Biometric Subsystem Processing/Transaction Time
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BEST PRACTICES IN REPORTING TIME DURATION IN BIOMETRICS Ben Petry, Dr. Stephen Elliott, Dr. Richard Guest, Dr. Mathias Sutton, and Kevin O’Connor OVERVIEW • 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 WHAT IS TIME? • • • • “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): “Onedimensional subspace of space-time, which is locally orthogonal to space.” WHY EXAMINE TIME IN BIOMETRICS? • • • • 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]. A SHORT HISTORY OF TIME • 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]. A SHORT HISTORY OF TIME • 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]. A SHORT HISTORY OF TIME • 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] A SHORT HISTORY OF TIME • 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] A SHORT HISTORY OF TIME – WHERE ARE WE NOW? • 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 – UTC http://commons.wikimedia.org/wiki/File:World_Time_Zones_Map.png A SHORT HISTORY OF TIME – ISO TIME • 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 (NOTING DURATION) • 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 Example: P4Y2M1W4DT2H14M43S Duration = 4 years, 2 months, 1 week, 4 days, 2 hours, 14 minutes, 43 seconds A SHORT HISTORY OF TIME – ISO TIME (NOTING TIME INTERVALS) • Start and End: • Start and Duration: 2015-03-24T18:27:30.000Z/2015-03-14T08:00:00.000Z 2015-03-24T18:27:30.000Z/P4Y2M1W4DT2H14M43S A SHORT HISTORY OF TIME – IEC TIME • 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.” http://www.electropedia.org/iev/iev.nsf/index?openform&part=113 A SHORT HISTORY OF TIME – IEC TIME • 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.” http://www.electropedia.org/iev/iev.nsf/index?openform&part=113 A SHORT HISTORY OF TIME – IEC TIME • 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.” http://www.electropedia.org/iev/iev.nsf/index?openform&part=113 A SHORT HISTORY OF TIME – TAKEAWAYS • “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. • TIME IN BIOMETRICS – SUBJECTIVE MEASUREMENTS 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 – OPERATIONAL TIMES • ‘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]. TIME IN BIOMETRICS – OPERATIONAL 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]. TIME IN BIOMETRICS – OPERATIONAL TIMES [9] TIME IN BIOMETRICS – OPERATIONAL TIMES APPLIED • 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]. TIME IN BIOMETRICS – WHEN DOES THE INTERACTION START? • 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 – THE RELATIONSHIP BETWEEN PRESENTATIONS, ATTEMPTS AND TRANSACTIONS [11] TIME IN BIOMETRICS – THE RELATIONSHIP BETWEEN PRESENTATIONS, ATTEMPTS AND TRANSACTIONS • 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? TAKING A STEP BACK – THE GENERAL BIOMETRIC MODEL • 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 THE GENERAL BIOMETRIC MODEL ISO/IEC 19795-1 THE HUMAN BIOMETRIC-SENSOR (HBSI) MODEL • • 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 HBSI MODEL HBSI COMPONENT DEFINITIONS • 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]. SO WHAT? • 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 ALIGNMENT OF THE GENERAL BIOMETRIC MODEL WITH THE HBSI MODEL • (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 ALIGNMENT OF THE GENERAL BIOMETRIC MODEL WITH THE HBSI MODEL • (a) Expansion of the data capture subsystem: • Instantaneous Capture: • The system captures biometric data that is presented as is to the system immediately in secession of presentation, sensor, and detection. ALIGNMENT OF THE GENERAL BIOMETRIC MODEL WITH THE HBSI MODEL • (a) Expansion of the data capture subsystem: • 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. ALIGNMENT OF THE GENERAL BIOMETRIC MODEL WITH THE HBSI MODEL • (a) Expansion of the data capture subsystem: • If any other these capture processes fails, it is a DI or FTD error. ALIGNMENT OF THE GENERAL BIOMETRIC MODEL WITH THE HBSI MODEL • 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. ALIGNMENT OF THE HBSI MODEL WITH THE GENERAL BIOMETRIC MODEL • Division of the model into three specific sections: • Data Capture • Data Processing • Data Storage/Matching BIOMETRIC DURATION SCALE 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 BIOMETRIC DURATION SCALE MODEL • Consists of two major divisions: • Phases • Ranges BIOMETRIC DURATION SCALE MODEL PHASES • 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 – PRESENTATION DEFINITION PHASE • • • 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. BIOMETRIC DURATION SCALE MODEL – SAMPLE 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. BIOMETRIC DURATION SCALE MODEL – SAMPLE PHASE (FIGURES) 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. BIOMETRIC DURATION SCALE MODEL – SAMPLE PHASE (FRAMES) 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. BIOMETRIC DURATION SCALE MODEL – PROCESSING PHASE 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. BIOMETRIC DURATION SCALE MODEL – ENROLLMENT 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. BIOMETRIC DURATION SCALE MODEL – MATCHING 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. BIOMETRIC DURATION SCALE MODEL – PHASES Phase Presentation Definition Phase Sample Phase Processing Phase Enrollment Phase Matching Phase Color BIOMETRIC DURATION SCALE MODEL – RANGES 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. BIOMETRIC TIMES SCALE – DAY RANGE • 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 DURATION SCALE MODEL – WEEK 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. BIOMETRIC DURATION SCALE MODEL – MONTH 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. BIOMETRIC DURATION SCALE MODEL – YEAR 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. BIOMETRIC DURATION SCALE MODEL – LIFE • 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. BIOMETRIC DURATION MODEL – INTERMEDIATE RANGES • 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. METRICS THAT CAN BE EXAMINED IN BIOMETRIC DURATION SCALE MODEL [14] • • • • • • • • • • 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 BIOMETRIC DURATION SCALE MODEL – EXAMPLE WHOLE Presentation Definition Phase Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Sample Phase FTD FTD FTD Sample FTD DI Sample FTD DI FTD Sample Sample FTD DI Sample DI FTD Sample FTD Sample DI Sample DI FTD Sample Sample FTD FTD Sample Sample FTD Sample Sample Sample Sample FTD Sample FTD DI Sample FTD Sample DI Sample Processing Phase Enroll Phase Match Phase Day Range Week Range Month Range Year Range Life DD/MM//Y YYY FTX SPS SPS SPS FTE Statistical Summary of one day range Enroll NA FTM SPS NA Match SPS NA Match Statistical Summary of one week range Statistical Summary of one month range DD/MM//YYYY CI SPS NA Match Statistical Summary of one day range Statistical Summary of one year range DD/MM//YYYY FTX SPS NA Match SPS SPS NA NA FTM SPS NA FTM SPS NA Statistical Summary of one day range Match DD/MM//YYYY Statistical Summary of one day range FTX Statistical Summary of one week range Match FI SPS NA Match SPS NA Match SPS NA Match SPS NA Match DD/MM/YYYY Statistical Summary of Statistical Summary of one day range one week range DD/MM/YYYY Statistical Summary of Statistical Summary of Statistical Summary of one week range one month range one day range DD/MM/YYYY Statistical Summary of Statistical Summary of Statistical Summary of Statistical Summary of one week range one month range one year range one day range Statistical Summary of life of user or system 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/ BIOMETRIC DURATION SCALE MODEL – EXAMPLE STEP 1 Presentation Definition Phase Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Sample Phase FTD FTD FTD Sample FTD DI Sample FTD DI FTD Sample Sample FTD DI Sample DI FTD Sample Processing Phase Enroll Phase Match Phase Day Range Week Range Month Range Summary of one week range Summary of one month range Year Range Life DD/MM//YYYY FTX FTX Summary of one day range SPS SPS Enroll "Scores" NA SPS NA Match SPS NA Match FTM Summary of one Summary of life year range of user or system BIOMETRIC DURATION SCALE MODEL – EXAMPLE STEP 2 Presentation Definition Phase Presentation Presentation Presentation Presentation Presentation Presentation Presentation Presentation Presentation Presentation Presentation Presentation Presentation Presentation Presentation Presentation Presentation Presentation Presentation Presentation Presentation (type) (type) (type) (type) (type) (type) (type) (type) (type) (type) (type) (type) (type) (type) (type) (type) (type) (type) (type) (type) (type) Presentation (type) Sample Phase Processing Phase Enroll Phase Match Phase Day Range Week Range Month Range Statistical Summary of one week range Statistical Summary of one month range Year Range Life DD/MM//YY YY FTD FTD FTD Sample FTX FTD DI Sample SPS FTE FTD DI FTD Statistical Summary of one day range Sample Sample SPS SPS Enroll Sample SPS NA Match Sample SPS NA Match NA FTM FTD DI DI FTD FTD DD/MM//YYYY Sample CI Statistical Summary of one day range DI Sample SPS NA Match Statistical Summary of Statistical Summary of one year range life of user or system BIOMETRIC DURATION SCALE MODEL – EXAMPLE STEP 2 Presentation Definition Phase Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Sample Phase FTD FTD FTD Sample FTD DI Sample FTD DI FTD Sample Sample FTD DI Sample DI FTD Sample FTD Sample DI Sample Processing Phase Enroll Phase Match Phase Day Range Week Range Month Range Summary of one week range Summary of one month range Year Range Life DD/MM//YYYY FTX FTX SPS SPS Summary of one day range Enroll "Scores" NA FTM SPS NA Match SPS NA Match DD/MM//YYYY CI SPS NA Match Summary of one day range Summary of one year Summary of life of range user or system BIOMETRIC DURATION SCALE MODEL – EXAMPLE STEP 3 Presentation Definition Phase Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Sample Phase FTD FTD FTD Sample FTD DI Sample FTD DI FTD Sample Sample FTD DI Sample DI FTD Sample FTD Sample DI Sample DI FTD Sample Sample FTD FTD Sample Processing Phase Enroll Phase Match Phase Day Range Week Range Month Range Year Range Life Summary of one month range Summary of one year range Summary of life of user or system DD/MM//YYYY FTX FTX Summary of one day range SPS SPS Enroll "Scores" NA SPS NA Match SPS NA Match FTM Summary of one week range DD/MM//YYYY CI SPS NA Match Summary of one day range DD/MM//YYYY FTX SPS NA Match SPS NA Match Summary of one day range BIOMETRIC DURATION SCALE MODEL – EXAMPLE STEP 3 Presentation Definition Phase Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Sample Phase FTD FTD FTD Sample FTD DI Sample FTD DI FTD Sample Sample FTD DI Sample DI FTD Sample FTD Sample DI Sample DI FTD Sample Sample FTD FTD Sample Processing Phase Enroll Phase Match Phase Day Range Week Range Month Range Year Range Life Summary of one month range Summary of one year range Summary of life of user or system DD/MM//YYYY FTX FTX Summary of one day range SPS SPS Enroll "Scores" NA SPS NA Match SPS NA Match FTM Summary of one week range DD/MM//YYYY CI SPS NA Match Summary of one day range DD/MM//YYYY FTX SPS NA Match SPS NA Match Summary of one day range BIOMETRIC DURATION SCALE MODEL – EXAMPLE STEP 4 Presentation Definition Phase Sample Phase Presentation (type) FTD Presentation (type) FTD Presentation (type) FTD Presentation (type) Presentation (type) FTD Presentation (type) DI Presentation (type) Presentation (type) Processing Phase Enroll Phase Match Phase DI Presentation (type) FTD Sample FTX Sample FTX Sample SPS Sample SPS NA Sample SPS NA Match Sample SPS NA Match Presentation (type) FTD Presentation (type) DI Presentation (type) Presentation (type) DI Presentation (type) FTD Presentation (type) Life Summary of one month range Summary of one year range Summary of life of user or system Enroll "Scores" FTM FTD Presentation (type) CI Summary of one day range DI Sample Presentation (type) DI Presentation (type) FTD SPS NA Match DD/MM//YYYY Presentation (type) Sample Presentation (type) Sample SPS NA Presentation (type) Sample SPS NA Presentation (type) Sample SPS NA FTM SPS NA FTM SPS NA Presentation (type) FTD Presentation (type) FTD Summary of one week range DD/MM//YYYY Sample Presentation (type) FTX Match Summary of one day range Match DD/MM//YYYY FTD Presentation (type) Sample Presentation (type) Sample Summary of one day range FTX Presentation (type) Presentation (type) Year Range Summary of one day range Presentation (type) Presentation (type) Month Range DD/MM//YYYY Presentation (type) Presentation (type) Week Range FTD Presentation (type) Presentation (type) Day Range Sample Match Summary of one week range BIOMETRIC DURATION SCALE MODEL – EXAMPLE STEP 4 Presentation Definition Phase Sample Phase Presentation (type) FTD Presentation (type) FTD Presentation (type) FTD Presentation (type) Presentation (type) FTD Presentation (type) DI Presentation (type) Presentation (type) DI Presentation (type) FTD Presentation (type) Presentation (type) Presentation (type) FTD Presentation (type) DI Presentation (type) Presentation (type) DI Presentation (type) FTD Presentation (type) Match Phase Sample FTX Sample FTX SPS Sample SPS NA Sample SPS NA Match Sample SPS NA Match Month Range Year Range Life Summary of one month range Summary of one year range Summary of life of user or system Enroll "Scores" FTM CI Sample DI Presentation (type) FTD Summary of one day range SPS NA Match DD/MM//YYYY Presentation (type) Sample Presentation (type) Sample SPS NA Presentation (type) Sample SPS NA Presentation (type) Sample SPS NA FTM SPS NA FTM SPS NA Presentation (type) FTD Presentation (type) FTD Summary of one week range DD/MM//YYYY Sample Presentation (type) FTX Match Summary of one day range Match DD/MM//YYYY FTD Presentation (type) Sample Presentation (type) Sample Summary of one day range FTX Presentation (type) Presentation (type) Week Range Summary of one day range Sample DI Presentation (type) Presentation (type) Day Range DD/MM//YYYY FTD Presentation (type) Presentation (type) Enroll Phase FTD Presentation (type) Presentation (type) Processing Phase Sample Match Summary of one week range BIOMETRIC DURATION SCALE MODEL – EXAMPLE STEP 5 Presentation Definition Phase Sample Phase Presentation (type) FTD Presentation (type) FTD Presentation (type) FTD Presentation (type) Sample Presentation (type) FTD Presentation (type) DI Presentation (type) Presentation (type) Sample Processing Phase Enroll Phase Match Phase DI Presentation (type) FTD SPS Sample SPS NA SPS NA Match SPS NA Match Presentation (type) FTD Presentation (type) DI Presentation (type) Sample Presentation (type) DI Presentation (type) FTD Presentation (type) Sample Summary of one month range Summary of one year range Summary of life of user or system Enroll "Scores" FTM FTD Presentation (type) Sample Sample Presentation (type) DI Presentation (type) FTD Summary of one week range DD/MM//YYYY CI Summary of one day range DI Presentation (type) SPS NA Match DD/MM//YYYY Presentation (type) Sample Presentation (type) Sample Presentation (type) FTD Presentation (type) FTD FTX SPS NA Match Presentation (type) Sample SPS NA Presentation (type) Sample SPS NA FTM SPS NA FTM SPS NA Summary of one day range Match DD/MM//YYYY FTD Presentation (type) Sample Presentation (type) Sample Summary of one day range FTX Summary of one week range Presentation (type) Presentation (type) Sample Presentation (type) Sample Presentation (type) Life Summary of one day range Sample Presentation (type) Year Range FTX Presentation (type) Presentation (type) Month Range FTX Presentation (type) Presentation (type) Week Range FTD Presentation (type) Presentation (type) Day Range DD/MM//YYYY Match FI DD/MM/YYYY FTD Sample SPS NA Match Summary of one day Summary of one week range range BIOMETRIC DURATION SCALE MODEL – EXAMPLE STEP 5 Presentation Definition Phase Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Sample Phase Processing Phase Enroll Phase Match Phase FTD FTD FTD Day Range Week Range Month Range Year Range Life Summary of one month range Summary of one year range Summary of life of user or system DD/MM//YYYY Sample FTX Sample FTX FTD DI FTD DI FTD Summary of one day range Sample Sample SPS SPS Enroll "Scores" Sample SPS NA Match Sample SPS NA Match NA FTM FTD DI Summary of one week range DI FTD FTD DD/MM//YYYY Sample CI Summary of one day range DI Sample SPS NA Match DI FTD DD/MM//YYYY Sample Sample FTX SPS NA Match SPS SPS NA NA FTM SPS NA FTM SPS NA FTD FTD Sample Sample Summary of one day range Match DD/MM//YYYY FTD Sample Sample FTX Sample Sample FI Summary of one day range Match FTD Sample SPS NA Summary of one week range Match DD/MM/YYYY Summary of one day range Summary of one week range BIOMETRIC DURATION SCALE MODEL – EXAMPLE STEP 6 Presentation Definition Phase Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Sample Phase Processing Phase Enroll Phase Match Phase FTD FTD FTD Day Range Week Range Month Range Year Range Life Summary of one year range Summary of life of user or system DD/MM//YYYY Sample FTX Sample FTX FTD DI FTD DI FTD Summary of one day range Sample Sample SPS SPS Enroll "Scores" Sample SPS NA Match Sample SPS NA Match NA FTM FTD DI Summary of one week range DI FTD FTD Summary of one month range DD/MM//YYYY Sample CI Summary of one day range DI Sample SPS NA Match DI FTD DD/MM//YYYY Sample Sample FTX SPS NA Match SPS SPS NA NA FTM SPS NA FTM SPS NA FTD FTD Sample Sample Summary of one day range Match DD/MM//YYYY FTD Sample Sample FTX Sample Sample FI Summary of one day range Match FTD Sample SPS NA Match FTD DI DD/MM/YYYY Summary of one day range DD/MM/YYYY Sample SPS NA Summary of one week range Match Summary of one day range Summary of one week range Summary of one week range Summary of one month range BIOMETRIC DURATION SCALE MODEL – EXAMPLE STEP 6 Presentation Definition Phase Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Sample Phase Processing Phase Enroll Phase Match Phase FTD FTD FTD Day Range Week Range Month Range Year Range Life Summary of one year range Summary of life of user or system DD/MM//YYYY Sample FTX Sample FTX FTD DI FTD DI FTD Summary of one day range Sample Sample SPS SPS Enroll "Scores" Sample SPS NA Match Sample SPS NA Match NA FTM FTD DI Summary of one week range DI FTD FTD Summary of one month range DD/MM//YYYY Sample CI DI Sample SPS NA Match DI FTD Summary of one day range DD/MM//YYYY Sample Sample FTX SPS NA Match SPS SPS NA NA FTM SPS NA FTM SPS NA FTD FTD Sample Sample Summary of one day range Match DD/MM//YYYY FTD Sample Sample FTX Sample Sample FI Summary of one day range Match Match DD/MM/YYYY Summary of one day range Match DD/MM/YYYY Summary of one day range FTD Sample SPS NA FTD DI Sample SPS NA Summary of one week range Summary of one week range Summary of one week range Summary of one month range BIOMETRIC DURATION SCALE MODEL – EXAMPLE STEP 7 Presentation Definition Phase Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Sample Phase Processing Phase Enroll Phase Match Phase FTD FTD FTD Day Range Week Range Month Range Year Range Life DD/MM//YYYY Sample FTX Sample FTX FTD DI FTD DI FTD Summary of one day range Sample Sample SPS SPS Enroll "Scores" Sample SPS NA Match Sample SPS NA Match NA FTM FTD DI Summary of one week range DI FTD FTD Summary of one month range DD/MM//YYYY Sample CI DI Sample SPS NA Match DI FTD Summary of one day range Summary of one year range Summary of life of user or system DD/MM//YYYY Sample Sample FTX SPS NA Match SPS SPS NA NA FTM SPS NA FTM SPS NA FTD FTD Sample Sample Summary of one day range Match DD/MM//YYYY FTD Sample Sample FTX Sample Sample FI Summary of one day range Summary of one week range Match FTD Sample SPS NA Match Sample SPS NA Match Sample SPS NA Match Sample SPS NA Match FTD DI FTD DI DD/MM/YYYY Summary of one day range DD/MM/YYYY Summary of one day range DD/MM/YYYY Summary of one day range Summary of one week range Summary of one week range Summary of one month range Summary of one week range Summary of one month range Summary of one year range BIOMETRIC DURATION SCALE MODEL – EXAMPLE STEP 7 Presentation Definition Phase Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Presentation (type) Sample Phase Processing Phase Enroll Phase Match Phase FTD FTD FTD Day Range Week Range Month Range Year Range Life DD/MM//YYYY Sample FTX Sample FTX FTD DI FTD DI FTD Summary of one day range Sample Sample SPS SPS Enroll "Scores" Sample SPS NA Match Sample SPS NA Match NA FTM FTD DI Summary of one week range DI FTD FTD Summary of one month range DD/MM//YYYY Sample CI DI Sample SPS NA Match DI FTD Summary of one day range Summary of one year range Summary of life of user or system DD/MM//YYYY Sample Sample FTX SPS NA Match SPS SPS NA NA FTM SPS NA FTM SPS NA FTD FTD Sample Sample Summary of one day range Match DD/MM//YYYY FTD Sample Sample FTX Sample Sample FI Summary of one day range Match Match DD/MM/YYYY Summary of one day range DD/MM/YYYY Summary of one day range FTD Sample SPS NA Summary of one week range FTD DI Sample SPS NA Match Sample SPS NA Match Sample SPS NA Match FTD Summary of one week range Summary of one week range Summary of one month range Summary of one week range Summary of one month range DD/MM/YYYY DI Summary of one day range Summary of one year range BIOMETRIC DURATION SCALE MODEL – COMMON REPORTING • 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. BIOMETRIC DURATION SCALE MODEL – 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). BIOMETRIC DURATION SCALE MODEL – COMMON REPORTING • Common reporting sentence structuring should 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-mmddThh:mm:ss.sssZ/PYYMMWWDDTHHHMMMSSS). BIOMETRIC DURATION SCALE MODEL – COMMON REPORTING • Common reporting sentence structuring should 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). CONCLUSIONS • 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. REFERENCES • [1] “Time.” [Online]. Available: http://www.merriam-webster.com/dictionary/time. [Accessed: • 28-Jan-2015]. • [2] “Time.” [Online]. Available: http://www.yourdictionary.com/time. [Accessed: 28-Jan2015]. • • [3] F. Weil, “The Meaning of Time,” 2013. [Online]. Available: http://www.huffingtonpost.com/frank-a-weil/the-meaning-of-time_b_4351464.html. [Accessed: 28-Jan-2015]. [12] J. L. Wayman, “A Generalized Biometric Identification System Model,” Signals, Syst. amp; Computer. 1997, Conf. Rec. Thirty-First Asilomar Conf., vol. 1, pp. 291–295, 1998. • [13] S. J. Elliott, D. Phd, and E. P. Kukula, “A Definitional Framework for the HumanBiometric Sensor Interaction Model,” pp. 1–6, 2009. • [4] J. O’Connor and E. Robertson, “A History of Time: Classical Time,” School of Mathematics and Statistics University of St. Andrews, Scotland, 2002. [Online]. Available: http://www-history.mcs.st-and.ac.uk/HistTopics/Time_1.html. • [14] T. Dunstone and N. Yager, “Definitions,” in Biometric System and Data Analysis, T. Dunstone and N. Yager, Eds. Springer, 2009, pp. 99–108. • • [5] G. Moyer, “The Gregorian Calendar,” Sci. Am., vol. 246, no. 5, pp. 144–152, May 1982. • [15] Hicklin, A., Watson, C., & Ulery, B. “The Myth of Goats : How many people have fingerprints that are hard to match?” US Department of Commerce, National Institute of Standards and Technology, pp. 1–24, 2005. • • [7] S. A. Diddams, J. C. Bergquist, S. R. Jefferts, and C. W. Oates, “Standards of Time and Frequency at the Outset of the 21st Century,” vol. 306, no. November, pp. 1318–1324, 2004. • [16] Baker, S. E., Bowyer, K. W., Flynn, P. J., & Phillips, P. J. “Template Aging in Iris Biometrics : Evidence of Increased False Reject Rate in ICE 2006.” In Handbook of Iris Recognition, Springer, pp. 205–218, 2013. [6] A. Emmerson, “Things are Seldom what they Seem - Christiaan Huygens, the Pendulum, and the Cycloid,” Horol. Sci. Newsletter., pp. 2–32, 2001. • [8] E. Bullard and H. Jolly, “Gravity Measurements in Great Britian,” Geophysics. J. Int., vol. 3, no. 9, pp. 443–477, 1936. • [9] S. J. Elliott, E. P. Kakula, and R. T. 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