(Fall 2012) Compression VS Performance in Fingerprint Images
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The purpose of this study is to evaluate the performance of fingerprint image matching based on various compression levels. The fingerprint images were taken from a Purdue University database and each were compressed to various Quality of Compression levels (QoC 99, QoC 90, QoC 80, QoC 70, QoC 50, QoC 25, QoC 15, QoC 10, QoC 5, QoC 1) using XnConvert. The compressed images were then run through Megamatcher and Oxford Wave to determine the Equal Error Rate (EER).
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This research focused on classifying Human-Biometric Sensor Interaction errors in real-time. The Kinect 2 was used as a measuring device to track the position and movements of the subject through a simulated border control environment. Knowing, in detail, the state of the subject ensures that the human element of the HBSI model is analyzed accurately. A network connection was established with the iris device to know the state of the sensor and biometric system elements of the model. Information such as detection rate, extraction rate, quality, capture type, and more metrics was available for use in classifying HBSI errors. A Federal Inspection Station (FIS) booth was constructed to simulate a U.S. border control setting in an International airport. The subjects were taken through the process of capturing iris and fingerprint samples in an immigration setting. If errors occurred, the Kinect 2 program would classify the error and saved these for further analysis.
IT 34500
IT 34500 is an undergraduate course offered to Purdue West Lafayette students. The course gives an introduction into biometrics and automatic identification and data capture technologies
An Investigation into Biometric Signature Capture Device Performance and User...
An Investigation into Biometric Signature Capture Device Performance and User...International Center for Biometric Research
The human signature provides a natural and publically-accepted legally-admissible method for providing authentication to a process. Automatic biometric signature systems assess both the drawn image and the temporal aspects of signature construction, providing enhanced verification rates over and above conventional outcome assessment. To enable the capture of these constructional data requires the use of specialist ‘tablet’ devices. In this paper we explore the enrolment performance using a range of common signature capture devices and investigate the reasons behind user preference. The results show that writing feedback and familiarity with conventional ‘paper and pen’ donation configurations are the primary motivation for user preference. These results inform the choice of signature device from both technical performance and user acceptance viewpoints.Entropy of Fingerprints
The inherent differences between secret-based authentication (such as passwords and PINs) and biometric authentication have left gaps in the credibility of biometrics. These gaps are due, in large part, to the inability to adequately cross-compare the two types of authentication. This paper provides a comparison between the two types of authentication by equating biometric entropy in the same way entropy of secrets are represented. Similar to the method used by Ratha, Connell, and Bolle [1], the x and y dimensions of the fingerprints were examined to determine all possible locations of minutiae. These locations were then examined based on the observed probability of minutiae occurring in each of the designated locations. The results of this work show statistically significant differences in the frequencies and probabilities of occurrence for minutiae location, type, and angle, across all possible minutiae locations. These components were applied to Shannon’s Information Theory [2] to determine the entropy of fingerprint biometrics, which was estimated to be equivalent to an 8.3-character, randomly chosen password
Biometric and usability
This course covers biometric usability testing with a focus on border control and mobile devices. The course objectives are to understand biometric systems, how people use them, testing methodologies, limitations, and research methods. Topics include genuine users, usability, attacks, border security, tokens, qualitative/quantitative research, and focus groups. Students will complete a research-based group project, assignments, and quizzes. The course uses lectures, discussions, guest speakers and students are expected to regularly attend and complete all work.
Examining Intra-Visit Iris Stability - Visit 4
This document examines the stability of iris recognition over short periods of time. It analyzes iris scan data from 60 participants in a single visit lasting 10 minutes or less. The stability of each iris is measured using a stability score index. Statistical analysis finds no significant difference in stability scores between age groups, gender, or ethnicity. This suggests the iris remains stable within a single visit. Future work could examine stability over longer time periods and whether it decreases with more extended testing.
Examining Intra-Visit Iris Stability - Visit 6
In this research, intra-visit match score stability was examined for the human iris. Scores were found to be statistically stable in this short time frame.
Examining Intra-Visit Iris Stability - Visit 2
In this research, intra-visit match score stability was examined for the human iris. Scores were found to be statistically stable in this short time frame.
Examining Intra-Visit Iris Stability - Visit 1
In this research, intra-visit match score stability was examined for the human iris. Scores were found to be statistically stable in this short time frame.
Examining Intra-Visit Iris Stability - Visit 3
In this research, intra-visit match score stability was examined for the human iris. Scores were found to be statistically stable in this short time frame.
Best Practices in Reporting Time Duration in Biometrics
A lot of work done in Center recently has focused around different topics concerning "time". Iris stability across different "times" has been in the forefront due to work in the undergraduate class, IT345, the graduate class IT545, as well as work in Ben Petry's thesis. Of course "time" is a fairly inaccurate word to use. Assessing stability over time is very ambiguous to the research question. For example time may mean millisecond, months, years, or even life of the user. Upon further examination of other academic literature, the reporting of research duration, collection interval, and specific time frame of interest are sporadic at best and missing completely at worst. To solve this issue, the Center has created the biometric duration scale (BDS) model with associated suggested best practices for reporting time duration in biometrics.
The BDS model marries the general biometric model with HBSI model to create a logical flow of five phases: the presentation definition phase, sample phase, processing phase, and enrollment or matching phase. By tracking information through this progression such as specific subject presentations made, HBSI error, and FTE/Enrollment score (to name a few), performance within the general biometric model can be examined. The BDS model goes one step further by creating specific durations to report research specific metrics. By creating this model, outcomes that effect a yearly performance metrics can be looked at by examining monthly performance, daily performance, or even specific user presentations and how those subcomponents effect the whole system.
Additionally, best practices for the reporting of duration is also included. The reporting methodology stems from ISO 8601 and is in compliance with ISO 21920. In the common reporting structure, start date, duration, number of visits at how many intervals, and time scope of interest for the specific research are given in a logical, readily available format along with the very specific, detailed ISO 8601 methodology. The goal of creating a formal script for reporting research duration was to eliminate ambiguity and create an environment where replication and drawing parallels between research is encouraged.
Examining Intra-Visit Iris Stability - Visit 5
The document examines the stability of iris recognition over a short period of time. It discusses how iris recognition works and why the iris is considered unique and stable over time. The research presented in the document analyzed iris image data collected over four weekly visits. The results showed no statistically significant difference in iris matching scores between the different visits, suggesting the iris is stable over a short time period. This supports the idea that the iris can be used for biometric identification applications that require stability over time.
Standards and Academia
ICBR has been involved in standards development for over 14 years through committees like INCITS M1 and ISO/IEC JTC1 SC37. To provide students real-world experience, students participated on these committees by submitting documents, comments, and reviews. This engagement between academia and standards development benefits both fields by allowing applied research and education in new and emerging technical areas.
Interoperability and the Stability Score Index
The stability score index, conceptualized in 2013, was designed to address the weaknesses of the zoo menagerie and other performance metrics by quantifying the relative stability of a user from on condition to another. In this paper, the measure of interoperability is the stability score from enrolling on one sensor and verifying on multiple sensors. The results showed that like performance, individual performance were not stable across these sensors. When examining stability by sensor family (capacitance, optical and thermal) we find that capacitive as the enrollment sensor were the least stable. Both enrolling and verifying on a thermal sensor, individuals were the most stable of the three family types. With respect to interaction type, enrolling on touch and verifying on swipe was more stable than enrolling on swipe and verifying on swipe, which was an interesting finding. Individuals using the thermal sensor generated the most stable stability scores.
Advances in testing and evaluation using Human-Biometric sensor interaction m...
Advances in testing and evaluation using Human-Biometric sensor interaction m...International Center for Biometric Research
This document discusses advances in testing and evaluating human-biometric sensor interaction using a new model. It describes gaps in traditional biometric testing, such as how users interact with systems. A new Human Biometric Sensor Interaction model is presented and has been tested on iris and fingerprint biometrics. The model has been expanded to more complex systems like border gates. Testing looks at how users interact with biometric systems in different environments and factors like throughput. The goal is to better test and evaluate systems without overburdening test facilities.Cerias talk on testing and evaluation
This document discusses biometric testing and evaluation. It covers traditional biometric algorithm testing and more complex operational testing. There are gaps in areas like training, accessibility, human factors, and determining what causes errors. Filling these gaps is an ongoing work in progress as biometric devices become more complex and deployed in more environments and applications. Different types of testing include technology, scenario, and operational evaluations to adequately assess performance and usability.
IT 54500 overview
This course provides an overview of biometric technology as it relates to security, access control, and authentication. It examines basic biometric terminology and various biometric modalities such as fingerprint, face, and iris recognition. Students will learn about biometric data evaluation and interpretation, standards, integration, and challenges. The course is divided into fundamental, modality, integration, and research building blocks to cover topics like identification, matching, fusion, standards, and interoperability.
Ben thesis slideshow
This document outlines the structure and goals of a research study on the stability of iris recognition match scores over time. It introduces the problem statement around the lack of quantification of match score stability, and previews the research question, significance, purpose and scope, assumptions, limitations, and delimitations that will be discussed in the following chapters which focus on the literature review, methodology, results, and conclusions of the study.
ICBR Databases
This is a preview of the databases we use in the Center. The presentation overviews our data collection GUI, data storage (datawarehouse), and our project management database. Each of these databases work together to allow us to efficiently run our operations.
Understanding Fingerprint Skin Characteristics and Image Quality
Understanding Fingerprint Skin Characteristics and Image QualityInternational Center for Biometric Research
This document summarizes research examining the relationship between fingerprint skin characteristics (moisture, oiliness, elasticity, temperature) and image quality. Three datasets were collected from participants using different fingerprint sensors and skin analysis devices. Correlation analyses were conducted to determine relationships between the skin characteristics and image quality, as well as between characteristics. Preliminary results found slight correlations between some characteristics and quality, but inconsistencies between datasets. The research aims to determine if collecting skin data improves image quality.More from International Center for Biometric Research (20)
An Investigation into Biometric Signature Capture Device Performance and User...
An Investigation into Biometric Signature Capture Device Performance and User...
Best Practices in Reporting Time Duration in Biometrics
Best Practices in Reporting Time Duration in Biometrics
Advances in testing and evaluation using Human-Biometric sensor interaction m...
Advances in testing and evaluation using Human-Biometric sensor interaction m...
Understanding Fingerprint Skin Characteristics and Image Quality
Understanding Fingerprint Skin Characteristics and Image Quality
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(Fall 2012) Compression VS Performance in Fingerprint Images
- 1. COMPRESSION VS. PERFORMANCE IN FINGERPRINT IMAGES The purpose of this study is to evaluate the performance of fingerprint image matching based on various compression levels. The fingerprint images were taken from a Purdue University database and each were compressed to various Quality of Compression levels (QoC 99, QoC 90, QoC 80, QoC 70, QoC 50, QoC 25, QoC 15, QoC 10, QoC 5, QoC 1) using XnConvert. The compressed images were then run through Megamatcher and Oxford Wave to determine the Equal Error Rate (EER). Faisal Al-Khalidi, Troy Davis, Christopher Guntz, Michael Brockly, Stephen Elliott Overview Methodology • Gather right index fingerprint images from a thermal swipe sensor • Compress the images to ten different quality levels formatted as .jpg • Run each compressed image set through Megamatcher to obtain genuine and impostor match scores at each compression level • Process the data in Oxford Wave to produce readable performance graphs and obtain EER’s • Analyze the results from the graphs Compression SizesEERs of Compression Levels Fingerprint Comparison Observations • We noticed that the EER, our measure of performance, varied between each compression level. There was little observable trend between compression level and EER, as the EER values varied between 0.1861% and 0.2289% across all compression levels. • Uncompressed had the largest file size and QoC1 had the smallest file size • Unexpectedly, the results do not appear to be linear. One example is that QoC15 has a lower EER than QoC99. This may suggest that the average file size is not the best predictor of performance. • Furthermore, the EER range varied only 0.0698%. This suggests that compression may not have the impact that literature we reviewed suggested. Possible reasons could be that the compression method chosen does create much fragmenting or distortion when compression is applied *EER: Equal Error Rate: The acceptance rate at which False Acceptance Rate and False Rejection Rate are equal. A lower EER is considered to have better performance QoC 100 QoC 99 QoC 90 QoC 80 QoC 70 QoC 50 QoC 25 QoC 15 QoC 10 QoC 5 QoC 1 EER at QoC 0.1591 0.216 0.1882 0.1727 0.2124 0.2195 0.1963 0.1861 0.2093 0.2223 0.2289 0.12 0.14 0.16 0.18 0.2 0.22 0.24 EqualErrorRate EER at Quality of Compression