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The IJB-A Face Verification Challenge
Performance Report
Patrick Grother and Mei Ngan
Caution: This report quantifies face recognition performance
using data supplied byexternal research and development
organizations. Its results are derived from self-administered
experiments on the fully public IJB-A dataset. As such the results
canbe manipulated by various means that may not be operationally
realistic. Therefore,end users of face recognition technology
should prefer results from NIST’s ongoingsequestered testing
campaigns, FRVT or FIVE, or on similar independent evaluationsof
face recognition. Developers whose algorithms exhibit good
performance here areencouraged to submit their algorithms into
those sequestered tests programs.
This report is generated automatically. It will be updated as
new algorithms areevaluated, and as new analyses are added.
Automated notifications can be obtainedvia the mailing list.
Correspondence should be directed to the authors via
[email protected].
This report was last updated on April 28, 2017.
http://www.nist.gov/itl/iad/ig/facechallenges.cfmhttp://www.nist.gov/itl/iad/ig/frvt-home.cfmhttp://www.nist.gov/itl/iad/ig/five.cfmmailto:[email protected]?subject=subscribe
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IJB-A 1:1 VERIFICATION 1
Figure 1: Three images of one subject in the IJB-A dataset. The
entire dataset is available online. Many photos were taken by photo
journalistsand, as such, are well exposed, well focused, and deemed
suitable for public display. For face recognition, they
nevertheless remain challengingdue to wide variations in pose,
illumination, expression and occlusion.
1 Introduction
Three IARPA Janus Benchmark A challenges are described by Klare
et al. in the paper Pushing the Frontiers ofUnconstrained Face
Detection and Recognition[2]. The first of these, the IJB-A 1:1
challenge, quantifies performanceof face verification algorithms
(“same person or not?”) on challenging photo-journalism images of
the kind shownin Figure 1. They are considerably more difficult to
recognize than the portraits mandated by facial
recognitionstandards1.
IJB-A 1:1 is a “take-home” test in that it is based on fully
public data. It follows the design of the LFW protocol inrequiring
many pairs of samples to be compared in isolation2. This
corresponds to recognition tasks like passportverification or
forensic comparison where there is just a pair of samples and no
central database or gallery.
The IJB-A 1:1 challenge departs from LFW as follows:
. Face selection: LFW contains faces that could be detected with
the Viola-Jones face detection algorithm. Thislimits difficulty.
IJB-A on the other hand, uses manually located and annotated
faces.
. Landmarks: The IJB-A tests include landmark coordinates (eyes
and nose) whereas LFW provides just rawimages, and aligned
(funneled) images.
. Multi-image samples: LFW compared single images. IJB-A uses
richer samples containing 1 ≤ K ≤ 202images, including frames from
video sequences.
. More impostor pairs: IJB-A 1:1 uses many more impostor
comparisons that genuines. In LFW, the ratio was 1which precluded
computation of false match rates at usefully low values.
2 Metrics
This section describes the one-to-one verification accuracy
metrics present in this report.
1 NIST maintains a challenge for such images based on the
mugshots of NIST Special Database 32 (“MEDS”)[1]. This is intended
as a steppingstone prior for developers prior to entering NIST’s
ongoing fully sequestered FRVT verification test.
2IJB-A 1:1 does not cross-compare galleries and probesets; it
has no concept of such. It does not attempt to measure both
verification andidentification accuracy from the same similarity
score matrix; it does not pin the prior probabilities of impostor
vs. genuine pairs i.e. O(n2) vs.O(n).
NIST 1 Last updated: April 28, 2017
http://www.nist.gov/itl/iad/ig/facechallenges.cfmhttp://www.nist.gov/itl/iad/ig/facechallenges.cfmhttp://www.nist.gov/itl/iad/ig/frvt-home.cfm
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IJB-A 1:1 VERIFICATION 2
2.1 Quantifying false acceptance
False acceptance is computed over N comparisons. Each comparison
involves a pair of samples, one from each oftwo different
individuals. Each comparison yields a single non-negative scalar
similarity score. The false match rate(FMR) is defined as the
proportion of scores that are at or above threshold T .
FMR(T ) = 1− 1N
N∑i=1
H(si − T ) (1)
where H is the Heaviside unit step, and si is the score from the
i-th impostor comparison.
2.2 Quantifying false rejection
False rejection is computed over M comparisons. Each comparison
involves a pair of samples. Both samples comefrom a single
individual. Each comparison yields a single non-negative scalar
similarity score. The false non-matchrate is defined as the
proportion of scores that are below a threshold T .
FNMR(T ) =1
M
M∑i=1
H(si − T ) (2)
with si being the score from the i-th genuine comparison.
The common term true accept rate (TAR) is a synonym for the
complement 1-FNMR. This document uses neitherTAR nor false accept
rate (FAR), as these are reserved[3] for transactional error rates
involving potentially severalattempts to use a biometric system.
The terms here, FNMR and FMR, are matching error rates, befitting
IJB-A.
2.3 Quantifying failure to enrol
The above definitions assume that each comparison produces a
score. Indeed the IJB-A protocol requires a completeset of scores
to be submitted. This is operationally atypical because: a) some
algorithms electively refuse to enrolimagery that is too poor to
process; b) face or landmark detection can fail; c) feature
extraction fails; and d) soft-ware throws an exception. Together
these outcomes are quantified by the failure-to-enrol rate (FTE), a
term whosedefinition is overloaded in the literature, and in
practice.
This report includes two statements of the failure to enrol rate
(FTE).
. Empty templates: The proportion of all templates that are
empty, where empty is defined as having size below32 bytes. This
value is used heuristically to handle implementations that
sometimes fail to produce a viabletemplate but nevertheless include
a short header.
. Failed comparisons: The proportion of comparisons that give a
non-zero return code, or a negative similarityscore.
The consequences of FTE operationally should depend on the
application. A one-to-one access control systemwould reject the
presentation, and allow limited re-submissions of new face samples.
In negative identificationsystems, where subjects make an implicit
claim not to be present in a database (e.g. deportees), the correct
actionwould be to investigate the sample for evidence of evasion or
tampering. To effect fair comparison of algorithms,failed
comparisons must be accounted for. This is done here by setting
scores to zero to simulate rejection. Thiscorresponds operationally
to a (fortuitously) correct rejection of an impostor, and an
incorrect rejection of a genuineuser. Note that in the case of
negative identification, scores should be set to high values to
simulate the triggeringof a human investigation. This would
correctly flag enrolled subjects, and incorrectly flag the
non-enrolled. Mostresearch reports set scores to zero, as is done
here.
NIST 2 Last updated: April 28, 2017
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IJB-A 1:1 VERIFICATION 3
This issue presents a gaming opportunity: An algorithm developer
can code a strategy for handling low qualityimages if he knows or
assumes the number of impostor comparisons in a test will be larger
(or smaller) than thenumber of genuines. In such cases, his
implementation would speculatively and preferentially guess a low
(high)score in cases where feature extraction fails, or when image
quality is poor. While the IJB-A challenge is wide opento such
gaming, there are several mitigations to such strategies,
particularly in sequestered tests. Operationally, anoff-the-shelf
implementation will not know the prior probability of an
impostor.
2.4 Score-range normalization
Some graphs in this report include similarity scores normalized
via the probability integral transform. If the empir-ical
cumulative distribution of the comparison scores is F , then the
new variable
s† = F (s) (3)
will be uniformly distributed on [0,1] (absent tied scores).
This transformation is applied later to compare theresponse of
different algorithms to certain covariates.
If the transform is applied to just the genuine scores, then for
a threshold, T, the quantity F(T) is the false non-matchrate,
FNMR(T), i.e. the fraction of genuine scores below threshold.
Likewise a function G(T) computed over just theimpostor scores
gives FMR(T) = 1 - G(T).
The function F, computed from genuine scores, can also be
applied to impostor scores.
3 Results
3.1 Comparing accuracy
The graphs that follow include results for several classes of
algorithms that are differentiated by their developmentdate, and
use of landmarks and training data - see Table 1. This latter issue
is nuanced and yet critical to under-standing how and whether
algorithms can be compared. Historically commercial algorithms have
been providedand used in an entirely off-the-shelf manner - the
representation is fixed and the user in no way adapts (trains)the
algorithm to his native data. The academic community, meanwhile,
almost always isolates some portion of thedata for the express
purpose of adapting the algorithm. The result is a refined set of
parameters, or explicit data“models” (most prosaically, a PCA basis
set). The academic community, ignoring marketplace practice, has
notedthat recognition accuracy is improved by training, and
training is improved through detailed exploitation of largetraining
sets. Why then do commercial implementations not roll-out training
facilities within their commercial offthe shelf products. The
answer partly rests on the observation that succesful training and
adaptation is a fine artthat, empirically, cannot be canned in a
simple function call.
That said, one particular kind of training is possible
operationally: Gallery training occurs after templates havebeen
enrolled into a gallery. This is discussed in the accompanying 1:N
report. The issue is moot here because 1:1verification exists in
the IJB-A challenge without any gallery ever being constructed.
3.2 Performance gains through time
Figure 10 shows DETs by organization. The DETs generally march
down and to the left.
NIST 3 Last updated: April 28, 2017
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IJB-A 1:1 VERIFICATION 4
Algorithm Developmentthru
Training data Role of provided IJB-A landmarks
BENCH-MK1 2010 External training data onlyANON-2013 2013
External training data only Algorithm was provided only with
image cropped from bounding boxMSU-071715 2015 External training
data and IJB-A
training splitsAlgorithm was provided only withfull
IJBA-specified landmarks
JANUS* 2015-09 External training data and IJB-Atraining
splits
Algorithm was provided only withfull IJBA-specified
landmarks
RankOne-011816 2016-01 External training data Developer asserts
IJBA boundingboxes and landmark points were notused.
Table 1: Context of use: Comparison of the algorithms should be
conducted in the context of variations in when they were developed,
withwhat training data and on whether the ran in fully automated
mode or were assisted by the provision of geometric information.
Note thatthe ANON-2013 algorithm was developed before the IJB-A
challenge was assembled and was provided to NIST without the
expectation that itwould be run on images of this type.
FTE EMPTY TEMPLATES FTE FAILED COMPARISONS
BENCH−MK1
JanusB−071315
JanusB−092015
JanusC−071515
JanusC−090815
JanusD−071715
MSU−072115
NUS−Panasonic−032917
VCOG−021317
COTS−04
COTS−08
RankOne−011816
RankOne−091015
0.0 0.1 0.2 0.3 0.0 0.1 0.2 0.3Algorithm
Failu
re to
Ext
ract
Rat
e
Failure to enrol, updated 2017−04−27 18:13:14
Figure 2: Failure to enrol: Per the discussion in section 2.3,
the chart quantifies failure to enrol rate (FTE) in two ways: as
the proportion ofempty templates; and as the proportion of failed
comparisons. These two quantities are not independent since empty
templates should not yieldvalid comparison scores.
NIST 4 Last updated: April 28, 2017
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IJB-A 1:1 VERIFICATION 5
0.041
0.155
0.178
0.227
0.265
0.275
0.303
0.421
0.448
0.543
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NUS−Panasonic−032917
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COTS−04
0.0 0.2 0.4 0.6 0.8FNMR@FMR=0.01
Figure 3: Leaderboard: The chart shows the false non-match rate
(FNMR) at a false match rate (FMR) of 0.01, as a headline
comparativeaccuracy statement. This FMR value is higher than that
typically targeted in operational face verification settings, but
is chosen here given thelimited number of subjects present in the
IJB-A 1:1 dataset. The effects of failure-to-enrol are included in
these accuracy numbers. The full errortradeoff characteristics of
Figure 4 give accuracy estimates at a broader range of FMR
values.
NIST 5 Last updated: April 28, 2017
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IJB-A 1:1 VERIFICATION 6
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NIST 6 Last updated: April 28, 2017
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IJB-A 1:1 VERIFICATION 7
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NIST 7 Last updated: April 28, 2017
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IJB-A 1:1 VERIFICATION 8
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NIST 8 Last updated: April 28, 2017
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IJB-A 1:1 VERIFICATION 9
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T(1
) =
160
00,
T(n
) ~
160
00n^
1.0
byte
s
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T(1
) =
560
0,
T(n
) ~
419
7n^1
.0 b
ytes
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T(1
) =
128
000,
T
(n)
~ 1
2798
2n^1
.0 b
ytes
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T(1
) =
937
52,
T(n
) ~
937
39n^
1.0
byte
s
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T(1
) =
411
2,
T(n
) ~
410
7n^1
.0 b
ytes
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T(1
) =
414
4,
T(n
) ~
413
9n^1
.0 b
ytes
●●
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T(1
) =
163
84,
T(n
) ~
163
84n^
0.0
byte
s
●
●
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76,
T(n
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243
12n^
0.4
byte
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T(1
) =
136
, T
(n)
~ 8
2n^1
.0 b
ytes
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T(1
) =
128
, T
(n)
~ 6
7n^1
.0 b
ytes
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T(1
) =
820
8,
T(n
) ~
820
8n^0
.0 b
ytes
BE
NC
H−
MK
1C
OT
S−
04C
OT
S−
08Ja
nusB
−07
1315
Janu
sB−
0920
15Ja
nusC
−07
1515
Janu
sC−
0908
15Ja
nusD
−07
1715
MS
U−
0721
15N
US
−P
anas
onic
−03
2917
Ran
kOne
−01
1816
Ran
kOne
−09
1015
VC
OG
−02
1317
0.0e
+00
4.0e
+06
8.0e
+06
1.2e
+07
0
5000
00
1000
000
1500
000
2000
000
0e+
00
2e+
05
4e+
05
6e+
05
0.0e
+00
5.0e
+06
1.0e
+07
1.5e
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