Best Practices in Reporting Time Duration in Biometrics

BEST PRACTICES IN

REPORTING TIME DURATION

IN BIOMETRICSBen Petry, Dr. Stephen Elliott, Dr. Richard Guest, Dr.

Mathias Sutton, and Kevin O’Connor

• What is Time?

• Why Examine Time in Biometrics?

• A Short History of Time

• Time in Biometrics

• The General Biometric Model and HBSI Model

• Changes to the General Biometric Model and HBSI Model

• Biometrics Duration Scale Model

• Conclusions

OVERVIEW

• “The measured or measurable period during which an action, process, or condition exists or continues” [1].

• “Indefinite, unlimited duration in which things are considered as happening in the past, present, or future; every moment there has ever been or ever will be” [2].

• “The science of arranging time in fixed periods for the purpose of dating events accurately and arranging them in order of occurrence” [2].

• International Electrotechnical Commission Time Definition (60050-113-01-03): “One-dimensional subspace of space-time, which is locally orthogonal to space.”

WHAT IS TIME?

• The current model of time in the biometrics field are not consistently applied, as there are limited definitions that can be used.

• Researchers speak of very specific intervals of data collection which may be confusing and create a disconnect between studies, making them hard to compare.

• Time, though, “is basically a human construct to fit the needs of humans as we grow and evolve, it stands to reason that we can and should rethink and try to adapt our ideas and use of time into something that will be more useful” [3].

• Namely, a new construct should be developed to fit the needs of the field.

WHY EXAMINE TIME IN BIOMETRICS?

• Examples of “time” in biometrics literature:

• “One set in particular, the US-VISIT Point of Entry dataset (POE) contains all fingerprints collected by US-VISIT between January and June 2004” [15].

• “All of the iris images used in this study were acquired with the same LG 2200 iris imaging camera, located in the same studio throughout the four years of image acquisition” [16].

• “The database comprises 264,645 iris images of 676 unique subjects captured over 27 sessions” [17].

• “The final dataset was collected in four acquisition sessions, which spanned a total of 12 weeks” [18].

WHY EXAMINE TIME IN BIOMETRICS

•Animal bones dating to 20,000 years ago used

to keep track of the number of days between

moon cycles [4].

•2776 BC, Egyptians calculated the year to be

365 days year [4].

A SHORT HISTORY OF TIME

• The Sumerians into divided their days into 12 equal parts [4].

• 1000 years later, the Babylonians created the modern framework of 24 hour days with each hour divided into 60 minutes and each minute divided into 60 seconds [4].


• Romans merged the framework of the Egyptian system of 365 days per year with the Babylonian concept of hour, minutes, and seconds [5].

• Developed the Julian calendar system.

• The Gregorian calendar (1582) updated the Julian calendar to take into account that the Earth takes 365 ¼ days to revolve the sun [5].


• Clocks that changed the world

• Clepsydras (water clocks) developed in Egypt (1500 BC) [4]

• Pendulum regulated clock created by Christiaan Huygens (1656) [6]

• The Harrison chronometer accurate to one second loss per day and required for accurate Atlantic ocean navigation (1737) [7]


• Clocks that changed the world

• The most capable mechanical clock is the Shortt clock capable of one tenth of a second error per day (1921) [8]

• The first atomic clocks developed capable of 0.00001 seconds of error per day and is more accurate then any measure of time that could be calculated from astronomical observations (1949) [7]

• Current cesium based clocks are accurate to one trillionth of a second uncertainty per day [7]


• Coordinated Universal Time (UTC):

• The primary time standard the world sets their clocks to.

• The standard time for the internet, aviation, and other industries which operate on a global scale with precision.

• Interchangeable with Greenwich Mean Time in most cases.

• Acts as the global ‘midnight’

• Denoted as ‘Zulu’ time (Used by the United States military)

• Does not vary with seasons (Daylight Savings Time)

A SHORT HISTORY OF TIME – WHERE

ARE WE NOW?

A SHORT HISTORY OF TIME – UTC

http://commons.wikimedia.org/wiki/File:World_Time_Zones_Map.png

• ISO 8601 created in 1988, in part to restore “numerous misconceptions of dates and time”.

• [hh] refers to a zero-padded hour 00 – 24

• [mm] refers to a zero-padded minute 00 – 59

• [ss] refers to a zero-padded second 00 – 60

• Time Zone Designators: Z

• Example: 15:43:07.159Z = 15 hours, 43 minutes, 7.159 seconds

A SHORT HISTORY OF TIME – ISO TIME

• P = the duration designator (historically called "period") placed at the start of the duration representation

• Y = the year designator

• M = the month designator

• W = the week designator

• D = the day designator

• T = the time designator that precedes the time components of the representation

• H = the hour designator

• M = the minute designator

• S = the second designator


(NOTING DURATION)

Example: P4Y2M1W4DT2H14M43S

Duration = 4 years, 2 months, 1 week, 4 days, 2 hours, 14 minutes, 43 seconds

•Start and End: 2015-03-24T18:27:30.000Z/2015-03-14T08:00:00.000Z

•Start and Duration: 2015-03-24T18:27:30.000Z/P4Y2M1W4DT2H14M43S


(NOTING TIME INTERVALS)

• IEC 60050-113 (2011)

• Time (113-01-03): “One-dimensional subspace of space time, which is locally orthogonal to space.”

• Process (113-01-06): “Sequence in time of interrelated events.”

• Time Axis (113-01-07): “Mathematical representation of the succession in time of instantaneous events along a unique axis.

• Instant (113-01-08): “Point on the time axis.”

A SHORT HISTORY OF TIME – IEC TIME

http://www.electropedia.org/iev/iev.nsf/index?openform&part=113

• IEC 60050-113 (2011)

• Time Interval (113-01-10): “Part of the time axis limited by two instants.”

• Time Scale (113-01-11): “System of ordered marks which can be attributed to instants of the time axis, one instant being chosen as the origin.”

• Date (113-01-12): “Mark attributed to an instant by means of a specified time scale.”

• Duration (113-01-13): “Range of a time interval.”



• IEC 60050-113 (2011)

• Accumulated Duration (113-01-14): “Sum of durations characterized by given conditions over a given time interval.”

• Calendar Date (113-01-16): “Date on a time scale consisting of a calendar and a succession of calendar days.”

• Clock Time (113-01-18): “Quantitative expression marking an instant within a calendar day by the duration elapsed after midnight in the local standard time.”



• “Time” is purely conceptual. It doesn’t matter if you divide a day into six parts, 12 parts, or 24 parts, as year as everyone agrees what those divisions are.

• Time resolution is as accurate as the current technology allows. The smaller the unit of time, the more we can understand our surroundings.

A SHORT HISTORY OF TIME –

TAKEAWAYS

• With as many definitions of “time” and associated terminology, there are just as many subjective measurements:

• Morning, afternoon, or evening have many meanings within the context to time depending on with whom you ask.

• Additionally, the subdivisions of time may be necessary depending on the research being conducted.

• Single day a week/month/year collections

• Morning vs evening

• Before/after treatment

TIME IN BIOMETRICS – SUBJECTIVE

MEASUREMENTS

• Definitional

• Operational Times [9]

• The Relationship Between Presentations, Attempts and Transactions [11]

• Non-definitional

• ‘Subject biometric information was collected once every __ (days, weeks, months, years, semesters, etc.) in __ visits over __ amount of time.’

• Very sporadic time frames that are confusing and difficult to interpret/replicate.

TIME IN BIOMETRICS – SUBJECTIVE

MEASUREMENTS

• ‘Time’ aligns with the specific function the user

or system are undergoing for a duration of

measurable units (seconds for example).

•Helpful for analyzing system throughput

performance.

TIME IN BIOMETRICS – OPERATIONAL

TIMES

• Total Transaction Time: The “…sum of all the subcomponent periods of time associated with the biometric application system” [9].

• Overt Biometric Transaction Time: “This begins with the biometric sample presentation and ends with the biometric decision. Therefore, this includes the presentation of the biometric trait portion of the subject interaction time, biometric subsystem processing time, which includes sample acquisition and sample processing time, and the biometric decision time” [9].

• Subject Interaction Time: “…commences when a claim of identity is made (or presented)… The time ends when the individual has presented his/her biometric characteristic(s) and the sensor begins to acquire the sample” [9].


TIMES

• Biometric Subsystem Processing/Transaction Time: “…the time taken for the system to acquire the biometric sample, to evaluate the quality of the sample, and if the quality is satisfied, to process that sample for comparison. For the samples of bad quality, the biometric system requests the subject to submit the biometric trait. The biometric subsystem processing time ends when either a comparison score or a request for re-submission is generated” [9].

• Biometric Decision Time: “…the time required by the biometric subsystem to generate an accept or reject response based on the comparison score and the decision logic” [9].

• External Operation Time: “…the time required to complete the application transaction” [9].


TIMES


TIMES [9]

• Using the logic of the operational times, [10] found mean enrollment and mean verification times of hand geometry recognition machines.

• By measuring and analyzing the frames of collected video recordings of subject interactions with the machine, the researchers we able to accurately conclude transaction times to the closest one-fifteenth of a second.

• “This paper has shown that videos can be automatically coded post-hoc to determine transaction times without the use of a human operator” [10].


TIMES APPLIED

•All subject-led

•Physical determination of when the interaction starts – “interaction volume” – which is modality and sensor-led

• Use beam-breakers for example

TIME IN BIOMETRICS – WHEN DOES

THE INTERACTION START?

TIME IN BIOMETRICS – THE RELATIONSHIP

BETWEEN PRESENTATIONS, ATTEMPTS AND

TRANSACTIONS [11]

• This framework, at its core, is very ingenious.

• Definitionally, it is quite confusing:

• Are erroneous presentations and correct presentations classified as the same thing?

• What if the system does not detect a correct presentation?

TIME IN BIOMETRICS – THE RELATIONSHIP

BETWEEN PRESENTATIONS, ATTEMPTS AND

TRANSACTIONS

• The general biometric model was in 1998 and aims to identify “the common structures and parallelisms between seemingly disparate methodologies” [12].

• The most recent version provides better clarity with regards to data storage, matching, and decision making processes

TAKING A STEP BACK – THE GENERAL

BIOMETRIC MODEL

THE GENERAL BIOMETRIC MODEL

ISO/IEC 19795-1

• The Human-Biometric Sensor Interaction (HBSI) model was created “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” [13].

• Strengths of the HBSI model

• The ability to define how to user interacts with a system

• What errors, especially common ones, are made by the user

• What is causing these errors to occur repeatedly

• How well does the user need to be trained to correctly interact with the system

THE HUMAN BIOMETRIC-SENSOR

(HBSI) MODEL

THE HBSI MODEL

• Erroneous Presentation: Any presentation, whether made with malicious intent or not, that was not performed to the specifications of the particular biometric sensor collecting the sample.

• Defective Interaction (DI): “…occurs when a bad presentation is made to the biometric sensor and is not detected by the system” [13].

• Concealed Interaction (CI): “…occurs 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” [13].

• False Interaction (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” [13]

HBSI COMPONENT DEFINITIONS

• Correct Presentation: Any presentation that was performed within the specifications of the particular biometric sensor collecting the sample.

• Failure to Detect (FTD): “…the proportion of presentations to the sensor that are observed by test personnel but are not detected by the biometric sensor” [13]. There are two types of FTD: system and external factor.

• Failure to Extract (FTX): “…the proportion of samples that are unable to process or extract biometric features” [13].

• 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 systems” [13].

HBSI COMPONENT DEFINITIONS

•These two very important, useful models to

date have not been mapped together.

•This is first necessary before the biometric

duration scale model can be explained

SO WHAT?

• (a) Expansion of the

data capture

subsystem:

• Includes the addition of

different capture

technologies

ALIGNMENT OF THE GENERAL BIOMETRIC

MODEL WITH THE HBSI MODEL

• (a) Expansion of the data capture subsystem:

• Pre-Processing Capture:

• The system performs some quality or other analysis during data capture.

• Once correct collection specifications have been obtained, the capture device detects and captures a sample




• Instantaneous Capture:

• The system captures biometric data that is presented as is to the system immediately in secession of presentation, sensor, and detection.




• Continuous Capture:

• The system captures biometric data that is presented as is to the system over a certain collection period.

• Example: Signature recognition is continually detecting input from the user to the sensor.



• (a) Expansion of the

data capture

subsystem:

• If any other these

capture processes fails,

it is a DI or FTD error.



• Specification of the reacquire loop (b):

• This reacquire loop only occurs if a FTX, FI, or CI error occurs.

• May happen at any point in segmentation, feature extraction, or quality control.

• Note: For CI, these should be re-acquired if detected by test administrator or system.



• Division of the model into three specific sections:

• Data Capture

• Data Processing

• Data Storage/Matching

ALIGNMENT OF THE HBSI MODEL WITH

THE GENERAL BIOMETRIC MODEL

Uses the mapped general biometric model and

HBSI model to create a timeline to explain what

is happening with the user, the system, and the

resulting output of the two.

BIOMETRIC DURATION SCALE MODEL

•Consists of two

major divisions:

• Phases

• Ranges


• Phases:

• Phases occur as part of the general biometric model and HBSI model.

• The three models (general biometric, HBSI, and biometric duration scale) are all mapped together and are represented as phases.

• The phases ultimately result in the enroll/match phase which is the summary of all scores, metrics, and other items measured.

BIOMETRIC DURATION SCALE MODEL -

PHASES

• This portion begins outside of the general

biometric model. These are the interactions and

processes that the user undergoes before and

during data capture. It is these interactions that

are determined to be "erroneous" or "correct"

presentations in the HBSI model.

• Interactions begin when a “physical space” is

being entered. This “physical space” is modality

dependent and may change with the research.

“Physical space” should be noted as part of the

publication of the research.

• The actions that are categorized from this

section can yield more information on how

subject interactions can effect system

performance.


– PRESENTATION DEFINITION PHASE

The smallest, most discrete unit of measurable content that is collected from a sensor. These units may be collected as individual figures in discrete capture systems or as frames in continuous capture systems. This phase occurs in the presentation sub-system of the general biometric model. HBSI errors include DI and FTD. If these errors occur, move back to start.


– SAMPLE PHASE

The smallest, most discrete unit of measurable content collected in the data capture sub-system of a discrete capture system. One or many figures are collected in order to obtain a sample.


– SAMPLE PHASE (FIGURES)

The smallest, most discrete unit of measurable content collected in the data capture sub-system of a continuous capture system. One or many frames are collected in order to obtain a sample.


– SAMPLE PHASE (FRAMES)

One or more samples are collected until a sample which contains measurable properties of the specific modality are obtained. These properties are subjected to the three initial processes of the signal processing sub-section (segmentation, feature extraction, and quality control) of the general biometric model. HBSI errors include FI and FTP. If these errors occur, move back to the start.


– PROCESSING PHASE

In the case of system enrollment, the template created after the signal processing sub-system is stored in the data storage sub-system. This template and associated metrics are stored in enroll phase of the biometric time model regardless if a result of enrollment or failure to enroll (FTE) occurs. If an FTE occurs, move back to start. If a CI from the HBSI model occurs, look back presentation definition phase and determine error made by subject. If a SPS from the HBSI model occurs, end the phase portion of the model and submit metrics to day range summary.


– ENROLLMENT PHASE

In the case of system matching, either verification or identification, the metrics created from the sample after the signal processing sub-system is sent to the matching sub-system of the general biometric model. These metrics are compared to a stored user located in the data storage sub-section. If an FTM occurs, add metrics to the day range statistical summary and move back to start. If a CI from the HBSI model occurs, look back presentation definition phase and determine error made by subject. If a SPS from the HBSI model occurs, end the phase portion of the model and submit metrics to day range summary.


– MATCHING PHASE


– PHASESPhase Color

Presentation Definition Phase

Sample Phase

Processing Phase

Enrollment Phase

Matching Phase

All ranges occur on a traditional Gregorian time frame and UTC. These ranges occur in the traditional terms "days", "weeks", "months", and "years". Ranges are summaries of the previous range or, in the case of day range, enroll and matching phases.


– RANGES

•A summary of all metrics from enrollment, FTE,

match, FTM, and any another other metrics of

interest collected from 0:00.00 UTC to

23:59.99 UTC for a user, system, or both.

•After such time, a new day range begins.

BIOMETRIC TIMES SCALE – DAY

RANGE

• A summary of all metrics from enrollment, FTE, match, FTM, and any other metrics of interest collected from one to seven day range for a user, system, or both.

• When an eighth day range occurs or a new Gregorian calendar week begins (starts on Sunday), a new week range begins.


– WEEK RANGE

• A summary of all metrics from enrollment, FTE, match, FTM, and any other metric of interest collected from up to four week range points for a user, system, or both.

• When a fifth week range occurs or a new Gregorian calendar month begins, a new month range begins.

• Months are variable (Feb has 28 or 29 days depending on the year while June and July always have 30 and 31 respectively). However, if a start date with the month and year are included with the duration, exact month duration can be determined including leap year.


– MONTH RANGE

• A summary of all metrics from enrollment, FTE, match, FTM, and any metric of interest collected from up to twelve year range points for a user, system, or both.

• When a thirteenth month range occurs or a Gregorian calendar year begins, a new year range begins.


– YEAR RANGE

•A summary of all metrics from enrollment, FTE,

match, FTM, and any other metric of interest

collected over the life span of the user, system,

or both.


– LIFE

• Intermediate Ranges

• If required in data collection, intermediate ranges are allowed an encouraged

• Examples:

• Hours: Day range is broken into 24 parts each an hour year.

• Quarterly: Year range is broken into four parts each three months year.

BIOMETRIC DURATION MODEL –

INTERMEDIATE RANGES

• Characteristic

• Detection Error Trade-Off Curve

• Enrollment

• Equal Error Rate

• Failure to Acquire

• Failure to Enroll

• False Accept Rate

• False Match

• False Match Rate

• False Non-Match

• False Non-Match Rate

• False Reject Rate

• False-Negative Identification Error Rate

• False-Positive Identification Rate

• Feature

• Genuine Match

• Ground Truth

• Histogram

• Identification Rate

• Impostor Match

• Match

• Match Score

• Presentation

• Receiver Operating Characteristic Curve

• Sample

• Template

• Transaction

• Zoo Plot

• Modality Specific Metrics

• Any Other Metric of Interest

METRICS THAT CAN BE EXAMINED IN

BIOMETRIC DURATION SCALE MODEL [14]

BIOMETRIC DURATION SCALE MODEL – EXAMPLE WHOLE Presentation Definition

PhaseSample Phase Processing Phase Enroll Phase Match Phase Day Range Week Range Month Range Year Range Life

Presentation (type) FTD

DD/MM//Y

YYY

Statistical Summary of

one month rangeStatistical Summary of

one year range Statistical Summary

of life of user or

system



one day range


one week range


Presentation (type) Sample FTX


Presentation (type) DI

Presentation (type) Sample SPS FTE




Presentation (type) Sample SPS Enroll

Presentation (type) Sample SPS NA FTM



Presentation (type) Sample SPS NA Match




Presentation (type) FTD DD/MM//YYYY

Presentation (type) Sample CIStatistical Summary of

one day rangePresentation (type) DI


Presentation (type) DI DD/MM//YYYY



one day range






Presentation (type) Sample SPS NA FTM DD/MM//YYYY


one week range



one day range



Presentation (type)


Presentation (type) Sample FI DD/MM/YYYY

Presentation (type) FTD Statistical Summary of

one day range


one week rangePresentation (type) Sample SPS NA Match

Presentation (type) FTD DD/MM/YYYYStatistical Summary of

one week range


one month rangePresentation (type) DI Statistical Summary of

one day rangePresentation (type) Sample SPS NA Match

Presentation (type) FTD DD/MM/YYYY


one week range


one month range


one year range

Presentation (type) Sample SPS NA MatchStatistical Summary of



BIOMETRIC DURATION SCALE MODEL –

RELATING TO EXISTED MODEL IN SCIENCE• Eon: A major division of geological

time, subdivided into eras.

• Era: A major division of time that is a subdivision of an eon and is itself subdivided into periods.

• Period: A major division of geological time; an era is usually divided into two or more periods.

• Epoch: Any of several divisions of a geologic period during which a geologic series is formed. http://www.geomore.com/geologic-time-scale/

Presentation

Definition PhaseSample Phase Processing Phase Enroll Phase Match Phase Day Range Week Range Month Range Year Range Life


Summary of one

week range

Summary of one

month range

Summary of one

year range

Summary of life

of user or system


Summary of one

day range









Presentation (type) Sample SPS Enroll "Scores"









– EXAMPLE STEP 1


– EXAMPLE STEP 2Presentation Definition



DD/MM//YY

YY


one week range


one month range


one year range


life of user or system



one day range





Presentation (type) Sample SPS FTE




Presentation (type) Sample SPS Enroll









Presentation (type) Sample CIStatistical Summary of




– EXAMPLE STEP 2Presentation



Summary of one

week range

Summary of one

month range

Summary of one year

range

Summary of life of

user or system


Summary of one day

range


















Presentation (type) Sample CISummary of one day

rangePresentation (type) DI


Presentation



Summary of one

month range

Summary of one

year range

Summary of life

of user or system


Summary of one

day range

Summary of one

week range


















Presentation (type) Sample CISummary of one

day rangePresentation (type) DI




Summary of one

day range







– EXAMPLE STEP 3


– EXAMPLE STEP 3Presentation



Summary of one

month range

Summary of one

year range

Summary of life

of user or system


Summary of one

day range

Summary of one

week range


















Presentation (type) Sample CISummary of one

day rangePresentation (type) DI




Summary of one

day range






Presentation Definition



Summary of one month

range

Summary of one year

range

Summary of life of user

or system


Summary of one day

range

Summary of one week

range























Summary of one day

range







Summary of one week

range


Summary of one day

range



Presentation (type)



– EXAMPLE STEP 4






range

Summary of one year

range


or system


Summary of one day

range

Summary of one week

range























Summary of one day

range







Summary of one week

range


Summary of one day

range



Presentation (type)





Summary of one

month range

Summary of one year

range

Summary of life of

user or system


Summary of one day

range

Summary of one week

range























Summary of one day

range







Summary of one week

range


Summary of one day

range



Presentation (type)



Presentation (type) FTD Summary of one day

range

Summary of one week

rangePresentation (type) Sample SPS NA Match


– EXAMPLE STEP 5






range

Summary of one year

range


or system


Summary of one day

range

Summary of one week

range























Summary of one day

range







Summary of one week

range


Summary of one day

range



Presentation (type)




range

Summary of one week






rangeSummary of one year

range


or system


Summary of one day

range

Summary of one week

range























Summary of one day

range







Summary of one week

range


Summary of one day

range



Presentation (type)




range

Summary of one week


Presentation (type) FTD DD/MM/YYYYSummary of one week

range


rangePresentation (type) DI Summary of one day



– EXAMPLE STEP 6







range


or system


Summary of one day

range

Summary of one week

range























Summary of one day

range







Summary of one week

range


Summary of one day

range



Presentation (type)




range

Summary of one week



range





Summary of one week

range


range

Summary of one year

range

Presentation (type) Sample SPS NA MatchSummary of one day








range

Summary of life of

user or system


Summary of one day

range

Summary of one week

range























Summary of one day

range







Summary of one week

range


Summary of one day

range



Presentation (type)




range

Summary of one week



range





Summary of one week

range


range

Summary of one year

range

Presentation (type) Sample SPS NA MatchSummary of one day




– EXAMPLE STEP 7

• Adoption of ISO 8601 methodology of reporting time and duration:

• Start and Duration: 2015-03-03T18:18:30.000Z/P4Y2M1W4DT2H14M43S

• Reporting of specific days, weeks, months, and years, in the Biometric Duration Scale.

• The following common reporting methodology is in compliance with ISO 21920.


– COMMON REPORTING

•Common reporting sentence structuring should

mimic the following:• Data collection began on 28 March 2015 and lasted for 4 years, 0 months, 1 week, and 4

days (2015-03-28T18:18:30.000Z/P4Y0M1W4DT2H14M43S). There were seven visits

which occurred in monthly intervals. The time scope of interest for this report is in the

month range. The collection period of interest for this analysis began on 1 April 2015 and

lasted for 30 days (2015-04-01T13:19:30.000Z/P0Y1M0W0DT2H14M43S).




mimic the following:• Data collection began on DAY MONTH YEAR and lasted for Y years, M months, W week,

and D days (yyyy-mm-ddThh:mm:ss.sssZ/PYYMMWWDDTHHHMMMSSS). There

were __ visits which occurred in ____ intervals. The time scope of interest for this report

is in the ____ range. The collection period of interest for this analysis began on DAY

MONTH YEAR and lasted for Y years, M months, W week, and D days (yyyy-mm-

ddThh:mm:ss.sssZ/PYYMMWWDDTHHHMMMSSS).




mimic the following:• ‘Data collection began on ___ ____ ____ and lasted for _ years, _ months, _ week, and _

days (____-__-__T__:__:__.Z/P_Y_M_W_DT__H__M__S). There were __ visits which

occurred in ___ intervals. The time scope of interest for this report is in the ____ range.

The collection period of interest for this analysis began on ___ ___ ___ and lasted for _

years, _ months, _ week, and _ days (____-__-

__T__:__:__.___Z/P_Y_M_W_DT__H__M__S).



• Time is a human construct that should be manipulated as a tool.

• Specifying intervals of time of interest for research is critical.

• The creation of a standardized framework to collect and describe data is imperative.

• By incorporating preexisting nomenclature from internationally recognized standards institutes, implementation will be quicker and easier.

CONCLUSIONS

• With small theoretical changes, the general biometric model and HBSI model can be mapped to each other as well as a new biometric time duration model.

• These models can be used to help explain what are the downstream and upstream effects of one model on another.

CONCLUSIONS

• The reporting of specific time metrics is important for the comparison of different research projects and replicating past research.

• By utilizing a common vernacular for reporting study duration, connections may become more apparent.

• Ultimately, it comes down to transparency of the collected data, declaring the scope of the research, and expressing the findings of your study in a common vernacular.

CONCLUSIONS

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REFERENCES

Best Practices in Reporting Time Duration in Biometrics

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