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Microsoft Research March 17, 2006 Face recognition: Opportunities and Challenges Yu Hen Hu University of Wisconsin – Madison Dept. Electrical and Computer Enginerring Madison, WI 53706 yhhu@wisc.edu Collaborators: Nigel Boston, Charles Dyer, Weiyang Lin, Ryan Wong, Goudong Guo Agenda

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face recognition opportunities and challenges

Microsoft Research

March 17, 2006

Face recognition: Opportunities and Challenges

Yu Hen Hu

University of Wisconsin – Madison

Dept. Electrical and Computer Enginerring

Madison, WI 53706

yhhu@wisc.edu

Collaborators: Nigel Boston, Charles Dyer,

Weiyang Lin, Ryan Wong, Goudong Guo

agenda
Agenda
  • Human Face Recognition Problem
  • Recent Progress and Findings
  • Research at UW-Madison
  • Invariant of transformation group
    • Existing Invariants
    • Integral invariant
    • Summation invariant
  • Experiment Environment
    • Face Recognition Grand Challenge
    • BEE: Biometric Experimentation Environment
  • Preliminary FRGC Results

(c) 2004-2006 by Yu Hen Hu

face recognition problem
Face Recognition Problem

gallery

probe image

Probe

(c) 2004-2006 by Yu Hen Hu

face recognition by human
Face Recognition by Human

A. Adler and J. Maclean,

“Performance comparison of human and automatic

face recognition”

Biometrics Consortium Conference 2004

(c) 2004-2006 by Yu Hen Hu

understanding human face recognition
Understanding Human Face Recognition
  • Many physiological and psychological studies have been conducted. Eg. Pawan Sinha @MIT. For example, we learned
  • facial configuration plays an important role in human judgments of identity [Sinha05]

Celebrity faces created using identikits

[Sinha05]

C:\users\yuhen\1Research\face\sinha\

(c) 2004-2006 by Yu Hen Hu

human face recognition limitations
Human Face Recognition Limitations

Harmon and Julesz, 1973

Degraded images

Human can handle low resolution celebrity images quite well.

[Sinha05]

(c) 2004-2006 by Yu Hen Hu

face recognition by machine
Face Recognition by Machine

Pre-processing

Feature Extraction

Pattern Classification

(c) 2004-2006 by Yu Hen Hu

face recognition process
Face Recognition Process
  • A pattern classification problem.
  • Two steps:
    • Feature extraction
    • Classification
  • Feature extraction properties
    • Invariant
    • Discriminant
  • Classification
    • Often use distance based method

Query image

Feature Extraction

Query image feature vector

Gallery face

images

feature vectors

Pattern Classification

Recognition results

Wei-Yang Lin

(c) 2004-2006 by Yu Hen Hu

challenges
Challenges
  • Sinha et al [2005] use this example to illustrate the difficulty of finding a suitable “similarity” measure to gauge similarity between a pair of faces.
  • In this example, the outer two faces actually belong to the same person while the middle one does not. But conventional pixel-based measures who say otherwise.
  • Common variations in pose (this case), lighting, expression, distance, aging remain challenges to face recognition.

(c) 2004-2006 by Yu Hen Hu

face recognition challenges
Face Recognition Challenges

Table 1 Face Recognition Technology Evaluation Size

Note that the MCINT portion of FRVT 2002 is the only test in this chart that included “video” signatures.Signatures in all other tests were a single still image.

http://www.frvt.org/FRVT2002/default.htm

(c) 2004-2006 by Yu Hen Hu

face recognition grand challenge
Face Recognition Grand Challenge
  • Goal: to advance performance of face recognition by 10-fold (20%  2% verification rate @0.1% false alarm rate)
  • Focus on five different scenarios.
  • Status: on-going to be concluded by the end of 2005

(c) 2004-2006 by Yu Hen Hu

object recognition
Object Recognition
  • Three Approaches for object recognition (Powen Sinha)
    • Transformationist approach
      • Requires normalization
      • Computationally expensive.
    • View-based approach
      • Store all possible views of the same object
      • Expensive on storage.
    • Invariant-based approach
      • Different views  same invariant features
  • Desired properties of features
    • Invariant to variations of the same object
    • Discriminate to separate similar objects

(c) 2004-2006 by Yu Hen Hu

geometric transformation groups

Euclidean

Similarity

Affine

Projective

Geometric Transformation Groups

(c) 2004-2006 by Yu Hen Hu

moment invariants
Moment Invariants
  • Introduced by M. K. Hu in 1962
  • Advantages
    • Do NOT require parameterization.
    • Not sensitive to noise.
  • Limitations
    • Low discriminating power.
    • Local characteristics can NOT

be extracted.

(c) 2004-2006 by Yu Hen Hu

differential invariants
Differential Invariants
  • Two examples in 2D
  • Limitation
    • sensitive to noise

(c) 2004-2006 by Yu Hen Hu

integral invariants
Integral Invariants
  • Hann and Hickman [2002] extend transformation to integrals
  • Advantages
    • do NOT require derivatives
    • local characteristics can be extracted
    • Invariants can be systematically generated
  • Limitation
    • Require analytical expression of shape

(c) 2004-2006 by Yu Hen Hu

summation invariants
Summation Invariants
  • Introduced by Lin et al. [2005]
  • Advantages
    • systematical approach
    • robustness
    • high discriminating power
  • Limitation
    • require parameterization

(c) 2004-2006 by Yu Hen Hu

method of moving frame
Method of Moving Frame
  • The method of moving frame, introduce by Elie Cartan, is a tool for finding invariants under group actions.
  • Definition: A moving frame is a smooth, G-equivariant map

(c) 2004-2006 by Yu Hen Hu

example integral invariants of e 2
Example: Integral Invariants of E(2)

(c) 2004-2006 by Yu Hen Hu

example summation invariants of e 2
Example: Summation Invariants of E(2)

(c) 2004-2006 by Yu Hen Hu

euclidean summation invariants of curves
Euclidean Summation Invariants of Curves
  • Given a curve under Euclidean transformation
  • We can find a moving frame by solving

(c) 2004-2006 by Yu Hen Hu

euclidean summation invariants of curves23
Euclidean Summation Invariants of Curves
  • From the moving frame, a family of invariant functions can be derived

(c) 2004-2006 by Yu Hen Hu

euclidean summation invariants of curves24
Euclidean Summation Invariants of Curves
  • The first summation invariants are explicitly shown below

where

(c) 2004-2006 by Yu Hen Hu

euclidean summation invariants of surfaces
Euclidean Summation Invariants of Surfaces
  • Similarly, the family of surface invariants

where

(c) 2004-2006 by Yu Hen Hu

face recognition grand challenge frgc
Face Recognition Grand Challenge (FRGC)
  • Organized by NIST to facilitate the development of FR technology
  • Provide challenging problems and facial images
  • FRGC v2.0 dataset contains 50,000 recordings, including
    • high resolution still images
    • 3D images

(c) 2004-2006 by Yu Hen Hu

four frgc experiments
Four FRGC Experiments
  • Controlled indoor
  • Multiple images
  • 3D images
  • Controlled vs. uncontrolled

(c) 2004-2006 by Yu Hen Hu

baseline @ far 0 1
Baseline @ FAR = 0.1%

(c) 2004-2006 by Yu Hen Hu

biometric experimentation environment bee
Biometric Experimentation Environment (BEE)

Image Preprocessing

BioBox

Sub-similarity Generation

Similarity Normalization

Analysis

(c) 2004-2006 by Yu Hen Hu

sub similarity generation
Sub-similarity Generation

(c) 2004-2006 by Yu Hen Hu

frgc 3d experiment
FRGC 3D Experiment

(c) 2004-2006 by Yu Hen Hu

frgc 3d baseline algorithm
FRGC 3D Baseline Algorithm

(c) 2004-2006 by Yu Hen Hu

proposed algorithm
Proposed Algorithm

shape

curve

SI

PCA

similarity

score

similarity

score

+

PCA

similarity

score

shape

surface

SI

(c) 2004-2006 by Yu Hen Hu

experiment setup
Experiment Setup
  • Use only 3D shape. Note 2D texture is not utilized in our experiment.
  • Specified the region of interest
  • At each pixel, compute summation invariants from a specified window region
  • Perform PCA to reduce dimensionality

(c) 2004-2006 by Yu Hen Hu

roc performance of curve invariants
ROC Performance of Curve Invariants

(c) 2004-2006 by Yu Hen Hu

roc performance of surface invariants
ROC Performance of Surface Invariants

(c) 2004-2006 by Yu Hen Hu

fusion of summation invariants
Fusion of Summation Invariants

(c) 2004-2006 by Yu Hen Hu

comparison with baseline algorithm
Comparison with Baseline Algorithm

(c) 2004-2006 by Yu Hen Hu

conclusion
Conclusion
  • Summation invariants
    • novel geometric feature
    • provide useful shape information
    • fusion further improve recognition performance
  • In principle, one can apply summation invariants to unnormalized images

(c) 2004-2006 by Yu Hen Hu