1 

 

An Introduction to the Good, the Bad, 

& the Ugly Face Recognition Challenge 

Problem

Phillips, P. J.

Beveridge, J. R.

Draper, B. A.

Givens, G. H.

O’Toole, A. J.

Bolme, D.S.

Dunlop, J.

Lui, Y. M.

Sahibzada, H. A.

Weimer, S.

NISTIR 7758
 2 

NISTIR 7758

An Introduction to the Good, the Bad, & the 

Ugly Face Recognition Challenge Problem

Phillips, P. J. 

Sahibzada, H. A. 

NIST 

Information Access Division 

Beveridge, J. R. 

Draper, B. A. 

Givens, G. H. 

Bolme, D. S. 

Lui, Y. M 

Colorado State University 

O’Toole, A. J. 

Dunlop, J. 

Weimer, S. 

U. of Texas at Dallas 

March 2011

U.S. Department of Commerce 

Gary Locke, Secretary 

National Institute of Standards and Technology 

Patrick D. Gallagher, Director
An Introduction to the Good, the Bad, & the Ugly Face Recognition

Challenge Problem

P. Jonathon Phillips, J. Ross Beveridge, Bruce A. Draper, Geof Givens, Alice J. O’Toole,

David S. Bolme, Joseph Dunlop, Yui Man Lui, Hassan Sahibzada, and Samuel Weimer

Abstract— The Good, the Bad, & the Ugly Face Challenge

Problem was created to encourage the development of algorithms that are robust to recognition across changes that occur

in still frontal faces. The Good, the Bad, & the Ugly consists of

three partitions. The Good partition contains pairs of images

that are considered easy to recognize. On the Good partition,

the base verification rate (VR) is 0.98 at a false accept rate

(FAR) of 0.001. The Bad partition contains pairs of images of

average difficulty to recognize. For the Bad partition, the VR

is 0.80 at a FAR of 0.001. The Ugly partition contains pairs

of images considered difficult to recognize, with a VR of 0.15

at a FAR of 0.001. The base performance is from fusing the

output of three of the top performers in the FRVT 2006. The

design of the Good, the Bad, & the Ugly controls for pose

variation, subject aging, and subject “recognizability.” Subject

recognizability is controlled by having the same number of

images of each subject in every partition. This implies that the

differences in performance among the partitions are result of

how a face is presented in each image.

I. INTRODUCTION

Face recognition from still frontal images has made great

strides over the last twenty years. Over this period, error

rates have decreased by three orders of magnitude when

recognizing frontal faces in still images taken with consistent

controlled illumination in an environment similar to a studio [1], [2], [3], [4], [5], [6]. Under these conditions, error

rates below 1% at a false accept rate of 1 in 1000 were

reported in the Face Recognition Vendor Test (FRVT) 2006

and the Multiple Biometric Evaluation (MBE) 2010 [4], [6].

With this success, the focus of research is shifting to

recognizing faces taken under less constrained conditions.

Less constrained conditions include allowing greater variability in pose, ambient lighting, expression, size of the

face, and distance from the camera. The trick in designing a

face recognition challenge problem is selecting the degree

to which the constraints are relaxed so that the resulting

problem has the appropriate difficulty. The complexity of

this task is compounded by the fact that it is not well

understood how the above factors effect performance. The

problem cannot be too easy that it is an exercise in tuning

P. J. Phillips and H. Sahibzada are with the National Institute of Standards

and Technology, 100 Bureau Dr., MS 8940 Gaithersburg MD 20899, USA

(e-mail: jonathon@nist.gov). Please direct correspondence to P. J. Phillips.

J. R. Beveridge, B. A. Draper, D. S. Bolme, and Y-M Lui are with

the Department of Computer Science, Colorado State U., Fort Collins, CO

46556, USA.

G. Givens is with the Department of Statistics, Colorado State U., Fort

Collins, CO 46556, USA.

A. J. OToole, J. Dunlop, and S. Weimer are with the School of Behavioral

and Brain Sciences, GR4.1 The University of Texas at Dallas Richardson,

TX 75083-0688, USA

existing algorithms, nor so hard that progress cannot be

made—the three bears problems [2].

Traditionally, a challenge problem is specified by the two

sets of images that are to be compared. The difficulty of

the problem is then characterized by the performance of

a set of algorithms tasked with matching the two sets of

face images. To create a problem of a desired level of

difficulty, a set of algorithms could be one component in

the image selection process. Others factors in the selection

process include limiting the number of images per person

and requiring that pairs of images of a person are collected

on different days.

The Good, the Bad, and the Ugly (GBU) challenge problem consists of three partitions which are called the Good, the

Bad, and the Ugly. The difficulty of each partition is based

on the performance of three top performers in the FRVT

2006. The Good partition consists of pairs of face images

of the same person that are easy to match; the Bad partition

contains pairs of face images of a person that have average

matching difficulty; and the Ugly partition concentrates on

difficult to match face pairs. For the Good partition, the nominal performance based on the FRVT 2006 is a verification

rate (VR) of 0.98 at a false accept rate (FAR) of 0.001. For

the Bad and Ugly partitions, the corresponding VR at a FAR

of 0.001 are 0.80 and 0.15. The performance range over the

three partitions is roughly an order of magnitude. The three

partitions capture the range of performance inherent in less

constrained images1.

There are numerous sources of variation, known and

unknown, in face images that can effect performance. Four

of these factors are explicitly controlled in the design of

the GBU challenge problem: subject aging, pose, change

in camera, and variations among faces. The data collection

protocol eliminated or significantly reduced the impact of

three of the factors. Changes in the appearance of a face due

to aging is not a factor because all images were collected in

the same academic year. However, the data set contains the

natural variations in a person’s appearance that would occur

over an academic year. Because all the images were collected

by the same model of camera, difference in performance

cannot be attributable to changes in the camera. Changes in

pose are not a factor because the data set consists of frontal

face images.

One potential source of variability in performance is that

1

Instructions for obtaining the complete GBU distribution can be found

at http://face.nist.gov. Instructions for obtaining the LRPCA algorithm can

be found at http://www.cs.colostate.edu/facerec.
people vary in their “recognizability.” To control for this

source of variability, the face images of the same people are

in each partition. In addition, each partition has the same

number of images of each person. Because the partition

design controls for variation in the recognizability of faces,

the differences in performance among the three partitions are

a result of how a face is presented in each image, and with

the pairs of faces that are matched.

II. GENERATION OF THE GOOD, THE BAD, & THE UGLY

PARTITIONS

The GBU partitions were constructed from the Notre

Dame multi-biometric data set used in the FRVT 2006 [4].

The images for the partitions were selected from a superset of

9,307 images of 570 subjects. All the images in the superset

are frontal still face images collected either outside or with

ambient lighting in hallways. The images were acquired with

a 6 Mega-pixel Nikon D70 camera. All photos were taken

in the 2004-2005 academic year (Aug 2004 through May

2005).

Each partition in the GBU is specified by two sets of

images, a target set and a query set. For each partition, an

algorithm computes a similarity score between all pairs of

images in that partition’s target and query sets. A similarity

score is a measure of the similarity between two faces.

A higher similarity scores implies greater likelihood that

the face images are of the same person. If an algorithm

reports a distance measure, then a smaller distance measure

implies greater likelihood that the face images are of the

same person. A distance measure is converted to a similarity

score by multiplying by minus one. The set of all similarity

scores between a target and a query set is called a similarity

matrix. A pair of face images of the same person is called a

match pair; and a pair of face images of different people

is called a non-match pair. From the similarity matrix,

receiver operating characteristics (ROC) and other measures

of performance can be computed.

To construct the GBU Challenge Problem we sought to

specify target and query sets for each of the three partitions

such that recognition difficulty would vary markedly while at

the same time factors such as the individual people involved

or number of images per person remained the same. To gauge

the relative difficulty associated with recognizing a pair of

images, similarity scores were created by fusing scores from

three of the top performing algorithms in the FRVT 2006;

this fusion process is described more fully in the next section.

The following constraints were imposed when selecting

the GBU partitions:

Distinct Images: An image can only be in one

target or query set.

Balanced subject counts: The number of images

per person are the same in all target

and query sets.

Different days: The images in all match pairs were

taken on different days.

After applying these constraints, and given the total number

of images available, the number of images per person in the

target and query sets was selected to fall between 1 and 4.

This number depended upon the total availability of images

for each person.

The selection criteria for the partition results in the following properties. An image is only in one partition. There are

the same number of match face pairs in each partition and the

same number of non-match pairs between any two subjects.

This implies that any difference in performance between the

partitions is not a result of different people. The difference

in performance is a result of the different conditions under

which the images were acquired. Figures 1, 2, and 3, are

examples of matching face pairs from each of the partitions.

The images included in the GBU target and query sets

were decided independently for each person. For each subject

i, a subject-specific similarity matrix Siis extracted from a

larger matrix containing similarity scores from the FRVT

2006 fusion algorithm. Each subject-specific matrix contains

all similarity scores between pairs of images of subject i. For

the Good partition, a greedy selection algorithm iteratively

added match face pairs for subject i that maximized the

average similarity score for subject i; for the Ugly partition,

match face pairs were selected to minimize the average

similarity score for subject i; and for the Bad partition, face

pairs for subject i were selected to maintain an approximately

average similarity score. The selection process for each

subject was repeated until the desired number of images were

selected for that subject. Since the images for each subject

are selected independently, the similarity score associated

with a good face pair can vary from subject to subject

(similarly for the Bad and Ugly partitions).

Each of the GBU target and query sets contains 1,085

images for 437 distinct people. The distribution of image

counts per person in the target and query sets are 117 subjects

with 1 image; 122 subjects with 2 images; 68 subjects with

3 images; and 130 subjects with 4 images. In each partition

there is 3,297 match face pairs and 1,173,928 non-match face

pairs. In the GBU image set 58% of the subjects were male

and 42% female; and 69% of the subjects were Caucasian,

22% east Asian, 4% Hispanic, and the remaining 5% other

groups; and 94% of the subjects were between 18 and 30

years old with the remaining 6% over 30 years old. For the

images in the GBU, the average distance between the centers

of the eyes is 175 pixels with a standard deviation of 36

pixels.

III. THE FRVT 2006 FUSION PERFORMANCE

Performance results for the GBU Challenge Problem are

reported for the GBU FRVT 2006 fusion algorithm, which is

a fusion of three of the top performers in the FRVT 2006. The

algorithms were fused in a two-step process. In the first step,

for each algorithm, the median and the median absolute deviation (MAD) were estimated from every 1 in 1023 similarity

scores (mediank and MADk are the median and MAD for

algorithm k). The median and MAD were estimated from 1

in 1023 similarity scores to avoid over tuning the estimates to
(a) (b) (c)

Fig. 1. Examples of face pairs of the same person from each of the partitions: (a) good, (b) challenging, and (c) very challenging.

(a) (b) (c)

Fig. 2. Examples of face pairs of the same person from each of the partitions: (a) good, (b) challenging, and (c) very challenging.
(a) (b) (c)

Fig. 3. Examples of face pairs of the same person from each of the partitions: (a) good, (b) challenging, and (c) very challenging.

the data. The similarity scores were selected to evenly sample

the images in the experiment. The fused similarity scores are

the sum of the individual algorithm similarity scores after the

median has been subtracted and then divided by the MAD.

If sk is a similarity score for algorithm k and sf is a fusion

similarity score, then sf =

P

k

(sk − mediank)/MADk .

Figure 4 reports performance of the fusion algorithm on

each of the partitions. Figure 5 shows the distribution of

the match and non-matches for the fusion algorithm on all

three partitions. The non-match distribution is stable across

all three partitions. The match distribution shifts for each

partition. The Good partition shows the greatest difference

between the median of the match and non-match distributions

and the least difference for the Ugly partition.

IV. PROTOCOL

The protocol for the GBU Challenge Problem is oneto-one matching with training, model selection, and tuning

completed prior to computing performance on the partitions.

Consequently, under this protocol, the similarity score s(t, q)

between a target image t and a query image q does not in

any way depend on the other images in the target and query

sets. Avoiding hidden interactions between images, other

than the two being compared at the moment, provides the

clearest picture of how algorithms perform. More formally,

any approach that redefines similarity s(t, q; T ) such that

it depends upon the target (or query) image set T is NOT

allowed in the GBU Challenge Problem.

To maintain separation of training and test sets, an algorithm cannot be trained on images of any of the subjects

in the GBU Challenge Problem. It is important to note that

there are images of the subjects in the GBU problem that are

in the FRGC and the MBGC data sets. These images must

be excluded from model selection, training, or tuning of an

algorithm.

We illustrate acceptable and unacceptable training protocols with three examples. The first example is training of a

principal components analysis (PCA) based face-recognition

algorithm. In a PCA-based algorithm, PCA is performed

on a training set to produce a set of Eigenfaces. A face is

represented by projecting a face image on the set of Eigenfaces. To meet the training requirements of the protocol,

images of subjects in the GBU must be excluded from the

PCA decomposition that produces a set of Eigenfaces. The

benchmark algorithm in Section V includes a training set

that satisfies the training protocol.

A second example is taken from a common training

procedure for linear discriminant analysis (LDA) in which

the algorithm is trained on the images in a target set. Training

an algorithm on a GBU target set the GBU protocol. Generally, it is well known that the performance of algorithms

can improve with such training, but the resulting levels of

performance typically do not generalize. For example, we’ve

conducted experiments with an LDA algorithm trained on

the GBU target images and performance improved over the

baseline algorithm presented, see Section V. However, when

we trained our LDA algorithm following the GBU protocol,

performance did not match the LDA algorithm trained on a

GBU target set.

The GBU protocol does permit image specific representations as long as the representation does not depend on other
0.1 0.0 0.1 0.2 0.3

More Less

Match Nonmatch

Simila

rity

Good

0.1 0.0 0.1 0.2 0.3

Match Nonmatch

Bad

0.1 0.0 0.1 0.2 0.3

Match Nonmatch

Ugly

Fig. 5. Histogram of the match and non-match distributions for the Good, the Bad, & the Ugly partitions. The green bars represent the match distribution

and the yellow bars represent the non-match distribution. The horizontal axes indicate relative frequency of similarity scores. 0.0 0.2 0.4 0.6 0.8 1.0

False accept rate

Verification rate

0.001 0.01 0.1 1.0

Good

Bad

Ugly

● 0.15

●

0.80

●

0.98

Fig. 4. ROC for the Fusion algorithm on the Good, the Bad, & the Ugly

partitions. The verification rate for each partition at a FAR of 0.001 is

highlighted by the vertical line at FAR=0.001.

images of other subjects in the GBU Challenge Problem.

An example is an algorithm based on person-specific PCA

representations. In this example, during the geometric normalization process, 20 slightly different normalized versions

of the original face would be created. A person-specific PCA

representation is generated from the set of 20 normalized

face images. This method conforms with the GBU training

protocol because the 20 face images and the person specific

PCA representation are functions of the original single face

image. When there are multiple images of a person in a target

or query set, this approach will generate multiple imagespecific representations. This training procedure does not

introduce any dependence upon other images in the target

set and consequently is permitted by the GBU protocol.

V. BASELINE ALGORITHM

The GBU Challenge Problem includes a baseline face

recognition algorithm as an entry point for researchers. The

baseline serves two purposes. First, it provides a working

example of how to carry out the GBU experiments following

the protocol. This includes training, testing and evaluation

using ROC analysis. Second, it provides a performance standard for algorithms applied to the GBU Challenge Problem.

The architecture of the baseline algorithm is a refined implementation of the standard PCA-based face recognition algorithm, also known as Eigenfaces [7][8]. These refinements

considerably improve performance over a standard PCAbased implementation. The refinements include representing

a face by local regions, a self quotient normalization step,

and weighting eigenfeatures based on Fischer’s criterion. We

refer to the GBU baseline algorithm as local region PCA

(LRPCA).

It may come as a surprise to many in the face recognition

community that a PCA-based algorithm was selected for the

GBU benchmark algorithm. However, when developing the

LRPCA baseline algorithm, we explored numerous standard

alternatives, including LDA-based algorithms and algorithms

combining Gabor based features with kernel methods and

support vector machines. For performance across the full

range of the GBU Challenge Problem, our experiments

with alternative architectures never resulted in overall performance better than the GBU baseline algorithm.

A. A Step-by-step Algorithm Description

The algorithm’s first step is to extract a cropped and

geometrically-normalized face region from an original face

image. The original image is assumed to be a still image and

the pose of the face is close to frontal. The face region in the

original is scaled, rotated, and cropped to a specified size and

the centers of the eyes are horizontally aligned and placed on

standard pixel locations. In the baseline algorithm, the face

chip is 128 by 128 pixels with the centers of the eyes spaced

64 pixels apart. The baseline algorithm runs in two modes:

partially and fully automatic. In the partially automatic mode
Fig. 6. This figure shows a cropped face and the thirteen local regions. The

crop face has been geometrically normalized and the self quotient procedure

performed.

Fig. 7. This figure illustrates the computation of a self-quotient face image.

The face image to the left is a cropped and geometrically normalized image.

The image in the middle is the geometrically normalized image blurred by a

Gaussian kernel. The image on the left is a self-quotient image. This image

is obtained by pixel-wise division of the normalized image by the blurred

image.

the coordinates of the centers of the eyes are provided; in

the fully automatic mode, the centers of the eyes are located

by the baseline algorithm.

In the LRPCA algorithm, the PCA representation is based

on thirteen local regions and the complete face chip. The

thirteen local regions are cropped out of a normalized face

image. Some of the local regions overlap, see Figure 6. The

local regions are centered relative to the average location of

the eyes, eyebrows, nose and mouth.

The next step normalizes the 14 face regions to attenuate

variation in illumination. First, self quotient normalization is

independently applied to each of the 14 regions [9]. The self

quotient normalization procedure first smoothes each region

by convolving it with a two-dimensional Gaussian kernel and

then divides the original region by the smoothed region, see

Figure 7. In the final normalization step, the pixel values in

each region are further adjusted to have a sample mean of

zero and a sample standard deviation of one.

During training, 14 distinct PCA subspaces are constructed, one for each of the face regions. From each PCA decomposition, the 3rd through 252th eigenvectors are retained

to represent the face. The decision to use these eigenvectors

was based upon experiments on images similar to the images 0.0 0.2 0.4 0.6 0.8 1.0

False accept rate

Verification rate

0.001 0.01 0.1 1.0

Good

Bad

Ugly

● 0.07

● 0.24

● 0.64

Fig. 8. ROC for the LRPCA baseline algorithm on the GBU partitions.

The verification rate for each partition at a FAR of 0.001 is highlighted by

the vertical line at FAR=0.001.

in the GBU Challenge Problem. A region in a face is encoded

by the 250 coefficients computed by projecting the region

onto the region’s 250 eigenvectors. A face is encoded by

concatenating the the 250 coefficients for each of the 14

regions into a new vector of length 3500.

Each dimension in the PCA subspace is further scaled.

First, the representation is whitened by scaling each dimension to have a sample standard deviation of one on the

training set. Next, the weight on each dimension is further

adjusted based on Fisher’s criterion. This weight is computed

based on the images in the training set. The Fisher’s criterion

weight emphasizes the dimensions along which images of

different people are spread apart. The weight attenuates

the dimensions along which the average distance between

images of the same person and images of different people

are roughly the same.

When used for recognition, i.e. during testing, images are

first processed as described above and then projected into

the 14 distinct PCA subspaces associated with each of the

14 regions. The coordinates of images projected into these

spaces, 250 for each of the 14 regions, are then concatenated

into a single feature vector representing the appearance

of that face. This produces one vector per face image;

each vector contains 3,500 values. The baseline algorithm

measures similarity between pairs of faces by computing

the Pearson’s correlation coefficient between pairs of these

vectors. The performance of the baseline algorithm on the

GBU Challenge Problem is summarized in Figure 8. A

comparison of performance of the Fusion and the LRPCAbaseline algorithm is given in Table I.

A recent area of interest in face recognition and bio-
metrics is recognition from the ocular region of the face.

There is interest in recognition from both near infrared and

visible imagery. The region-based design of the LRPCA

algorithm allows for baselining ocular performance on the

GBU partitions. Baseline performance for the left ocular is

computed from three of the 14 regions. The regions are

the left eye and two left eye brow regions. For the right

ocular region, performance is computed from the right eye

and two right eye brow regions. The left eye (resp. right

eye) are with respective to the subject; e.g., the left ocular

region corresponds to a subject left eye. Performance for the

LRPCA-ocular baseline for the left and right ocular regions

is given in Figure 9.

A summary of performance of the Fusion, the LRPCAface baseline and the LRPCA-ocular baseline algorithms are

given in Table I.

TABLE I

PERFORMANCE OF THE FUSION, THE LRPCA-FACE BASELINE AND THE

LRPCA-OCULAR BASELINE ALGORITHMS. FOR THE OCULAR

BASELINE, PERFORMANCE IS GIVEN FOR BOTH THE LEFT AND THE

RIGHT OCULAR REGIONS. THE VERIFICATION RATE AT A FAR = 0.001

IS GIVEN.

LRPCA-ocular

Partition Fusion LRPCA-face left right

Good 0.98 0.64 0.47 0.46

Bad 0.80 0.24 0.16 0.17

Ugly 0.15 0.07 0.05 0.05

VI. DISCUSSION AND CONCLUSION

This paper introduces the Good, the Bad, & the Ugly

Challenge Problem. The main goal of the challenge is to

encourage the development of algorithms that are robust

to recognizing frontal faces taken outside of studio style

image collections. The three partitions in the GBU Challenge

Problem emphasize the range of performance that is possible

when comparing faces photographed under these conditions.

This structure allows for researchers to concentrate on the

“hard” aspects of the problem while not compromising

performance on the “easier” aspects.

Partitioning the challenge by levels of difficulty is the most

prominent feature of the GBU Challenge Problem design.

Another is controlling for the “recognizability” of people by

selecting images of the same 437 people for inclusion in

each of the GBU partitions. The data in the three partitions

is further balanced so as to ensure that for each person

the number of target and query images in each partition

is the same. The design of the GBU Challenge Problem

means that any difference in performance observed between

partitions cannot be attributed to differences between people

or numbers of images for individual people.

The unique design of the GBU Challenge Problem allows researchers to investigate factors that influence the

performance of algorithms. O’Toole et al. [10] looks at the

demographic effects on the nonmatch distribution. Beveridge

et al. [11] shows that the quality of face images comes

in pairs. Additional possible lines of investigation include

understanding the factors that characterize the difference in

match face pairs across the partitions. A second line of research is characterizing the recognizability of a face; e.g., the

biometric zoo. A third line of research is developing methods

for predicting performance of face recognition algorithms.

The design of the GBU Challenge Problem encourages both

the development of algorithms, and the investigation of

methods for understanding algorithm performance.

ACKNOWLEDGMENTS

The authors wish to thank the Federal Bureau of Investigation (FBI) and the Technical Support Working Group

(TSWG) for their support of this work. The identification

of any commercial product or trade name does not imply

endorsement or recommendation by the Colorado State University, the National Institute of Standards and Technology

or the University of Texas at Dallas. The authors thank Jay

Scallan for his assistance in preparing the GBU challenge

problem.

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[11] J. R. Beveridge, P. J. Phillips, G. H. Givens, B. A. Draper, M. N. Teli,

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Proceedings, Ninth International Conference on Automatic Face and

Gesture Recognition, 2011.
0.0 0.2 0.4 0.6 0.8 1.0

False accept rate

Verification rate

0.001 0.01 0.1 1.0

Good

Bad

Ugly

● 0.05

● 0.16

● 0.47

0.0 0.2 0.4 0.6 0.8 1.0

False accept rate

Verification rate

0.001 0.01 0.1 1.0

Good

Bad

Ugly

● 0.05

● 0.17

● 0.46

(a) (b)

Fig. 9. ROC for the LRPCA-ocular baseline algorithm on the Good, the Bad, & the Ugly partitions. In (a) performance is for the left ocular region that

consists of the left eye and two left eye-brow regions; performance in (b) is for corresponding right ocular regions. The verification rate for each partition

at a FAR of 0.001 is highlighted by the vertical lines at FAR=0.001.