the rest of the face
  • Relatively low probability of occlusions
  • Perform multiple ICP matches using multiple
    regions
  • Match smaller probe surfaces to a larger gallery
    surface (a practical ICP technique)
  • Use sum of squared distance (RMS) as
    dissimilarity measure

9
Registration
  • Iterative Closest Point (ICP) is used to register
    a probe surface to a gallery surface.
  • Rotates and translates the probe surface to match
    it with the gallery surface.
  • Does not deform either surfaces.
  • Provides good surface registration when facial
    expressions are present.
  • Computationally intensive, requires pair-wise
    matching
  • Requires good initialization, otherwise it will
    converge to wrong result

10
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11
Rules for Fusion
  • Three fusion rules
  • Sum
  • Product
  • Minimum

12
ICP, RMS similarity, and Fusion
13
Experiment algorithms
  • ICP baseline
  • PCA baseline
  • Landmark points are manually selected
  • The whole face is used for matching
  • ARMS auto
  • Landmark points are detected by their algorithm
    automatically. Used for ROI selection and ICP
    initialization.
  • ARMS manual
  • Landmark points are selected manually.

14
Experiment the dataset
  • The dataset later becomes part of FRGC v2.0
  • Experimentation protocols are different
  • The dataset makes it possible to evaluate
  • Neutral expressions vs. non-neutral expressions
  • Time-lapse between gallery and probe

15
Results Expressions
16
Results Fusion
2 regions better than 1, but 3 doesnt help
17
Areas for improvement
  • Use more regions from other parts of face
  • Examples chin region
  • Implicit expression modeling through
    intra-personal vs. inter-personal spaces
  • Fusion beyond sum, product and minimum
  • Automatic learning (PCA, LDA, SVM)
  • Committee machine

18
Areas for improvement
  • Faster ICP algorithm and implementation
  • Spatial search technique
  • Specialized data structure

19
Interesting side note invariance
  • The algorithms for computing mean and Gaussian
    curvatures are documented in great detail
  • Their algorithm is Euclidean invariant and
    involves elements similar to Lins summation
    invariant
  • Local coordinate transformation
  • Least square fitting curvature estimation ltgt
    second order monomial potentials
  • Preliminary correspondence is being worked out

20
Thank you.
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