Face Age and Demographics
What’s in a Face?
•
Age
•
Gender
•
Race/ethnicity
•
Who cares?
–
These factors may be interesting on their own
–
They also impact face recognition systems
Impact of Demographics on Face
Recognition
•
“Face
Recognition Performance: Role of
Demographic Information
,”
–
B.F.
Klare
, M.J. Burge, J.C.
Klontz
, , R.W.
Vorder
Bruegge
, A.K. Jain, TIFS
vol.7, no.6, pp.1789

1801,
Dec. 2012
.
•
“MBE 2010: Report on the evaluation of 2D still

image face recognition algorithms,”
–
P. J.
Grother
, G. W. Quinn, and P. J. Phillips, National
Institute of Standards and Technology, NISTIR, vol.
7709, 2010.
Demographic Factors
Datasets
•
Per

demographic datasets sampled from
Pinellas County Sherriff's Office (PCSO)
mugshot
database
•
~8,000 samples for most groups
Algorithms Evaluated
•
3 commercial off the Shelf (COTS) systems
–
Black box face recognition systems, no training possible
•
Training free local feature based methods
–
LBP
–
concatenated histograms of LBP values calculated
over patches in a grid, LBP features from 2 radii are
concatenated
–
Gabor features
–
concatenated histograms of (quantized)
phase values calculated over patches in a grid
•
4SF algorithm
–
Trainable algorithm, using LBP as base features
Gabor Filters
•
Complex exponential weighted by a Gaussian
•
Euler’s Theorem:
e
ix
=
cos
(
x
) +
i
sin(
x
)
•
Output of a Gabor filter is a complex number, can
be viewed as
–
Orthogonal real part (Gaussian *
cos
) and imaginary
part (Gaussian * sin)
–
magnitude and phase (polar form)
4SF Algorithm
•
Spectrally Sampled Structural Subspace
Features (4SF) algorithm
Per

Gender True Accept Rates (TAR) at
.1% False Accept Rate (FAR)
Per Race/Ethnicity TAR at .1% FAR
Per Age Group TAR at .1% FAR
Conclusions
•
Worse performance on female, Black, and
younger cohorts for all algorithms tested
•
Training per

demographic models improved
performance slightly, but did not eliminate
performance differences
MBE 2010: Report on the evaluation of
2D still

image face recognition
algorithms
•
MBE 2010: NIST competition (similar to FRVT)
•
7 Commercial, 3 academic algorithms
submitted to NIST, evaluated on operational
face recognition databases
•
Large datasets
–
~1.8 million law enforcement
book images, ~1.8 million visa images
•
Various “investigations”
Investigation 15: Link between sex and
accuracy
•
Marginally worse performance on males
Investigation 16: link between subject
age and accuracy
Investigation 19: Link between race
and accuracy
MBE 2010 Conclusions
•
Impact of gender:
–
Generally marginally worse performance on
femalse
than males
•
Impact of age:
–
Variable effect, often small not consistent across
algorithms
•
Impact of race:
–
“
Blacks are easier to recognize than
whites
for 5 of
the 6 algorithms. American Indians and Asians were
clearly easier to recognize for 3 of the algorithms
(P01, Z05, and Too), while for V07 American Indians
and Asians appeared more difficult to recognize.
“
Inconsistencies
•
MBE 2010 report conclusions on gender, and race are wildly
inconsistent with results reported in the TIFS paper
(although the 3 COTS used in the TIFS paper were also
evaluated in MBE 2010).
•
Experimental method:
590,105 genuine comparisons from
the FBI set were separated by race. For each algorithm, and
for each race, a distribution of FNMRs (at FMR=0.001) was
computed using 2000 bootstrap iterations. The resulting
plot shows how the false non

match rate differs for
different races. A given genuine comparison was
only
retained
if the recorded race was consistent across all
image captures for the given individual. The false match
rate was computed using results from study 3.
–
Speculation
: The imposter scores used for each group were
drawn from the complete dataset
Within Group vs. Between Group
Imposter Score Distributions
•
FaceVACS scores on a full comparison matrix of
5,000 probe vs. 5,000 gallery images drawn from
the PCSO dataset
•
Mean within

race imposter score: .0769
•
Mean cross

race imposter score: .0487
•
Proportion of imposter scores > .5:
–
Within race: .15 %
–
Cross race: .0083%
•
A demographically

imbalanced data set has fewer
plausible imposters for minority groups
Age Estimation from Face Images
•
Given a face image, predict the subjects age
•
Who cares?
–
Various possible applications, again including
improving recognition accuracy
AGES
•
Geng
,
Xin
,
Zhi

Hua
Zhou, and Kate Smith

Miles
. “Automatic
age estimation based on
facial aging patterns
.”
Pattern Analysis and
Machine Intelligence, IEEE Transactions on
29.12 (2007):
2234

2240
FG

NET dataset
•
1,002 images of 82 subjects, avg. 12.2 per
subject
Representation
•
Given images of a single subject:
–
Extract features:
•
AAM PCA shape and appearance coefficients
•
Apply LDA to the AAM coefficients using sample ages as
class labels (
AGES
lda
)
–
Form “Aging Pattern,” concatenated features from
every year of the subjects life with some missing
values
–
Age range 0

69, avg. 12.2 samples per subject in
FG

NET => aging pattern is mostly missing values
AGES Overview
•
For an aging patterns
x
k
, find a subspace
projection:
𝒌
=
𝐖
T
(
𝑘
−
μ
)
–
x
k
= {
𝑘
𝑎
,
𝑘
𝑚
}
•
Since
x
is mostly missing values just applying
PCA won’t work
•
Reconstruction:
𝒌
=
𝜇
+
𝐖
𝑘
–
𝒌
= {
𝑘
𝑎
,
𝑘
𝑚
}
•
Goal: Find W which minimizes:
Training Procedure
•
“E

M like algorithm”
•
Initialization:
–
Replace
𝑘
𝑚
with [
𝝁
𝑘
𝑚
]
–
Apply PCA to get
W
0
and
μ
0
•
Repeat until convergence:
–
Find
y
k
the least squares solution of:
–
Calculate
𝒌
, replace
𝑘
𝑚
with
𝑘
𝑚
–
Apply PCA to get
W
i
+1
and
μ
i
Subspace Based Age Estimation
•
Given W and
μ
which minimize:
•
How to predict the age of a test image?
•
Generate p aging patterns
z
j
(j=1:p), calculate
corresponding
y
j
via:
•
Select the age which minimizes reconstruction
error from projecting
z
j
into the subspace:
Age Estimation Performance Metrics
•
Mean Absolute Error (MAE):
–
Summary statistic:
1
𝑁
𝐴𝑔𝑒
−
𝐴𝑔𝑒
𝑖𝑚𝑎
𝑁
𝑖
=
1
•
Cumulative Score (CS) Plot:
–
x

axis: absolute error (years)
–
y

axis: fraction of test data with error ≤ x

value
Results
FG

NET (LOPO)
MORPH Album 1
Human Age Estimation on FG

NET
Dataset
•
This paper uses age estimates from 29
humans on 51 face images, MAE: 6.23
•
Results from mechanical
turk
, 5 human
estimates per image for all 1,002 FG

NET
images: MAE: 4.7
Imbalanced age estimation
•
FG

NET is biased towards young ages
•
Build per age

range AGES models:
•
4.15 MAE if GT age groups are used, only
slight improvement otherwise
“Age
Synthesis and Estimation via
Faces: A
Survey”
•
Yun
Fu;
Guodong
Guo
;
Huang, T.S
.; Pattern
Analysis and Machine Intelligence, PAMI
2010
–
Human facial aging
–
Age image synthesis
–
Age estimation
Facial Aging
•
2 main stages
–
Early growth
•
Changes characterized by craniofacial growth
•
In age estimation, shape based features are good for images
of children
–
Adult aging
•
Some shape change over large time periods, but relatively
minor
•
Skin texture changes
–
“skin becomes thinner, darker, less
elastic, and more leathery”
•
Wrinkles form
•
In age estimation, shape based features less effective for
adults
Age Synthesis
•
Given an image, generate a synthetic image of
the subject at an older or younger age
•
Typically need a model:
–
Geometry

Based
•
ASM, Facial Action Coding System (FACS), caricature
generator
–
Image based
•
Clone face attributes from source to target image, Merging
Ratio Images
–
Appearance

Based Models (shape and texture)
•
AAM
•
3D
Morphable
model
Age Synthesis Methods
•
Given a face model, how to generate synthetic
images?
–
Explicit Data Driven Synthesis
•
Given a shape model, apply a transform to simulate aging
(PCA based caricature model, growth model
–
Explicit Mechanical Synthesis
•
Given a texture model, wrinkle synthesis, structural face
model (
explictly
modeling layers of skin and muscle)
–
Implicit Statistical Synthesis
•
Based on Shape + Texture models, variations due to aging
modeled statistically
Face Representations
•
Anthropometric models
—
statistics derived from e.g.
ratio of distances between
keypoints
, normalized
keypoint
locations (shape based representation
•
AAM based features
—
fitting an AAM gives appearance,
and shape coefficients that together describe a face
•
Manifold learning
–
given a set of subjects with known
ages, learn a low

dimensional representation capturing
the age distribution
•
Local feature based methods
–
LBP based features,
Gabor Features, BIF features (variation of Gabor
features)
–
Spatially flexible Patch (SFP)
–
patch location encoded
along with descriptors
Age Estimation Methods
•
Classification based methods
–
Consider age prediction an N class classification problem, apply
some classifier
–
Doesn’t account for the relationship between class labels
•
Regression based methods
–
Consider age prediction a regression problem, predict a
numerical value for age
–
Ages are (typically) discrete values, also constrained to a very
specific numerical range
•
Ordinal regression techniques can also be applied, e.g.
–
Kuang

Yu Chang; Chu

Song Chen; Yi

Ping Hung; , "Ordinal
hyperplanes
ranker with cost sensitivities for age
estimation,"
Computer Vision and Pattern Recognition (CVPR),
2011 IEEE Conference on
, vol., no., pp.585

592, 20

25 June
2011
Hierarchical Methods
•
Different features may be better for
differentiating coarse age groups than
predicting exact ages
•
Possible hierarchical models:
–
Age group classification

> within group regression
–
Rough age prediction via regression

>
classification within some range around estimate
–
Full age range regression

> regression within a
restricted range around the initial estimate
Impact of Demographics on Age
Estimation
•
Guodong
Guo
;
Guowang
Mu; , "Human age estimation: What is the
influence across race and gender?,"
Computer Vision and Pattern
Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference
on
, vol., no., pp.71

78, 13

18 June
2010
–
Experiments on MORPH Album 2 (public
mugshot
database)
–
Higher age estimation error when training and testing on
different demographic groups
–
Gender classification

> Race Classification

> Age Estimation
improved MAE vs. direct age estimation
•
Guodong
Guo
;
Guowang
Mu; , "Simultaneous dimensionality reduction and human age
estimation via kernel partial least squares regression,"
Computer Vision and Pattern
Recognition (CVPR), 2011 IEEE Conference on
, vol., no., pp.657

664, 20

25 June
2011
–
Simultaneous gender, race, and age estimation using KPLS on
BIF features, better age estimation performance than 3 stage
age estimation process
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