My PhD proposal

1. 1
POSE & OCCLUSION ROBUST FACE
ALIGNMENT USING MULTIPLE SHAPE MODELS
AND PARTIAL INFERENCE
- PhD Thesis Proposal -
Jongju Shin
[jjshin@postech.ac.kr]
Advisor : Daijin Kim
2013.01.03
I.M. Lab.
Dept. of CSE

2. 2
Outline
• Introduction
• Previous Work
• Proposed Method
• Shape Representation
• Formulation
• Multiple Shape Models
• Local Feature Detection
• Hypothesizing Transformation Parameters
• Hypothesizing Shape Parameters
• Model Hypotheses Evaluation
• Experimental Results
• Conclusion
• Future Work

3. 3
INTRODUCTION

4. 4
introduction
What is face alignment?
• Face alignment is to extract facial feature points :
• , and from the given image
Eyebrow
Eye
Nose
Mouth
Chin
* “The POSTECH Face Database (PF07) and Performance Evaluation”, FG 2008

5. 5
introduction
Why is it important?
• Face alignment is pre-requisite for many face-related
problem.
Angry Happy
-25° 0° +25°
Surprise Neutral
Face Recognition Face Expression Recognition Head Pose Estimation

6. 6
introduction
Challenges
Illumination Pose
Expression Occlusion

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PREVIOUS WORK

8. 8
Previous work
Previous work
• Two approaches
• 1. Discriminative approach
• Active Shape Model
• The shape parameters are iteratively updated by locally finding the best
nearby match for each feature point.
• 2. Generative approach
• Active Appearance Model
• The shape parameters are iteratively updated by minimizing the error
between appearance instance and input image.

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Previous work
Previous work
• 1. Discriminative approach
Constrained Local Model[1] Bayesian Tangent Shape Model[2]
• Feature detector : Linear SVM • Feature detector : gradient along normal vector
• Alignment algorithm : Mean-shifts • Alignment algorithm : Bayesian Inference
• They assume that all the feature points are visible.
• By the wrong detected feature points, alignment fails.
[1] Jason et al., “Face Alignment through Subspace Constrained Mean-Shifts”, ICCV 2009
[2] Yi et al., “Bayesian Tangent Shape Model:Estimating Shape and Pose Parameters via Bayesian Inference”, CVPR 2003

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Previous work
Previous work
• 2. Generative approach
Boosted Appearance Model[3] Fourier Active Appearance Model[4]
• Appearance model : Haar-like feature • Appearance model : Fourier transformed
and boosting. appearance
• Weak classifier : discriminate aligned • Alignment algorithm : gradient descent
images from not-aligned images.
• Due to high dimensional solution space, it has large number of
local minimums.
• They need good initialization by eye detection.
[3] Xiaoming Liu, “Generic Face Alignment using Boosted Appearance Model”, CVPR 2007
[4] Rajitha, et al., “Fourier Active Appearance Models”, ICCV 2011

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PROPOSED METHOD

12. 12
Proposed method
Motivation
• We follow discriminative approach.
• Determine whether a feature point is visible or not.
• Only visible feature points are involved alignment step.
• Invisible feature points are estimated by visible feature points using partial
inference (PI) algorithm.
• Using the multiple shape models, we solve pose problem.
We propose pose and occlusion robust face alignment !
Visible
Invisible

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Proposed method
Shape Representation
• Point Distribution Model
• The non-rigid shape :
• is represented by linear combination of shape bases with the
mean shape as
: mean shape associated to
: eigenvectors associated to
: shape parameter
: scale
: rotation
: translation(x, y)

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Proposed method
Formulation
• Shape Model with parameter, p ={s, R, q, t}
• Energy function
denotes whether the is aligned(visible) or not,
is the number of local features.

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Proposed method
Multiple Shape Models
• To cover various pose and expression, we build multiple
shape models.
• We build eigenvectors for nth pose, mth expression,
• Given n and m, shape is

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Proposed method
Formulation with multiple shape models
• Energy function

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Proposed method
Algorithm Overview
Model Hypotheses
[Input] Evaluation
Local Feature Detection
[Output]
[Hypothesis-and-test]
Hypothesizing
Transformation Parameters
Face Hypothesizing
Detection Shape Parameters

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Proposed method
Local Feature Detection
Model Hypotheses
[Input] Evaluation
Local Feature Detection
[Output]
[Hypothesis-and-test]
Hypothesizing
Transformation Parameters
Face Hypothesizing
Detection Shape Parameters

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Proposed method
Local feature detection
Local Feature Detection
• Goal
Detect feature point candidates with Gaussian Model!
• Based on MCT+Adaboost algorithm [5],
• We propose Hierarchical MCT to increase detection
performance.
[5] Jun, and Kim, “Robust Real-Time Face Detection Using Face Certainty Map”, ICB, 2007

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Proposed method
Local feature detection
Feature Descriptor
• Modified Census Transform (MCT)
I1 I2 I3 B1 B2 B3
9
I4 I5 I6 B4 B5 B6 C   B x * 2x
x1
I7 I8 I9 B7 B8 B9
1 9
M   Ix
9 x 1
Bx  1 if Ix  M
B x  0 otherwise
102 105 118 0 0 0
120 111 101 1 0 0 011100000 2
 224
123 119 109 1 1 0

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Proposed method
Local feature detection
Feature Descriptor
• Modified Census Transform (MCT)
• Transformed result
Gray image MCT
• MCT is point feature
• Represents local intensity’s difference
• Very sensitive to noise

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Proposed method
Local feature detection
Feature Descriptor
We propose Hierarchical MCT
• Regional feature
• To represent regional difference
• Robust to noise
I1 I2 I3
9
I4 I5 I6 C   B x * 2x
x 1
Partition Average MCT
I7 I8 I9

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Proposed method
Local feature detection
Training procedure
• Hierarchical MCT + Adaboost
35
25
Adaboost
Training
15
35
5
Image pyramid Concatenated
Input image
By Integral Image
MCT
vector

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Proposed method
Local feature detection
Feature Response
• Feature response by Adaboost with different feature
descriptor
Training
Image
Test
Image
Conventional Conventional Hierarchical Hierarchical
LBP MCT LBP MCT

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Proposed method
Local feature detection
Process of local feature detection
[Input] Hierarchical Adaboost Regressed
Search region
MCT Response Response
How to obtain feature point candidates?

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Proposed method
Local feature detection
Representation of Feature Response
• How to obtain feature point candidates?
• Local maximum points in candidate search region
arg max x  y, y  , and px  0, x is center of 
x
Segmented
[Input] Response region

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Proposed method
Local feature detection
Representation of Feature Response
• How to obtain feature point candidates?
• We compute distribution of segmented region through convex
quadratic function
is kth segmented region in ith feature point.
is the centroid of
is the inverted feature response function.
• We obtain and : feature candidate’s distribution and centroid.
• Independent Gaussian distribution
Kronecker delta function which is visible.

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Proposed method
Local feature detection
Feature clustering
• Mouth corner’s appearance varies according to facial
expression according.
• The detection performance degrades when only one detector is
used to train for all the mouth shapes and appearances.
Neutral Smile Surprise

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Proposed method
Local feature detection
Feature clustering
• Train each detector with each clustered feature
• Run detectors and combine results


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Proposed method
Local feature detection
Local feature detection
…..
..…
[Candidates
[Input] [Search region] [Adaboost [output of detection]
with Gaussian]
Response]

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Proposed method
Hypothesizing Transformation Parameters
Model Hypotheses
[Input] Evaluation
Local Feature Detection
[Output]
[Hypothesis-and-test]
Hypothesizing
Transformation Parameters
Face Hypothesizing
Detection Shape Parameters

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Proposed method
Hypothesizing Hypo. trans. param.
• Goal
Find a best combination of the
local feature point candidates
which represents input image well.
[Feature point candidates]
• Assumption for occlusion
• We assume that at least half of feature points are not occluded.
• Let be N is total number of features points.
• N/2 feature points can be assumed to be visible ones.

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Proposed method
Hypothesizing Hypo. trans. param.
• Coarse-to-fine approach
– The hypothesis space of visibility of feature p
oints is HUGE.
– Partial Inference (PI) Algorithm
• 1. Transformation parameters (s, R, t) are estimate
d by RANSAC.
• 2. Shape parameters (q) are estimated, also transfo
rmation parameters are updated by RANSAC

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Proposed method
Hypothesizing Transformation Parameters Hypo. trans. param.
Algorithm 1. Partial Inference (PI) algorithm for transformation parameters
[PI algorithm]

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Proposed method
Hypothesizing Shape Parameters
Model Hypotheses
[Input] Evaluation
Local Feature Detection
[Output]
[Hypothesis-and-test]
Hypothesizing
Transformation Parameters
Face Hypothesizing
Detection Shape Parameters

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Proposed method
Hypothesizing Shape Parameters
• From the selected feature points , we calculate parameters p
in closed form by
• Visibility indicator
• to and to are selected candidate’s Gaussian parameters.

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Proposed method
Hypothesizing shape parameters Hypo. shp. param.
Algorithm 2. Partial Inference (PI) algorithm for shape parameters
[Selected feature points]
[Hallucinated shape]

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Proposed method
Hypothesizing for all pose and expression
• Run two hypothesizing steps for all shape mod
els (of face pose and expression)

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Proposed method
Model Hypothesis Evaluation
Model Hypotheses
[Input] Evaluation
Local Feature Detection
[Output]
[Hypothesis-and-test]
Hypothesizing
Transformation Parameters
Face Hypothesizing
Detection Shape Parameters

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Proposed method
Model Hypotheses Evaluation
• We should select best pose and expression from all the
hypotheses.
• Hypothesis error is mean error of inliers(E) over number of
inliers(v).
Num. of Inliers 54 52 43 40
Error of inliers 2.9755 3.23 3.37 2.95

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Video

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EXPERIMENTAL RESULTS

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Experimental results
Training database
• CMU Multi-PIE [7]
• Various pose, expression and illumination
• We used 10,948 images among 750,000 images
• 5 Pose models
• 0°, 15°~30°, 30°~45° (70 feature points)
• 60°~75°, and 75°~90° (40 feature points)
• 2 Expression models
• Neutral and smile
• surprise
[7] Ralph et al., “Guide to the CMU Multi-pie database”, Technical report, CMU, 2007

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Experimental results
Test database
• ARDB [8]
• Occlusion (Sunglasses, and scarf)
• CMU Multi-PIE
• Various pose, expression, illumination
• For artificial occlusion
• LFPW(Labeled Face Parts in the Wild) [9]
• Various pose, expression, illumination, and partial occlusion.
• 29 feature points
• To compare our algorithm with other state-of-the art one
AR DB LFPW
[8] A.M. Martinez and R. Benavente. The AR Face Database. CVC Technical Report #24, June 1998
[9] P. Belhumeur, et al., “Localizing parts of faces using a concensus of exemplars”, IEEE CVPR, 2011

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Experimental results
Alignment Accuracy
• Normalized error
• Euclidean distance between aligned feature and ground truth with
respect to face size.
• If Normalized error is 0.01 with 100 pixel size face,
• distance between aligned feature and ground truth is only one pixel.

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Experimental results
AR database
• Test result
• 60 images

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Experimental results
AR database
• Normalized error for • Cumulative error
occlusion type
Normalized mean error for occlusion type
Non occlusion 0.0226
Scarf 0.0258
Sunglasses 0.0338

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Experimental results
CMU Multi-PIE Database
• Test result
• Test for pose
• 321 images

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Experimental results
CMU Multi-PIE Database
• Normalized mean error • Cumulative error
for pose
Normalized mean error for pose
*60°~90° shows a little poor than 0°~45°.
0° 0.0263 60° 0.0352
Since large portion of the facial features
15° 0.0253 75° 0.0336 are covered by hair, the total number of
30° 0.0273 90° 0.0368 visible feature points detected is too small
to hallucinate correct facial shape.
45° 0.0267

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Experimental results
CMU Multi-PIE Database
• Test for artificial occlusion
• Face area is divided by 5-by-5.
• Among 25 regions, 1 to 15 regions are selected randomly and filled by
black.
• From 8 of occluded regions, the fraction of occlusion starts to be over 50%
of feature points.
• 2,100 images

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Experimental results
CMU Multi-PIE Database
• Test result

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Experimental results
CMU Multi-PIE Database
• Normalized error for pose
• For the profile(60°~90°) view, even small occlusion affects the alignment
badly because there are fewer strong features like eyes, mouth, and
nostrils.
• However, with respect to the mean error, the proposed method shows
stable alignment up to 7 degree of occlusion which is nearly 50% of
occlusion.

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Experimental results
LFPW database
• Mean error over inter-ocular distance for 21 feature points
• 240 of 300 images
* P. Belhumeur, et al., “Localizing parts of faces using a concensus of exemplars”, IEEE CVPR, 2011

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Conclusion
• We proposed pose and occlusion robust face alignment
method.
• To solve pose problem, we used multiple shape models.
• To solve occlusion problem, we proposed partial
inference (PI) algorithm.
• We explicitly determine which part is occluded.
• We proposed Hierarchical MCT+Adaboost for local
feature detector to improve detection performance.

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FUTURE WORK

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Future work
• We combine generative approach (Active Appearance
Model) with discriminative approach (local feature detector).
• Current facial feature tracking
• AAM with temporal matching, template update, and motion
estimation

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Future work
• Problem in facial feature tracking
• Drift problem
Iterative Update
Appearance
Error
arg minE AAM I n , A,p, α 
[Input] [Output]
p,α
 - 
Update
parameters
p  p  p
α  α  α
x  x0   pi si
Condition

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Future work
• By local feature detection result,
• we can constrain the aligned feature points by AAM to the local
feature detector.

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Future work
[Input In]
Iterative Update
[Point constraint]
Feature point Appearance
selection Error
arg minE AAM I n , A,p, α 
p,α
 - 
Local feature [Output]
detector Point Error Update
parameters
 x1  y1  p  p  p
 
E pts   x2  y2  α  α  α
   x  x0   pi si
 
…
 xn  yn 
Condition

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Future work
• By local feature detection result,
• We can make validation matrix of AAM for robust fitting.
• After alignment,
• We run feature detector on the aligned feature points.
• We determine whether each point is occluded or not.
• Based on feature-occlusion information, we make validation matrix
of AAM for robust fitting.
• Validation matrix is used for robust AAM from the next input image.

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Future work
Validation
[Input In] Matrix
Iterative Update
[Point constraint]
Feature point Appearance
selection Error
arg minE AAM I n , A,p, α  Occlusion
 
p,α
Decision
x1-pos.
- x2-neg.
…
xn-pos.
Local feature
detector Point Error Update
parameters
 x1  y1  p  p  p [Output]
 
E pts   x2  y2  α  α  α
   x  x0   pi si
 
…
 xn  yn 
Condition

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Future work
Validation
[Input In+1] Matrix
Iterative Update
[Point constraint]
Feature point Robust
selection App. Error
arg minE AAM I n , A,p, α  Occlusion
p,α Decision
 * -  x1-pos.
x2-neg.
…
xn-pos.
Local feature
detector Point Error Update
parameters
 x1  y1  p  p  p [Output]
 
E pts   x2  y2  α  α  α
   x  x0   pi si
 
…
 xn  yn 
Condition

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Thank you.