# Formal Implementation of a Performance Evaluation Model for the Face Recognition System

Authors: Yong-Nyuo Shin; Jason Kim; Yong-Jun Lee; Woochang Shin; Jin-Young Choi
Journal: Journal of Biomedicine and Biotechnology
 (2008)

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## Abstract

Due to usability features, practical applications, and its lack of intrusiveness, face recognition technology, based on information, derived from individuals' facial features, has been attracting considerable attention recently. Reported recognition rates of commercialized face recognition systems cannot be admitted as official recognition rates, as they are based on assumptions that are beneficial to the specific system and face database. Therefore, performance evaluation methods and tools are necessary to objectively measure the accuracy and performance of any face recognition system. In this paper, we propose and formalize a performance evaluation model for the biometric recognition system, implementing an evaluation tool for face recognition systems based on the proposed model. Furthermore, we performed evaluations objectively by providing guidelines for the design and implementation of a performance evaluation system, formalizing the performance test process.


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## Body

## 1. Introduction

Face recognition systems provide the benefit
of collecting a large amount of biometric information in a relatively easy and
cost-effective manner,  because they do not require subjects to bring any part
of their body in contact with the recognition
device intentionally,  which results in fewer repercussions and less inconvenience
when collecting the biometric information. An additional advantage exists, that is, the widely
deployed image acquisition equipment can be used without modification. In
particular, various face recognition algorithms and commercial systems have
been developed and proposed, and the marketability of face recognition systems
has increased, with many immigration-related facilities such as air and sea
ports in many countries anxious to introduce face recognition systems after the
9.11 terror attacks in the US. These benefits, and the perceived necessity of
increased security, have led to a rising social demand for face recognition
systems; and certified performance evaluation has become important as a means
of evaluating these face recognition systems.This paper proposes a performance evaluation model (PEM) to evaluate the performance of
biometric recognition systems; and designs and implements a performance
evaluation tool that enables comparison and evaluation of face recognition
systems, based on the proposed PEM. The PEM is designed to be compatible with
related international standards, and contributes to the consistency and enhanced
reliability of the performance evaluation tool that is developed with reference
to the model.Section2
outlines existing studies related to performance evaluation of face recognition
systems. Section 3
proposes a PEM to evaluate the performance of face recognition systems. Section 4 describes the
design and implementation of the performance evaluation tool, based on the
proposed PEM. Section 5 compares the performance evaluation method, utilizing
the performance evaluation tool proposed in this paper, with existing
performance evaluation programs. The last section provides a conclusion, and
suggests some remarks for future research.


## 2. Related Studies

The representative certified performance evaluation programs
for facial recognition systems are FacE Recognition Technology (FERET) and Face
Recognition Vendor Testing (FRVT). As has been used since 1993, FERET includes not only performance
evaluation of facial recognition systems but also the development of algorithms
and the collection of a face recognition database. Headed by the U.S.
Department of Defense (USDoD), FERET
is an evaluation tool that has been systematically executed from 1993 through
1997 by testing changes in the environment (e.g., size, posture, background,
etc.), differences in the time when pictures are taken, and the performance of
algorithms in processing a mass-volume database. In particular, the FERET performance evaluations have been
general evaluations designed to measure algorithms at the level of research
centers. The major purpose of the FERET performance evaluation tool has been to
implement adaptations to the latest facial recognition technologies and their
flexibility. Therefore, the FERET test is neither used to clearly measure the influence
of algorithms on the performance of individual components nor to assess
performance in fully organized scientific manners under all operating
conditions of a system [1–3].Face
Recognition Vendor Testing (FRVT) was a performance test for the face
recognition system that was implemented using three Face Recognition Technology
(FERET) performance evaluations (1994, 1995, and 1996). The FERET program
introduced the evaluation technique in the face recognition area, and developed
the face recognition area at the earliest level (system prototype development).
However, as face recognition technology matured from the prototype level to the
commercial system level, FRVT 2000 measured the performance of these commercial
systems, and evaluated how far the technology had evolved through comparison
with the last FERET evaluation. The public began to pay more attention to face
recognition technology in 2002. As a consequence, FRVT 2002 measured the degree
of technical development since 2000, evaluated the large-size databases that
were in use, and introduced new experiments to better understand the
performance of face recognition. Size, difficulty, and complexity of this
performance evaluation were on the rise as the evaluation theory as well as the
face recognition technology grew. For example, FERET SEP96 performed just 14.5
million comparisons over a period of 72 hours, while FRVT 2000 carried out 192
million comparisons in 72 hours. In contrast, FRVT 2002 introduced an
evaluation that made 15 billion comparisons in 264 hours [4–6].Certified
performance evaluation programs like FERET and FRVT were designed to measure
the algorithm accuracy of face recognition systems. For these projects, a
common face image database was provided for the test, face recognition was
performed for a certain period of time according to the respective method, and
the results were evaluated. However, this method provides evaluation only for
face recognition technology vendors that participated in the program during the
evaluation period. In particular, database items were limited to image size,
target posture, image acquisition environment, and time, which left the problem
that various conditions of algorithm evaluation were not satisfied dynamically.
Moreover, the algorithm evaluation environment was commissioned to each of the
face recognition system developers, creating the problem of inconsistency in
establishing a performance evaluation system environment. Furthermore,
additional tasks were required in order to determine the accuracy of each
algorithm, and to analyze the algorithm implementation result again. Therefore,
it is necessary to design an algorithm evaluation method that can resolve these
problems, to build a standardized evaluation environment, and to automatically
figure out the evaluation result of the algorithm whose performance is measured
in this environment.


### 2.1. Factors Affecting Performance Evaluation

Results from the performance evaluation of facial
recognition systems change in accordance with varying factors, such as
lighting, posture, facial expression, and elapsed time. The JTC 1/SC
37/WG 5 International Standard [7] classifies those factors affecting the performance of biometric systems.As outlined in Table1, there are a number of factors
affecting the performance of facial recognition systems, and such factors must
be prudently taken into consideration when the probe and gallery are selected
during the performance evaluation of these systems. Algorithm performance evaluation of biometric recognition technology is
conducted in such a way that the standard gallery is trained or registered, and
is then compared with the test biometric information (probe) to be recognized, after
which the similarities between the two sets of information are measured.Table 1
Classification of factors affecting the performance of biometric system.
ClassificationParticular factorsPopulation demographicsAge, ethnic origin, gender,
occupation, etc.ApplicationTemplate aging, shooting time,
User friendliness, user motivation, etc.User physiologyHair, baldness, illnesses and diseases,
eyelashes, nail length, skin tone, etc.User behaviorFacial expression, dialect, motion,
posture, angle, distance, stress, nervousness, etc.Environmental influencesBackground features (color, shadow,
sound, etc.), lighting strength and direction, weather, (temperature,
humidity, rain, snow,
etc.)User appearanceOutfit (hat, sleeves, pants, dress shoes, etc.), band (bandage), contact lenses,
makeup, glasses, fake nails, hairstyle, rings, tattoos, etc.SensorDirt on camera lenses, focus, sensor quality,and hardwaresensor change, transmission
channel, etc.User interfaceFeedback, instructions, supervising directorGenerally, basic factors such as posture, angle, facial
expression, lighting brightness, and gender are considered in the construction
of a face image database. However, records of the face image database need to
be further subdivided in order to process the test under conditions similar to those
prevalent in the real world.The research facial database that was developed
by Korea Information Security Agency (KISA) from 2002 to 2004 was used for
performance evaluation [8]. Table 2 shows a classification of KISA’s database.Table 2
Classification of the KISA’s database.
CriterionContentGenderMale or femaleAgeAgeBirthplaceGyeonggi-do, SeoulLighting colorFluorescent lamp, Incandescent lampLighting directionFront, right/left45°, right/left 90°, right/left 135°, 180°Glasses directionFront, Right/Left45°, Right/Left 90°Facial expressionNo Expression, Smiling, Angry, Closed Eyes, SurprisedPostureFront, Right/Left15°, Right/Left 30°,Right/Left45°Hair conditionNatural Hairstyle, Hair Band/s, Glasses


## 2.1. Factors Affecting Performance Evaluation

Results from the performance evaluation of facial
recognition systems change in accordance with varying factors, such as
lighting, posture, facial expression, and elapsed time. The JTC 1/SC
37/WG 5 International Standard [7] classifies those factors affecting the performance of biometric systems.As outlined in Table1, there are a number of factors
affecting the performance of facial recognition systems, and such factors must
be prudently taken into consideration when the probe and gallery are selected
during the performance evaluation of these systems. Algorithm performance evaluation of biometric recognition technology is
conducted in such a way that the standard gallery is trained or registered, and
is then compared with the test biometric information (probe) to be recognized, after
which the similarities between the two sets of information are measured.Table 1
Classification of factors affecting the performance of biometric system.
ClassificationParticular factorsPopulation demographicsAge, ethnic origin, gender,
occupation, etc.ApplicationTemplate aging, shooting time,
User friendliness, user motivation, etc.User physiologyHair, baldness, illnesses and diseases,
eyelashes, nail length, skin tone, etc.User behaviorFacial expression, dialect, motion,
posture, angle, distance, stress, nervousness, etc.Environmental influencesBackground features (color, shadow,
sound, etc.), lighting strength and direction, weather, (temperature,
humidity, rain, snow,
etc.)User appearanceOutfit (hat, sleeves, pants, dress shoes, etc.), band (bandage), contact lenses,
makeup, glasses, fake nails, hairstyle, rings, tattoos, etc.SensorDirt on camera lenses, focus, sensor quality,and hardwaresensor change, transmission
channel, etc.User interfaceFeedback, instructions, supervising directorGenerally, basic factors such as posture, angle, facial
expression, lighting brightness, and gender are considered in the construction
of a face image database. However, records of the face image database need to
be further subdivided in order to process the test under conditions similar to those
prevalent in the real world.The research facial database that was developed
by Korea Information Security Agency (KISA) from 2002 to 2004 was used for
performance evaluation [8]. Table 2 shows a classification of KISA’s database.Table 2
Classification of the KISA’s database.
CriterionContentGenderMale or femaleAgeAgeBirthplaceGyeonggi-do, SeoulLighting colorFluorescent lamp, Incandescent lampLighting directionFront, right/left45°, right/left 90°, right/left 135°, 180°Glasses directionFront, Right/Left45°, Right/Left 90°Facial expressionNo Expression, Smiling, Angry, Closed Eyes, SurprisedPostureFront, Right/Left15°, Right/Left 30°,Right/Left45°Hair conditionNatural Hairstyle, Hair Band/s, Glasses


## 3. Performance Evaluation Model (PEM)

Many factors
must be considered when building a fair performance evaluation system for
biometric recognition systems. For example, to evaluate the performance of face
recognition systems, a database of facial information for use in face
recognition should be collected, and performance evaluation items (changes in facial
expression, lighting, etc.) as well as performance evaluation
measurement criteria such as false acceptance rate (FAR), false rejection rate (FRR),
and equal error rate (EER) should be selected. The face recognition system to
be evaluated and a standardized interface for the performance evaluation system
should be designed and international standards need to be applied at each stage
of performance evaluation, in order to enhance fairness and reliability.Thus, the performance
evaluation model (PEM) is created to analyze and arrange the criteria to be considered
in building up the performance evaluation system, and to support the
development of the performance evaluation system. The PEM presents the basic
system structure, guidelines, and development process used to build a system
for performance evaluation.The PEM
proposed in this paper is designed to (1) evaluate the performance of the
biometric recognition algorithm, and (2) to build a system that automatically evaluates
performance and outputs the results in tandem with the biometric recognition
system.


### 3.1. Structure of the PEM

The PEM structure for the system that evaluates the biometric recognition
algorithm is composed of a data preparation module, an execution model, and a
result analysis module, as shown in Figure1.Figure 1
PEM Framework.


#### 3.1.1. Data Preparation Module

The data preparation module prepares the
biometric information used for performance evaluation, for which the development
of a biometric information database and the design of the test criteria are the
major elements to consider. As the biometric information database used affects
the evaluation reliability of the performance evaluation system to a large
extent, it should be considered a priority at the initial stage of system development.
In addition, the biometric information used for performance evaluation should
never be exposed, so that evaluation reliability can be improved [9].Generally, algorithm performance evaluation of
biometric recognition technology is conducted in such a way that the standard
gallery is trained or registered, and is then compared with the test biometric
information (probe) to be recognized, after which the similarities between the
two sets of information are measured. At this time, algorithm performance
varies according to the information generation environment or conditions. For
example, if an expressionless front-view facial image is registered in the
gallery, and a smiling facial image photographed at a 15-degree angle from the
left is used as the probe, we can compare the strength of the different
algorithm technologies in terms of facial expression and angle. In this paper,
the “test criteria” refers to an item that could affect the performance
evaluation result, and the performance evaluation system developer should
design test criteria that are suitable for the objective of the evaluation. The
test criteria selected by the PEM are limited to the classification criteria of
the biometric information database.


#### 3.1.2. Execution Module

The execution module activates the biometric recognition system to be evaluated,
and executes a performance evaluation. There are two methods for establishing an
interface between the performance evaluation and the biometric recognition
system. The first one consists in developing two systems as independently
applied programs, while the second one consists in creating the biometric
recognition algorithm as a component or library and then inserting it into the
performance evaluation system. The former requires advance agreement between
two systems with regards to the input/output file format, since the input data
used for performance evaluation and the performance evaluation execution result
data are generally transferred in a predefined form (generally, XML). Even
though FRVT 2006 did not use the performance evaluation tool, participating
companies submitted the biometric recognition system as an execution file, and
the name of the input file used for evaluation and the output file that records
the evaluation result were transferred as the program argument. For the
component (or library) method, the standardized interface should be agreed upon
in advance. The agreed interface should be as simple as possible, and
compatibility with international standards is desirable. The related
international standards include biometric application programming interface (BioAPI) 1.1 [10] and BioAPI 2.0.


#### 3.1.3. Result Analysis Module

The result analysis module performs a final
analysis of recognition algorithm performance, using the result value obtained
from the execution module. The performance of the specific algorithm can be
expressed using several measurement criteria, and the appropriate measurement
factor is decided upon depending on the objectives of the performance
evaluation. Measurement factors can be broadly grouped into error rates and
throughput rates. Error rates are basically derived from matching errors and
sample acquisition errors, and the focus is on whether the algorithm is working
properly and accurately. The throughput rate shows the number of users that the
face recognition system can process in a given unit time. This throughput rate
has significant meaning when performing the verification in a large image
database [7].


### 3.2. Formalization of PEM

As biometric products
are being used in establishing national infrastructure, a need for more
effective and objective biometric performance test is on the increase.
As examples are being
announced such as FRVT, however, no objective and proved methodology is
reported yet. In this paper, by presenting and formalizing biometric system
performance test models, firstly, securing efficiency by eliminating unnecessary
processes or factors that can occur when evaluating the performance of a biometric
recognition system, and secondly, guaranteeing objectivity may be accomplished
by generating credible test factors and processes for the performance test,
which is completely  dependent on
heuristic methodology.The performance test
was executed by elevating the usability of presented models in this paper, and by
formulating model-based tools for validation.PEM is defined as a
structureΓ=(DB,ATTR,INT,MET) with the following meanings.
(a)
DB is a set of all images of the test database.(b)
ATTR is a set of pair〈pattr,gattr〉 representing
classification factors for probe and gallery set.
(i)
pattr,gattr⊆fact  where fact is a set of factors influencing
performance.(ii)
fact={age, sex, background,
expression, pose, illumination,…}.(c)
INT is an interface of
biometric recognition system being tested.(d)
MET is a set of performance metrics.
DB represents all
facial images in the image database to be used for the purpose of face
recognition performance evaluation. ATTR represents the
factors that affect the performance test. The factors include age, sex, photographing time, expression,  background,
posture, lighting, and costume;
and these factors are used when selecting probe and gallery image set. Probe
image set is PSET=σpattr(DB), and gallery image set is GSET=σgattr(DB). σcondition is a function selecting elements that satisfy
the condition.  For example, in case of ATTR={〈{Man,ExprSmile},{Front,Normal}〉} to perform the performance test for laughing men’s faces, the
probe image set used in the performance test is σMan,ExprSmile(DB).
Gallery image set is σFront,Normal(DB).When executing the performance test, all images of  GSET will be matched to
each image of PSET. That is, all image matching set MSET is a Cartesian product of PSET and GSET.(a)MSET={(x,y)∣x∈PSETandy∈GSET}
INT is the interface of face recognition module,
an object of the performance test. Through this interface, performance-testing
tools call the function of face recognition module. The interface should be as
simple as possible, and it is desirable to be compatible with the international
standards. INT should include the matching function Fmatching, and
this function outputs the matching results (accept or reject) by accepting one  factor of image matching set.(b)∃Fmatching∈INT, such that Fmatching(a) returns “accept” or “reject,” where  a∈MSET
The execution result of
face recognition function for the performance evaluation is expressed as the
results from callingFmatching function using
all element of MSET as factors, and this can be expressed as a two dimensional
matrix, RMATRIX. That is, RMATRIXi,j element value
is Fmatching⁢(PSETi,GSETj). When the result value is
accept, the outcome is 1, when
the resulting value is reject, it is 0. For example, PSET={p1,p2,p3}, and GSET={g1,g2,g3},  MSET  becomes {〈p1,g1〉,〈p1,g2〉,〈p1,g3〉,〈p2,g1〉,〈p2,g2〉,〈p2,g3〉,〈p3,g1〉,〈p3,g2〉,〈p3,g3〉}. If the resulting values of applying each element of MSET to Fmatching is 1, 0, 0, 0, 1, 1, 0, 0, 1, consecutively,
this can be expressed
as the following 2-dimensional
matrix:
(1)RMATRIX=p1p2p3[100011001]g1g2g3.
MET is a set of performance test measures such as fail-to-enroll
rate (FTER), fail-to-acquire rate (FTAR), and false nonmatch rate (FNMR), false
match rate (FMT). Such matching error-related metrics as FNMR, FMT can be
calculated using RMATRIX:
(2)FNMR=∑i=1n∑j=1m(Same(PSETi,GSETj)⁢∧(RMATRIXi,j=0))∑i=1n∑j=1mSame(PSETi,GSETj),FMR=∑i=1n∑j=1m(Diff(PSETi,GSETj)∧RMATRIXi,j)∑i=1n∑j=1mDiff(PSETi,GSETj),
where the following hold:
(i)
n is the size of PSET,(ii)
m is the size of GSET,(iii)
Same(a,b) is 1, if a and b are images of same person, otherwise Same(a,b) is 0,(iv)
Diff(a,b) is 0 if a and b
are images of same person, otherwise, Diff(a,b) is 1,(v)
a∧b is 1, if a and b are 1, otherwise it is 0,(vi)
a=b is 1, if values of a and b are same, otherwise it is 0.


### 3.3. Evaluation System Development Process

The following section describes how to build a performance evaluation system according to the PEM.(1)
Describe the objectives of developing and evaluating a performance evaluation system.(2)
Develop or select the biometric information database that will be used for performance evaluation.(3)
Design test criteria that fit into the evaluation objectives.(4)
Determine the type of interface to be used between the performance evaluation system and the biometric recognition system. If it runs as a standalone program, select the input/output file format; or, if it is linked by the component method, design the component interface.(5)
Select the measurement criteria that fit into the evaluation objectives.(6)
Implement the “data preparation module” which reads the biometric information according to the test criteria through the interface with the biometric information database.(7)
Implement the “execution
module” which executes the recognition algorithm with the gallery/probe
biometric information provided by the data preparation module. The execution
result (degree of similarity) should be saved in the database containing the
results of performance evaluation.(8)
Calculate the value of the measurement
criteria by analyzing the similarity saved in the performance evaluation result
database, and implement the “result analysis module” to generate a report on
the results of the performance evaluation.The test items and measurement criteria can be
decided when performing the actual performance evaluation instead of building
the performance evaluation system. In this case, select test items and
measurement criteria that can be selected at steps 3 and 5 as described above,
the test should be able to select the necessary items from among these when
running the performance evaluation tool.


## 3.1. Structure of the PEM

The PEM structure for the system that evaluates the biometric recognition
algorithm is composed of a data preparation module, an execution model, and a
result analysis module, as shown in Figure1.Figure 1
PEM Framework.


### 3.1.1. Data Preparation Module

The data preparation module prepares the
biometric information used for performance evaluation, for which the development
of a biometric information database and the design of the test criteria are the
major elements to consider. As the biometric information database used affects
the evaluation reliability of the performance evaluation system to a large
extent, it should be considered a priority at the initial stage of system development.
In addition, the biometric information used for performance evaluation should
never be exposed, so that evaluation reliability can be improved [9].Generally, algorithm performance evaluation of
biometric recognition technology is conducted in such a way that the standard
gallery is trained or registered, and is then compared with the test biometric
information (probe) to be recognized, after which the similarities between the
two sets of information are measured. At this time, algorithm performance
varies according to the information generation environment or conditions. For
example, if an expressionless front-view facial image is registered in the
gallery, and a smiling facial image photographed at a 15-degree angle from the
left is used as the probe, we can compare the strength of the different
algorithm technologies in terms of facial expression and angle. In this paper,
the “test criteria” refers to an item that could affect the performance
evaluation result, and the performance evaluation system developer should
design test criteria that are suitable for the objective of the evaluation. The
test criteria selected by the PEM are limited to the classification criteria of
the biometric information database.


### 3.1.2. Execution Module

The execution module activates the biometric recognition system to be evaluated,
and executes a performance evaluation. There are two methods for establishing an
interface between the performance evaluation and the biometric recognition
system. The first one consists in developing two systems as independently
applied programs, while the second one consists in creating the biometric
recognition algorithm as a component or library and then inserting it into the
performance evaluation system. The former requires advance agreement between
two systems with regards to the input/output file format, since the input data
used for performance evaluation and the performance evaluation execution result
data are generally transferred in a predefined form (generally, XML). Even
though FRVT 2006 did not use the performance evaluation tool, participating
companies submitted the biometric recognition system as an execution file, and
the name of the input file used for evaluation and the output file that records
the evaluation result were transferred as the program argument. For the
component (or library) method, the standardized interface should be agreed upon
in advance. The agreed interface should be as simple as possible, and
compatibility with international standards is desirable. The related
international standards include biometric application programming interface (BioAPI) 1.1 [10] and BioAPI 2.0.


### 3.1.3. Result Analysis Module

The result analysis module performs a final
analysis of recognition algorithm performance, using the result value obtained
from the execution module. The performance of the specific algorithm can be
expressed using several measurement criteria, and the appropriate measurement
factor is decided upon depending on the objectives of the performance
evaluation. Measurement factors can be broadly grouped into error rates and
throughput rates. Error rates are basically derived from matching errors and
sample acquisition errors, and the focus is on whether the algorithm is working
properly and accurately. The throughput rate shows the number of users that the
face recognition system can process in a given unit time. This throughput rate
has significant meaning when performing the verification in a large image
database [7].


## 3.1.1. Data Preparation Module

The data preparation module prepares the
biometric information used for performance evaluation, for which the development
of a biometric information database and the design of the test criteria are the
major elements to consider. As the biometric information database used affects
the evaluation reliability of the performance evaluation system to a large
extent, it should be considered a priority at the initial stage of system development.
In addition, the biometric information used for performance evaluation should
never be exposed, so that evaluation reliability can be improved [9].Generally, algorithm performance evaluation of
biometric recognition technology is conducted in such a way that the standard
gallery is trained or registered, and is then compared with the test biometric
information (probe) to be recognized, after which the similarities between the
two sets of information are measured. At this time, algorithm performance
varies according to the information generation environment or conditions. For
example, if an expressionless front-view facial image is registered in the
gallery, and a smiling facial image photographed at a 15-degree angle from the
left is used as the probe, we can compare the strength of the different
algorithm technologies in terms of facial expression and angle. In this paper,
the “test criteria” refers to an item that could affect the performance
evaluation result, and the performance evaluation system developer should
design test criteria that are suitable for the objective of the evaluation. The
test criteria selected by the PEM are limited to the classification criteria of
the biometric information database.


## 3.1.2. Execution Module

The execution module activates the biometric recognition system to be evaluated,
and executes a performance evaluation. There are two methods for establishing an
interface between the performance evaluation and the biometric recognition
system. The first one consists in developing two systems as independently
applied programs, while the second one consists in creating the biometric
recognition algorithm as a component or library and then inserting it into the
performance evaluation system. The former requires advance agreement between
two systems with regards to the input/output file format, since the input data
used for performance evaluation and the performance evaluation execution result
data are generally transferred in a predefined form (generally, XML). Even
though FRVT 2006 did not use the performance evaluation tool, participating
companies submitted the biometric recognition system as an execution file, and
the name of the input file used for evaluation and the output file that records
the evaluation result were transferred as the program argument. For the
component (or library) method, the standardized interface should be agreed upon
in advance. The agreed interface should be as simple as possible, and
compatibility with international standards is desirable. The related
international standards include biometric application programming interface (BioAPI) 1.1 [10] and BioAPI 2.0.


## 3.1.3. Result Analysis Module

The result analysis module performs a final
analysis of recognition algorithm performance, using the result value obtained
from the execution module. The performance of the specific algorithm can be
expressed using several measurement criteria, and the appropriate measurement
factor is decided upon depending on the objectives of the performance
evaluation. Measurement factors can be broadly grouped into error rates and
throughput rates. Error rates are basically derived from matching errors and
sample acquisition errors, and the focus is on whether the algorithm is working
properly and accurately. The throughput rate shows the number of users that the
face recognition system can process in a given unit time. This throughput rate
has significant meaning when performing the verification in a large image
database [7].


## 3.2. Formalization of PEM

As biometric products
are being used in establishing national infrastructure, a need for more
effective and objective biometric performance test is on the increase.
As examples are being
announced such as FRVT, however, no objective and proved methodology is
reported yet. In this paper, by presenting and formalizing biometric system
performance test models, firstly, securing efficiency by eliminating unnecessary
processes or factors that can occur when evaluating the performance of a biometric
recognition system, and secondly, guaranteeing objectivity may be accomplished
by generating credible test factors and processes for the performance test,
which is completely  dependent on
heuristic methodology.The performance test
was executed by elevating the usability of presented models in this paper, and by
formulating model-based tools for validation.PEM is defined as a
structureΓ=(DB,ATTR,INT,MET) with the following meanings.
(a)
DB is a set of all images of the test database.(b)
ATTR is a set of pair〈pattr,gattr〉 representing
classification factors for probe and gallery set.
(i)
pattr,gattr⊆fact  where fact is a set of factors influencing
performance.(ii)
fact={age, sex, background,
expression, pose, illumination,…}.(c)
INT is an interface of
biometric recognition system being tested.(d)
MET is a set of performance metrics.
DB represents all
facial images in the image database to be used for the purpose of face
recognition performance evaluation. ATTR represents the
factors that affect the performance test. The factors include age, sex, photographing time, expression,  background,
posture, lighting, and costume;
and these factors are used when selecting probe and gallery image set. Probe
image set is PSET=σpattr(DB), and gallery image set is GSET=σgattr(DB). σcondition is a function selecting elements that satisfy
the condition.  For example, in case of ATTR={〈{Man,ExprSmile},{Front,Normal}〉} to perform the performance test for laughing men’s faces, the
probe image set used in the performance test is σMan,ExprSmile(DB).
Gallery image set is σFront,Normal(DB).When executing the performance test, all images of  GSET will be matched to
each image of PSET. That is, all image matching set MSET is a Cartesian product of PSET and GSET.(a)MSET={(x,y)∣x∈PSETandy∈GSET}
INT is the interface of face recognition module,
an object of the performance test. Through this interface, performance-testing
tools call the function of face recognition module. The interface should be as
simple as possible, and it is desirable to be compatible with the international
standards. INT should include the matching function Fmatching, and
this function outputs the matching results (accept or reject) by accepting one  factor of image matching set.(b)∃Fmatching∈INT, such that Fmatching(a) returns “accept” or “reject,” where  a∈MSET
The execution result of
face recognition function for the performance evaluation is expressed as the
results from callingFmatching function using
all element of MSET as factors, and this can be expressed as a two dimensional
matrix, RMATRIX. That is, RMATRIXi,j element value
is Fmatching⁢(PSETi,GSETj). When the result value is
accept, the outcome is 1, when
the resulting value is reject, it is 0. For example, PSET={p1,p2,p3}, and GSET={g1,g2,g3},  MSET  becomes {〈p1,g1〉,〈p1,g2〉,〈p1,g3〉,〈p2,g1〉,〈p2,g2〉,〈p2,g3〉,〈p3,g1〉,〈p3,g2〉,〈p3,g3〉}. If the resulting values of applying each element of MSET to Fmatching is 1, 0, 0, 0, 1, 1, 0, 0, 1, consecutively,
this can be expressed
as the following 2-dimensional
matrix:
(1)RMATRIX=p1p2p3[100011001]g1g2g3.
MET is a set of performance test measures such as fail-to-enroll
rate (FTER), fail-to-acquire rate (FTAR), and false nonmatch rate (FNMR), false
match rate (FMT). Such matching error-related metrics as FNMR, FMT can be
calculated using RMATRIX:
(2)FNMR=∑i=1n∑j=1m(Same(PSETi,GSETj)⁢∧(RMATRIXi,j=0))∑i=1n∑j=1mSame(PSETi,GSETj),FMR=∑i=1n∑j=1m(Diff(PSETi,GSETj)∧RMATRIXi,j)∑i=1n∑j=1mDiff(PSETi,GSETj),
where the following hold:
(i)
n is the size of PSET,(ii)
m is the size of GSET,(iii)
Same(a,b) is 1, if a and b are images of same person, otherwise Same(a,b) is 0,(iv)
Diff(a,b) is 0 if a and b
are images of same person, otherwise, Diff(a,b) is 1,(v)
a∧b is 1, if a and b are 1, otherwise it is 0,(vi)
a=b is 1, if values of a and b are same, otherwise it is 0.


## 3.3. Evaluation System Development Process

The following section describes how to build a performance evaluation system according to the PEM.(1)
Describe the objectives of developing and evaluating a performance evaluation system.(2)
Develop or select the biometric information database that will be used for performance evaluation.(3)
Design test criteria that fit into the evaluation objectives.(4)
Determine the type of interface to be used between the performance evaluation system and the biometric recognition system. If it runs as a standalone program, select the input/output file format; or, if it is linked by the component method, design the component interface.(5)
Select the measurement criteria that fit into the evaluation objectives.(6)
Implement the “data preparation module” which reads the biometric information according to the test criteria through the interface with the biometric information database.(7)
Implement the “execution
module” which executes the recognition algorithm with the gallery/probe
biometric information provided by the data preparation module. The execution
result (degree of similarity) should be saved in the database containing the
results of performance evaluation.(8)
Calculate the value of the measurement
criteria by analyzing the similarity saved in the performance evaluation result
database, and implement the “result analysis module” to generate a report on
the results of the performance evaluation.The test items and measurement criteria can be
decided when performing the actual performance evaluation instead of building
the performance evaluation system. In this case, select test items and
measurement criteria that can be selected at steps 3 and 5 as described above,
the test should be able to select the necessary items from among these when
running the performance evaluation tool.


## 4. Designing and Implementing the Performance Evaluation Tool

The performance evaluation tool was designed
and implemented using the PEM proposed by this paper. The following section
describes the contents and results by step, according to the evaluation system
development process.  The purpose of performance evaluation is to identify the
technology level of the face recognition system through objective performance
evaluation and certification, so as to encourage public trust in face
recognition products and enhance their competitiveness.


### 4.1. Test Criteria

The test criteria are designed in such way that
those do not have to be selected when developing the evaluation system, enabling
the tester to select it in the course of performing the actual performance
evaluation. Basically, the test item lists all the classification criteria so
that the tester can select from them separately, based on the condition of the
gallery image set and the probe image set. The gallery image set and the probe
image set, each of which is composed of several items, are referred to as the
“test set.” One performance evaluation project can generate several test sets,
and each test set can generate a different result report. For instance, even
though the collection of facial images to be registered in the face recognition
system is white-front (normal) or purple-front (illum. yellow), the actual
image acquired by the image acquisition device to identify the user can be
normal, eye-closed, or tilted slightly to the left or right. The test set can
be configured as in Figure2. by assuming this kind of face recognition system.Figure 2
Example of setting the facial image test set.If we assume that 10 face images exist per
category in the above example, there are 20 facial images in the gallery set,
and 40 facial images in the probe set. Therefore, the template generation
function will be invoked 20 times for the gallery image, and image processing
will be performed 40 times for the probe image, which means that 800 (20×40)
comparison operations will be executed if performance evaluation is performed
on the above test set. Among these comparison operations, image comparison will
be performed 80 times for all the persons involved, because the image for a
specific person appears twice in the gallery set and 4 times in the probe set,
which results in an image comparison being performed 8 times for the same
person, with 10 persons in total being compared. Therefore, 720 different image
comparisons are made for the different persons, based on this system. Using
this method, image comparison times can be estimated in advance, and the tester
can estimate the time required for performance evaluation in advance, because
the comparison times are calculated beforehand.


### 4.2. Selected BioAPI

The performance evaluation tool provides a
standard interface for the face recognition system, and the examinee provides
the face recognition module that satisfies this interface as the dynamic
library. The performance evaluation tool is designed to enable the tester to
change the face recognition module during the run time so that the tester can
perform evaluation by changing the face recognition module without modifying
the performance evaluation tool.BioAPI 1.1 was applied for the compatibility with
international standards, and only the minimum number of functions required for
algorithm performance evaluation was selected, in order to reduce the
examinee’s development burden. Table3 shows the selected BioAPI for the face
recognition module.Table 3
Selected BioAPI for face recognition module.
Function nameMeaningBioAPI_Initinitializes the face recognition
moduleBioAPI_Terminateterminates the face recognition
moduleBioAPI_FreeBIRHandlereleases
the BIR data memoryBioAPI_GetBIRFromHandlereturns
the BIR data from the data handleBioAPI_CreateTemplategenerates
the data template from the galleryBioAPI_Processgenerates
the BIR data by processing the probe imageBioAPI_VerifyMatchreturns
the verification result by comparing two processed images


### 4.3. Measurement Criteria

The performance evaluation metrics used by
FERET and FRVT, as well as the metrics proposed by JTC 1/SC 37/WG 5 Standard [7],
were analyzed. Among these metrics, the criteria related with the technology
evaluation of the face recognition system were chosen, as shown in Table 4.Table 4
Selected performance evaluation metric.
Metric typeFail-to-enroll rateenrollment
throughput (no. of cases/s)Fail-to-acquire rateenrollment
time (min, max, avg)False reject rate (=fta+fnmr*(1-fta))extraction
throughput (no. of cases/s)false accept rate (=fmr*(1-fta))extraction
time (min, max, avg)false nonmatch ratematching
throughput (no. of cases/s)False match ratematching
time (min, max, avg)cmc curvestransaction
throughput(no. of cases/s)roc curvestransaction
time (min, max, avg)error equal ratetemplate
size (min, max, avg)


### 4.4. Class Diagram of Metadata

Within the performance evaluation
tool implemented in this paper, individual projects created for performance
evaluation internally generate metadata in a specific structure in order to
save the settings related to performance evaluation and performance evaluation
results. The structure of these metadata is as illustrated in Figure3.Figure 3
Example of setting the facial image test set.


#### 4.4.1. CMetaProject Class

Whenever a new project is created, this class
creates an instance in connection with the project. It maintains the path used to
save the project name and the project itself, and the path to access a database
in character strings. This information is related to the project
configurations, and contains values established by the tester upon the project’s
creation. In addition, the list of face image data designed by the tester for performance
evaluation receives the lists of CMetaTestSet class. A single project may have at least one group
of face image data for its performance evaluation, and independently create a
report based on these individual evaluation results. Therefore, the information
exists in the extendible list format. Moreover, this class allows users to
ascertain the number of total images to test (compare), and that of probes and
gallery images with regards to such information.


#### 4.4.2. CMetaCategory Class

This class contains data related to the subcategories
of face images. Face images are influenced by the direction of the picture
taken, the location of lighting, and posture, and so forth. According to these
conditions, they are classified, and the tester may choose some of the
classified images by using the performance test tool as the test subject group.
The chosen information is individually saved in the Cmetacategory class. These metadata
contain variables, such as the category name of each criterion, the total number
of images in the category, and the number of images failed to enroll (for gallery
items) or acquire (for probe items).


#### 4.4.3. CMetaTestItem Class

This class contains data pertaining to one
face image. Each face image has a unique ID for the photograph target, its
location, and the face image items. Additionally, it contains Boolean variables
in order to save whether each item failed to enroll (for gallery items) and
acquire (for probe items) or not.


#### 4.4.4. CMetatTestResult Class

This
class stores the verification results of the comparison of one probe item with one
gallery item in order to determine whether or not they come from an identical
person. It contains each item’s criteria, location, and ID information, along
with variables, such as the similarity
value and the comparison time created as a result of a comparison between two
images.


#### 4.4.5. CMetaEnrolled Class

This
class saves the template data created so as to recognize a face from the image
data for the system to execute the enrollment of a gallery item. It holds not
only the item’s criteria and location information but also a binary space to
store template data, as well as data used to save the results of the template creation
and the time required to create a template.


#### 4.4.6. CMetaTestset Class

This class includes information about the face
image probe group and gallery group in order to conduct the test. One project may
have several test groups, and each test group individually saves the
performance evaluation results. Metadata of the test groups bear the following
information. Firstly, the class contains CMetaCategory which is the
information holding the face image criteria as the list information for each of
the probe and gallery groups. Namely, each of the probe and gallery groups may
include a face image group with several different criteria. In addition, the
class maintains the list of CMetaTestItem for each of the probe and gallery groups.
This is not the criteria information but the metadata with individual image
item information used for the test. It also contains the list of CMetaTestResult,
containing the test results between a single probe item and a single gallery
item.Furthermore, where a system enrolls a gallery item, this
class will contain the list of CMetaEnrolled classes in order to store template
data that are created when each face recognition module generates its own
template data. This is accomplished by using the face image data. As general data of such list data, the
class contains variables to contain the test start time, end time, number of
total probe items, number of total gallery items, number of total gallery items
that failed to enroll, and number of total probe items that the system failed
to acquire. We developed the six
major modules and metadata classes that we examined above with the program for
Windows, under Microsoft’s Visual Studio development configuration. The face
recognition modules, which are the subject of the performance evaluation, work in
connection with the performance evaluation tool in the format of a dynamic link
library.


### 4.5. Implementing Data Preparation/Execution/Result Analysis Module

The performance evaluation
tool was developed as an application program running on Windows OS, and the
face recognition module to be evaluated was implemented as a dynamic link library
(DLL). The data preparation module that has the function of connecting with the
biometric database and of setting the gallery and probe image set was
implemented for use in the performance
evaluation, as shown in Figure4.Figure 4
Selection window for face image probe set and gallery set.The face recognition module provided by the vendor was checked to verify
that it provides the functions presented in Table3, and the execution module
that performs evaluation was implemented, using the functions of the selected
face recognition module. A function that visually displays whether performance
evaluation is progressing properly or not was included in the execution module,
as shown in Figure 5.Figure 5
Progress visualization when evaluating performance.Finally, the value of evaluation criteria is
calculated by analyzing the similarity saved in the performance evaluation
result database, and the result analysis module that generates the performance
evaluation result report is implemented. This performance evaluation tool is
equipped with a function that generates the evaluation result, as well as a
function that issues the certificate for the face recognition module, depending
on the evaluation result.


## 4.1. Test Criteria

The test criteria are designed in such way that
those do not have to be selected when developing the evaluation system, enabling
the tester to select it in the course of performing the actual performance
evaluation. Basically, the test item lists all the classification criteria so
that the tester can select from them separately, based on the condition of the
gallery image set and the probe image set. The gallery image set and the probe
image set, each of which is composed of several items, are referred to as the
“test set.” One performance evaluation project can generate several test sets,
and each test set can generate a different result report. For instance, even
though the collection of facial images to be registered in the face recognition
system is white-front (normal) or purple-front (illum. yellow), the actual
image acquired by the image acquisition device to identify the user can be
normal, eye-closed, or tilted slightly to the left or right. The test set can
be configured as in Figure2. by assuming this kind of face recognition system.Figure 2
Example of setting the facial image test set.If we assume that 10 face images exist per
category in the above example, there are 20 facial images in the gallery set,
and 40 facial images in the probe set. Therefore, the template generation
function will be invoked 20 times for the gallery image, and image processing
will be performed 40 times for the probe image, which means that 800 (20×40)
comparison operations will be executed if performance evaluation is performed
on the above test set. Among these comparison operations, image comparison will
be performed 80 times for all the persons involved, because the image for a
specific person appears twice in the gallery set and 4 times in the probe set,
which results in an image comparison being performed 8 times for the same
person, with 10 persons in total being compared. Therefore, 720 different image
comparisons are made for the different persons, based on this system. Using
this method, image comparison times can be estimated in advance, and the tester
can estimate the time required for performance evaluation in advance, because
the comparison times are calculated beforehand.


## 4.2. Selected BioAPI

The performance evaluation tool provides a
standard interface for the face recognition system, and the examinee provides
the face recognition module that satisfies this interface as the dynamic
library. The performance evaluation tool is designed to enable the tester to
change the face recognition module during the run time so that the tester can
perform evaluation by changing the face recognition module without modifying
the performance evaluation tool.BioAPI 1.1 was applied for the compatibility with
international standards, and only the minimum number of functions required for
algorithm performance evaluation was selected, in order to reduce the
examinee’s development burden. Table3 shows the selected BioAPI for the face
recognition module.Table 3
Selected BioAPI for face recognition module.
Function nameMeaningBioAPI_Initinitializes the face recognition
moduleBioAPI_Terminateterminates the face recognition
moduleBioAPI_FreeBIRHandlereleases
the BIR data memoryBioAPI_GetBIRFromHandlereturns
the BIR data from the data handleBioAPI_CreateTemplategenerates
the data template from the galleryBioAPI_Processgenerates
the BIR data by processing the probe imageBioAPI_VerifyMatchreturns
the verification result by comparing two processed images


## 4.3. Measurement Criteria

The performance evaluation metrics used by
FERET and FRVT, as well as the metrics proposed by JTC 1/SC 37/WG 5 Standard [7],
were analyzed. Among these metrics, the criteria related with the technology
evaluation of the face recognition system were chosen, as shown in Table 4.Table 4
Selected performance evaluation metric.
Metric typeFail-to-enroll rateenrollment
throughput (no. of cases/s)Fail-to-acquire rateenrollment
time (min, max, avg)False reject rate (=fta+fnmr*(1-fta))extraction
throughput (no. of cases/s)false accept rate (=fmr*(1-fta))extraction
time (min, max, avg)false nonmatch ratematching
throughput (no. of cases/s)False match ratematching
time (min, max, avg)cmc curvestransaction
throughput(no. of cases/s)roc curvestransaction
time (min, max, avg)error equal ratetemplate
size (min, max, avg)


## 4.4. Class Diagram of Metadata

Within the performance evaluation
tool implemented in this paper, individual projects created for performance
evaluation internally generate metadata in a specific structure in order to
save the settings related to performance evaluation and performance evaluation
results. The structure of these metadata is as illustrated in Figure3.Figure 3
Example of setting the facial image test set.


### 4.4.1. CMetaProject Class

Whenever a new project is created, this class
creates an instance in connection with the project. It maintains the path used to
save the project name and the project itself, and the path to access a database
in character strings. This information is related to the project
configurations, and contains values established by the tester upon the project’s
creation. In addition, the list of face image data designed by the tester for performance
evaluation receives the lists of CMetaTestSet class. A single project may have at least one group
of face image data for its performance evaluation, and independently create a
report based on these individual evaluation results. Therefore, the information
exists in the extendible list format. Moreover, this class allows users to
ascertain the number of total images to test (compare), and that of probes and
gallery images with regards to such information.


### 4.4.2. CMetaCategory Class

This class contains data related to the subcategories
of face images. Face images are influenced by the direction of the picture
taken, the location of lighting, and posture, and so forth. According to these
conditions, they are classified, and the tester may choose some of the
classified images by using the performance test tool as the test subject group.
The chosen information is individually saved in the Cmetacategory class. These metadata
contain variables, such as the category name of each criterion, the total number
of images in the category, and the number of images failed to enroll (for gallery
items) or acquire (for probe items).


### 4.4.3. CMetaTestItem Class

This class contains data pertaining to one
face image. Each face image has a unique ID for the photograph target, its
location, and the face image items. Additionally, it contains Boolean variables
in order to save whether each item failed to enroll (for gallery items) and
acquire (for probe items) or not.


### 4.4.4. CMetatTestResult Class

This
class stores the verification results of the comparison of one probe item with one
gallery item in order to determine whether or not they come from an identical
person. It contains each item’s criteria, location, and ID information, along
with variables, such as the similarity
value and the comparison time created as a result of a comparison between two
images.


### 4.4.5. CMetaEnrolled Class

This
class saves the template data created so as to recognize a face from the image
data for the system to execute the enrollment of a gallery item. It holds not
only the item’s criteria and location information but also a binary space to
store template data, as well as data used to save the results of the template creation
and the time required to create a template.


### 4.4.6. CMetaTestset Class

This class includes information about the face
image probe group and gallery group in order to conduct the test. One project may
have several test groups, and each test group individually saves the
performance evaluation results. Metadata of the test groups bear the following
information. Firstly, the class contains CMetaCategory which is the
information holding the face image criteria as the list information for each of
the probe and gallery groups. Namely, each of the probe and gallery groups may
include a face image group with several different criteria. In addition, the
class maintains the list of CMetaTestItem for each of the probe and gallery groups.
This is not the criteria information but the metadata with individual image
item information used for the test. It also contains the list of CMetaTestResult,
containing the test results between a single probe item and a single gallery
item.Furthermore, where a system enrolls a gallery item, this
class will contain the list of CMetaEnrolled classes in order to store template
data that are created when each face recognition module generates its own
template data. This is accomplished by using the face image data. As general data of such list data, the
class contains variables to contain the test start time, end time, number of
total probe items, number of total gallery items, number of total gallery items
that failed to enroll, and number of total probe items that the system failed
to acquire. We developed the six
major modules and metadata classes that we examined above with the program for
Windows, under Microsoft’s Visual Studio development configuration. The face
recognition modules, which are the subject of the performance evaluation, work in
connection with the performance evaluation tool in the format of a dynamic link
library.


## 4.4.1. CMetaProject Class

Whenever a new project is created, this class
creates an instance in connection with the project. It maintains the path used to
save the project name and the project itself, and the path to access a database
in character strings. This information is related to the project
configurations, and contains values established by the tester upon the project’s
creation. In addition, the list of face image data designed by the tester for performance
evaluation receives the lists of CMetaTestSet class. A single project may have at least one group
of face image data for its performance evaluation, and independently create a
report based on these individual evaluation results. Therefore, the information
exists in the extendible list format. Moreover, this class allows users to
ascertain the number of total images to test (compare), and that of probes and
gallery images with regards to such information.


## 4.4.2. CMetaCategory Class

This class contains data related to the subcategories
of face images. Face images are influenced by the direction of the picture
taken, the location of lighting, and posture, and so forth. According to these
conditions, they are classified, and the tester may choose some of the
classified images by using the performance test tool as the test subject group.
The chosen information is individually saved in the Cmetacategory class. These metadata
contain variables, such as the category name of each criterion, the total number
of images in the category, and the number of images failed to enroll (for gallery
items) or acquire (for probe items).


## 4.4.3. CMetaTestItem Class

This class contains data pertaining to one
face image. Each face image has a unique ID for the photograph target, its
location, and the face image items. Additionally, it contains Boolean variables
in order to save whether each item failed to enroll (for gallery items) and
acquire (for probe items) or not.


## 4.4.4. CMetatTestResult Class

This
class stores the verification results of the comparison of one probe item with one
gallery item in order to determine whether or not they come from an identical
person. It contains each item’s criteria, location, and ID information, along
with variables, such as the similarity
value and the comparison time created as a result of a comparison between two
images.


## 4.4.5. CMetaEnrolled Class

This
class saves the template data created so as to recognize a face from the image
data for the system to execute the enrollment of a gallery item. It holds not
only the item’s criteria and location information but also a binary space to
store template data, as well as data used to save the results of the template creation
and the time required to create a template.


## 4.4.6. CMetaTestset Class

This class includes information about the face
image probe group and gallery group in order to conduct the test. One project may
have several test groups, and each test group individually saves the
performance evaluation results. Metadata of the test groups bear the following
information. Firstly, the class contains CMetaCategory which is the
information holding the face image criteria as the list information for each of
the probe and gallery groups. Namely, each of the probe and gallery groups may
include a face image group with several different criteria. In addition, the
class maintains the list of CMetaTestItem for each of the probe and gallery groups.
This is not the criteria information but the metadata with individual image
item information used for the test. It also contains the list of CMetaTestResult,
containing the test results between a single probe item and a single gallery
item.Furthermore, where a system enrolls a gallery item, this
class will contain the list of CMetaEnrolled classes in order to store template
data that are created when each face recognition module generates its own
template data. This is accomplished by using the face image data. As general data of such list data, the
class contains variables to contain the test start time, end time, number of
total probe items, number of total gallery items, number of total gallery items
that failed to enroll, and number of total probe items that the system failed
to acquire. We developed the six
major modules and metadata classes that we examined above with the program for
Windows, under Microsoft’s Visual Studio development configuration. The face
recognition modules, which are the subject of the performance evaluation, work in
connection with the performance evaluation tool in the format of a dynamic link
library.


## 4.5. Implementing Data Preparation/Execution/Result Analysis Module

The performance evaluation
tool was developed as an application program running on Windows OS, and the
face recognition module to be evaluated was implemented as a dynamic link library
(DLL). The data preparation module that has the function of connecting with the
biometric database and of setting the gallery and probe image set was
implemented for use in the performance
evaluation, as shown in Figure4.Figure 4
Selection window for face image probe set and gallery set.The face recognition module provided by the vendor was checked to verify
that it provides the functions presented in Table3, and the execution module
that performs evaluation was implemented, using the functions of the selected
face recognition module. A function that visually displays whether performance
evaluation is progressing properly or not was included in the execution module,
as shown in Figure 5.Figure 5
Progress visualization when evaluating performance.Finally, the value of evaluation criteria is
calculated by analyzing the similarity saved in the performance evaluation
result database, and the result analysis module that generates the performance
evaluation result report is implemented. This performance evaluation tool is
equipped with a function that generates the evaluation result, as well as a
function that issues the certificate for the face recognition module, depending
on the evaluation result.


## 5.  Comparison of Performance Evaluation Methods

Table5 shows a comparison made between
FERET and FRVT, which are the representative face recognition evaluation cases,
and the evaluation method that uses the performance evaluation tool proposed by
this paper.Table 5
Comparison between evaluation methods (FERET, FRVT, and the proposed performance evaluation tool).
FERETFRVTProposed evaluation toolFace recognition systemEvaluation targetFace recognition systemScenarioFace recognition systemOperationFace DB distribution methodDB distribution onsite (possibility of face DB disclosure)DB distribution on-site (possibility of face DB disclosure)Vendors provide the face recognition module. (No risk of disclosing face DB)FRVT 2006: Vendors provide the face recognition module. (No risk of  disclosing face DB)Performance evaluatorVendor staffVendor staffEvaluatorFRVT 2006: EvaluatorResult analysis toolPerformance result analysis tool is requiredPerformance result analysis tool is requiredNo analysis tool is requiredResult reportManual workManual workAutomatically generated by thegenerationtoolIndividual evaluationImpossibleImpossibleIndividual valuation by vendorEvaluation costsAll related vendor staff should congregate in one place (expensive)All related vendor staff should congregate in one place (expensive)No meeting is required (low cost)FRVT 2006: No meeting is required (low cost)Responsibility of theI/O file format should be compliedI/O file format should be compliedComplies with recognition modulevendorwithwithinterfaceCompared with performance evaluation programs
such as FERET and FRVT, the performance evaluation tool proposed by this paper
provides the following benefits.(i)
Disclosure of the face image database can be fundamentally prevented.(ii)
Development of a face recognition module that complies with international standards will be encouraged.(iii)
The performance evaluation target can be separated from the performance tester.(iv)
The evaluation cost can be reduced significantly, and individual evaluations can be performed for each vendor.


## 6. Conclusion

This paper proposed a PEM to evaluate the
performance of biometric recognition systems. The proposed PEM is designed for compatibility
with the related international standards, thereby contributing to the enhanced
consistency and reliability of the performance evaluation tool that is
developed according to this design. The proposed PEM is essential for the
following reasons.(i)
It represents a model and development method for
the performance evaluation system.(ii)
It applies the related
international standards to the performance evaluation system.(iii)
It enhances the consistency and reliability of the performance evaluation system.(iv)
It provides guidelines for the
design and implementation of the performance evaluation system by formalizing
the performance test process.In addition, a performance evaluation tool capable
of comparing and evaluating the performance of the commercialized facial
recognition systems was designed and implemented, and an evaluation that executed
800 billion comparisons in 596 hours using the KFDB [8] was conducted. The
certificate issuance criteria regarding the performance of the face recognition
systems should be presented systematically, and a method should be prepared
that can promote certification.



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*Source: 742504-2007-12-25.xml*