Existing definitions for biometric testing and evaluation do not fully explain errors in a biometric system. This paper provides a definitional framework for the Human Biometric-Sensor Interaction (HBSI) model. This paper proposes six new definitions based around two classifications of presentations, erroneous and correct. The new terms are: defective interaction (DI), concealed interaction (CI), false interaction (FI), failure to detect (FTD), failure to extract (FTX), and successfully acquired samples (SAS). As with all definitions, the new terms require a modification to the general biometric model.

1. BSPAWP 060709
© Copyright BSPA Laboratory and Purdue University 2009. All rights reserved. Elliott & Kukula| Page 1 of 6

Abstract—Existing definitions for biometric testing and
evaluation do not fully explain errors in a biometric system.
This paper provides a definitional framework for the Human
Biometric-Sensor Interaction (HBSI) model. This paper
proposes six new definitions based around two classifications of
presentations, erroneous and correct. The new terms are:
defective interaction (DI), concealed interaction (CI), false
interaction (FI), failure to detect (FTD), failure to extract
(FTX), and successfully acquired samples (SAS). As with all
definitions, the new terms require a modification to the general
biometric model.
I. INTRODUCTION
BSERVATIONS made during biometric scenario and
operational evaluations involving several thousand
individuals across multiple modalities have led the authors to
identify and categorize interactions between either the human
and sensor, or the samples and biometric system itself, which
have not been previously defined or thoroughly analyzed. As
the subfield of Human-Biometric Sensor Interaction matures
and peers within the biometric community and related fields
of ergonomics and usability provide input, an etymology for
the field can now be presented. In doing so, the authors
re-examine current biometric testing and evaluation metrics
and definitions and propose additional metrics that align with
the HBSI model.
The origin of the Human Biometric Sensor Interaction
(HBSI) sub-field within biometrics resulted from
participation in the U.S. national biometric standards
committee INCITS M1, as well as the international
committee ISO/IEC JTC1 SC37. Participation on these
committees, especially during the development of the
framework testing and evaluation standards [1-4], has
enabled the authors to understand the breadth of the current
standards and their related measurements, as well as isolate
areas during biometric performance evaluations that have
been traditionally out of scope. The areas outside the
traditional metrics include the human-sensor interaction and
how this interaction impacts the biometric samples in the
system itself. HBSI evaluation concentrates on the minutiae
of each interaction to fully understand how users interact with
BSPAWP 060709 posted June 7, 2009 on www.bspalabs.org/publications.
S. J. Elliott, Ph.D. is Director of the Biometric Standards, Performance, &
Assurance (BSPA) Laboratory and Associate Professor in the Department of
Industrial Technology at Purdue University, West Lafayette, IN 47907 USA
(phone: 765-494-1088; fax: 765-496-2700; e-mail: elliott@ purdue.edu).
E. P. Kukula, Ph.D. is a Visiting Assistant Professor & Senior Biometric
Researcher in the BSPA Laboratory in the Department of Industrial
Technology at Purdue University, West Lafayette, IN 47907 USA (e-mail:
kukula@ purdue.edu).
a biometric system and how the biometric system responds to
this human-sensor interaction.
II. THE HUMAN-BIOMETRIC SENSOR INTERACTION (HBSI)
The HBSI model is conceptualized in Fig. 1 and derived from
separate areas of research, namely ergonomics [5], usability
[6], and biometrics [7]. Please see [8-13] for a complete
discussion regarding the HBSI research area. The purpose of
the model is to demonstrate how metrics from biometrics
(sample quality and system performance), ergonomics
(physical and cognitive), and usability (efficiency,
effectiveness, and satisfaction) overlap and can be used to
evaluate overall functionality and performance of a biometric
system. Including metrics from different disciplines enables
evaluators and system designers to better understand what
affects biometric system performance. Core research
questions the HBSI addresses are:
 How do users interact with biometric devices?
 What errors do users make?
 What are the most common errors or issues that users
face?
 Why do users continually make these interaction
errors and how do we prevent or avoid them from
happening?
 What level of training and experience is necessary to
successfully use biometric devices?
Fig. 1. The HBSI conceptual model [8, 11, 12].
A Definitional Framework for the Human-Biometric Sensor
Interaction Model
Stephen J. Elliott, Ph.D. and Eric P. Kukula, Ph.D., Member, IEEE
O

2. BSPAWP 060709
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III. BIOMETRIC TESTING AND EVALUATION METRICS
Traditional biometric performance testing and evaluation
metrics include: failure to enroll (FTE), failure to acquire
(FTA), the false match rate (FMR), the false non-match rate
(FNMR), the false accept rate (FAR), and the false reject rate
(FRR). These metrics are used for the different types of
performance evaluations, including technology, scenario, and
operational evaluations. A discussion of these metrics and
definitions leads to continued dissection of their meaning. Of
main concern to the HBSI is FTA, which has been the
biometric community’s de facto “usability” metric. FTA is
defined as the “proportion of verification or identification
attempts for which the system fails to capture or locate an
image or signal of sufficient quality” [2]. Through the
authors’ experience in testing and evaluation, interactions
with a biometric system are traditionally viewed as a binary
system; presentations either become successfully acquired
samples or result in an acquisition failure (FTA). This binary
result has impeded system designers the ability to understand
how users are interacting with a biometric system and how
the biometric system responds to this human-sensor
interaction as all acquisition errors have been treated the
same.
As the authors dissected this definition of FTA, it became
apparent that factors and interactions existed, but did not fit
within the existing definition or purpose of FTA as noted
above. The HBSI framework for biometric interactions
examines each recorded human-sensor interaction as an
event. Each event is classified as either an erroneous
presentation or correct presentation. Readers may question
the difference between FTA (traditional performance and
evaluation metrics), traditional usability metrics, and the
metrics contained in the HBSI framework for biometric
interactions. Recall that failure to acquire is a system reported
metric for which the biometric sub-system “fails to capture or
locate an image or signal of sufficient quality” [2]. The HBSI
framework for biometric interactions is concerned with each
interaction with the biometric device, regardless of result.
Summarizing the difference, FTA is concerned with
biometric performance, whereas metrics in the HBSI
framework evaluate biometric systems from both the system
and user perspective. Including both perspectives
differentiates the HBSI framework from traditional usability
evaluations, as HBSI is not only concerned with improving
user efficiency, effectiveness, and satisfaction, but also is
concerned with the impact the interactions have on the
biometric system. More in line with traditional usability
testing, [14] discusses an alternate usability taxonomy for
biometrics that only considers the user perspective. Work is
currently underway with these researchers to map the HBSI
framework and usability taxonomy.
IV. THE HBSI FRAMEWORK METRICS
The purpose of the HBSI framework is to understand what
common correct and incorrect movements or behaviors are
occurring with biometric devices. Correct presentations are
interactions with the sensor that can be classified as a: failure
to detect (FTD), failure to extract (FTX), or successfully
acquired samples (SAS). An erroneous presentation is an
interaction with the sensor that should be classified in three
ways: defective interaction (DI), false interaction (FI), or
concealed interaction (CI). The sum of these six
classifications is equal to all the human-biometric sensor
interactions with the system being evaluated. The following
subsections discuss the six classifications included in the
HBSI framework that is shown in Fig. 2.
A. Erroneous Presentation
The first subsection of measurements within the HBSI
framework that is discussed involves erroneous presentations.
These presentations are further classified into three metrics:
defective interactions, concealed interactions, and false
interactions.
1) Defective Interaction (DI)
A defective interaction (DI) occurs when a bad
presentation is made to the biometric sensor and is not
detected by the system. A DI results from an individual
placing biometric features incorrectly on or to the sensor or in
the case of biometric camera devices (face, iris, etc…) not
looking in the appropriate area or direction. An example of a
DI is a user looking away from the camera when approaching
an access control gate controlled by iris recognition. The
biometric system was “correct” in not detecting the presence
of the subject because the individual did not present
appropriate biometric characteristics to the sensor.
DI’s are important to measure because they provide
quantifiable data for system design and throughput time, for
example. Additionally, DI’s provide crucial quantifiable data
to facilitate better training materials or policy guides for
system implementation.
2) Concealed Interaction (CI)
The next classification of erroneous presentations are called
concealed interactions (CI). CI’s occur when an erroneous
presentation is made to the sensor that is detected by the
biometric system, but is not handled or classified correctly as
an “error” by the biometric system. Therefore CIs are
accepted as successfully acquired samples even though it was
from an erroneous presentation. In other words, concealed
interactions are those attempts where the user presents
biometric characteristic(s) to the sensor, but used the wrong
biometric characteristic, yet the sensor recorded the
interaction as a successfully acquired sample. The CI rate is
defined as the proportion of presentations that contain
incorrect biometric characteristics acquired by the sensor that
are classified as acceptable by the biometric system. In order
to better explain where the anomaly occurred CI’s are
segmented into two categories: user concealed interactions
and system concealed interactions.
User CI: User concealed interactions result from
presentations where the user is at fault for the erroneous
presentation that is recorded as a successfully acquired
sample. An example of user CI with fingerprint recognition
would be a user that is instructed to use one finger, but
chooses, for whatever reason, to use a different one. Even

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Fig. 2. HBSI framework for biometric interactions.
though the interaction with the incorrect finger over the
sensor was performed correctly, the system accepted the
presentation, even though the incorrect finger was used.
System CI: The second category of CI is from the system
perspective. System CI’s are the result of an erroneous
presentation that contains unrecognizable features which are
subsequently recorded as a successfully acquired sample. An
example of this in a fingerprint system would be where a user
inadvertently interacts with the sensor with their fingertip or
interphalangeal joint when the expected or desired
characteristics were the volar pads of the fingers. However,
the system records this inadvertent interaction as a
successfully acquired sample. Another example of a system
CI involving face or iris recognition would be where an image
of a shadow, reflection, unrecognizable features, etc… are
captured by the system and stored as successfully acquired
samples.
The CI rate regardless of whether it is user or system
concealed might have implications for measuring habituation.
It should be noted that the commonly used term “habituation”
might have to be revisited as we learn more about how people
learn to use such devices. For example, with dynamic
signature verification, individuals are probably habituated to
the act of signing, but not the interaction with a particular
signature pad. Another such example would be where users
are used to interacting with a biometric sensor one way, but
not another; for example in fingerprint recognition: swipe
versus slap. The authors will endeavor to define habituation,
training, and learning with metrics and experimental data in
subsequent articles.
3) False Interaction (FI)
The last classification that is possible within the erroneous
presentation category is false interaction (FI). A FI occurs
when a user presents their biometric features to the biometric
system, which are detected by the system and is correctly
classified by the system as erroneous due to a fault or errors
that originated from an incorrect action, behavior, or
movement executed by the user. The FI rate is defined as the
proportion of interactions with the sensor that the biometric
system detects and correctly classifies as erroneous.
FI’s are concerned with presentations in which a user does
not interact properly with the sensor, and the biometric
system correctly recognizes the attempt as a problematic or
erroneous presentation. The user presents biometric
characteristic(s) to a biometric sensor where the system both
detects and acknowledges the error with a message and/or
feedback to the user to retry.
The false interaction (FI) rate is proposed to evaluate the
effectiveness of sensor design and training materials.
Additionally, one would expect a difference in the FI rates of
effectively trained or ineffective or non-trained individuals,
which has been examined in [15].
B. Correct Presentation
The second category of measurements within the HBSI
framework involves correct presentations. These are further
divided into three subcategories: failure to detect (FTD),
failure to extract (FTX), and successfully acquired samples
(SAS).

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1) Failure to Detect (FTD)
The first classification involving a correct presentation to the
sensor but not detected by the biometric system is called a
failure to detect (FTD). The definition of FTD is the
proportion of presentations to the sensor that are observed by
test personnel but are not detected by the biometric system.
Failure to detect errors can be separated as system errors or
external factor (environmental) errors.
System FTD: A system FTD occurs when a user presents their
biometric characteristic(s) properly (or appears correct to the
data collection / analysis personnel) to the sensor, but the
system does not detect that a presentation was made and the
system remains in the same state as before the user interaction
took place. The biometric sub-system does not detect the
correct interaction of the user. For example, with iris
recognition, a user stands in the appropriate position for the
camera to collect iris characteristics, but the system fails to
respond and/or does not detect the presence of the subject or
iris with the sensor, i.e. the biometric system remained in the
same state as before the interaction.
External Factor FTD: An external factor FTD results from an
extraneous factor impacting the ability of the biometric
system to recognize a user’s correct presentation to the
sensor. For example with face recognition, iris recognition, or
hand geometry if a light source is in the field of view of the
device, the biometric system will not be able to detect the
presence of biometric characteristic(s). Due to factors beyond
the user, and the biometric system, the presentation cannot be
detected.
The failure to detect rate provides data to system designers
revealing the user interactions the system did not detect,
regardless of the cause. The FTD rate exposes user
interactions that have typically not been collected during
performance evaluations. Understanding these issues will
enable system designers to further improve devices and
algorithms and reduce user frustration of those who believe
they are interacting with the sensor correctly, yet the sensor
does not detect their features as being present.
2) Failure to Extract (FTX)
After a correct presentation is made to the sensor, and it is
detected by the sensor, and acquisition occurs, the system
attempts to create biometric features from the collected
sample. In the general biometric model, this occurs in the
signal processing module. A failure to extract (FTX) is
concerned with samples from the data collection module that
are unable to be processed completely. This may occur for a
number of reasons, such as segmentation, feature extraction,
or quality control. The authors debated on whether to segment
these errors into individual components described in the
general biometric model. However, as these components
may, or may not be present in a biometric system, it would be
hard to differentiate these errors into failure to segment,
failure to feature extract, or failure to determine quality, and
therefore a grouping metric of failure to extract was
determined. The biometric system would provide this metric
in the log. Formally, the definition of failure to extract is the
proportion of samples that are unable to process or extract
biometric features. FTX is a system error. An example of this
error would be when the biometric system acquires a
fingerprint sample from the data collection module, but is
unable to process the sample into biometric features, thus
returns an error.
3) Successfully Acquired Samples (SAS)
As the name implies, a successfully acquired sample (SAS)
occurs if a correct presentation is detected by the system and
if biometric features are able to be created from the sample.
SAS result from presentations where biometric features are
able to be processed from the captured sample, which are then
passed to the biometric matching system.
V. REVISIONS TO THE BIOMETRIC MODEL
The HBSI framework that has been discussed until now was
dependent upon one thing – the involvement of the human
during data collection to thoroughly understand the
human-biometric sensor interaction. To align the general
biometric model (Fig. 3) with the proposed framework, the
authors propose revising the signal processing module where
template creation exists for two reasons.
Additionally, the HBSI framework can easily be applied to
scenario and operational performance evaluations, but does
not fully align with technology evaluations. Biometric
technology evaluations typically use data that was collected
at an earlier point in time to test biometric algorithms. The
authors have noticed during some of these evaluations that the
data used originally was not meant for a biometric system.
Thus, the general biometric model should reflect the use of
non-biometrically collected (NBC) data. The following
sections will discuss these two revisions.
Fig. 3. General Biometric Model [2].
A. Template Creation and Enrollment
In the current general biometric model (Fig. 3), the creation of
an enrollment template is undertaken in the signal processing
module (Fig. 4A). In order to re-align the model with the
system errors proposed in this paper, namely the failure to
extract (FTX), the enrollment template creation is moved
outside of the signal processing module (Fig. 4B). The
movement of template creation outside signal processing is

5. © Copyright BSPA Laboratory and Purdue University 2009. All rights reserved. Elliott & Kukula| Page 5 of 6
proposed to differentiate the Failure to Enroll (FTE) metric
from the Failure to Extract (FTX) metric, which as mentioned
earlier is a grouping metric for failures due to quality, feature
extraction, and/or segmentation. In addition to differentiating
FTE and FTX, this movement is also proposed to better
handle non-biometrically captured (NBC) data.
Fig. 4. General biometric model (A) Current signal processing module and
(B) Proposed signal processing module with Template Creation relocation.
B. Non-Biometrically Captured (NBC) Data
There are some cases where the signal processing system may
accept samples offline, such as in technology testing, which
may use data that was not collected by a biometric system.
This data is vastly different from data collected with a
biometric device and should be labeled accordingly. The
authors propose classifying data of this kind
non-biometrically captured data, or NBC. An example of this
would be face recognition. In this example, photographs were
taken by an operator of a camera that was not intended at the
time for face recognition. The camera does not have any
software that specifically looks at the image as a face.
Therefore the operator acquires a photograph, which may not
be able to be extracted by the face recognition system.
Another example is inked ten-print cards for use with
fingerprint recognition. At the time the data was collected, the
intent was not to be scanned and used with a biometric
system.
With respect to the biometric model, the hashed line in Fig.
5 stemming from the NBC data storage module indicates the
process flow. The hashed line divides post signal processing
once biometric features have been extracted for either
enrollment or matching. Again, the proposition of the NBC
data storage area by the authors is due to the fact that some
performance metrics associated with online data collection
might not be available. In the current general biometric model
it is assumed that the process is done online, and this NBC
route allows for offline processing, which is in line with
technology testing.
Fig. 5. General biometric model with additions of template create and Non
Biometrically Captured Data (NBC).
VI. CONCLUSION
The general biometric model and the various definitions
wrapped into it have provided the basis for biometric testing
and reporting standards. As the biometric community
examines and discusses these definitions and continues to test
and evaluate systems, additions to the general biometric
model and testing and reporting standards have to be
considered. In our previous work, we have explained the
development of the concept of the human biometric sensor
interaction, and this paper provides a series of terms and
definitions for that model.
VII. FUTURE WORK
The authors have a series of future works planned in this area
and have already applied the error framework to fingerprint
recognition. Additional modalities will be evaluated with this
framework, such as hand geometry, iris recognition and
dynamic signature verification. Other terms that are in use
within the biometric community will also be examined within
the context of the HBSI model; habituation is one example of
this.
ACKNOWLEDGMENT
The authors would like to thank members of INCITS M1.5
for their input during a presentation of this HBSI framework
[12] in Morgantown, WV April 16, 2009. The authors would
especially like to recognize Brad Wing, Patrick Grother, and
Rick Lazarick for their feedback and contribution.

6. © Copyright BSPA Laboratory and Purdue University 2009. All rights reserved. Elliott & Kukula| Page 6 of 6
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