(2013) Examination of Stability in Fingerprint Recognition Across Force Levels

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Kevin O'Connor's master's thesis presentation for Purdue University.

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  • {"49":"Instability can occur within a classification at different force levels. An individual is capable of moving ¼ of the maximum possible movement and remain in the same classification. \n","16":"-Red shaded areas change\n-means lower impostor score\n","55":"007\n0.0086 \n0.2959 \n0.1936\n0.0210 \n259\n0.1469 \n0.0110 \n0.1572 \n0.1301 \n","22":"- authors have tended to ignore instability in the normal classification. \nYager and Dunstone (2010) ignore the normal classification, referred to in their papers as the “none” classification. \nThe normal classification comprises the majority of individuals across all force levels, which creates the opportunity for the individual to move significantly without changing classification. Thus it is an important classification to examine.\nnormal classification of individuals lies in the 2nd quartile of at least one of the two performance metrics (genuine or impostor) in the dataset. \nnormal classification comprises the majority of the zoo plot, there can be some instability within this classification. \n","11":"Force collected randomly on 5 force levels 5, 7, 9, 11, and 13\n","50":"individual 34 moving within the chameleon classification. \nThe star represents the individual’s coordinates on the 7 N zoo plot. The arrow points to the coordinates on the 9 N zoo plot, which results in a stability score of 0.1296.\n","39":"normal\n","28":"- Individual 34 was classified as a chameleon across all five force levels. \nGenuine and impostor scores differ between the force levels for individual 34, but remain in the same classification. \nThis illustrates instability within the same animal classification. \n","17":"In Figure 4.4, the number of individuals in each animal classification increases or stays the same compared to the previous force levels (5 N and 7 N). Table 4.5 shows the classification data at 9 N.\n","45":"The existence of instability has been proven along with the weaknesses of the zoo menagerie plots. \nThe proposed method of being able to calculate the instability of an individual can better showcase how an individual performs on a particular biometric system. \nThe Euclidean distance formula was used in order to calculate the change of performance from one force level to the next. \nThe stability score index ranges from 0 to 1. Zero would indicate perfect stability from one zoo plot to another and 1 would indicate the maximum possible movement possible. To provide reference to this scoring methodology, the previous cases are examined.\n","34":"- The most drastic case of instability examined was the change in animal classifications. \n- Individual 117 was highlighted movement in different animal classifications. \n- First couple are classified as normal. \n- 9N and 11N the existence of inter-animal instability was proven. \n","1":"[Title]\nUsed the zoo menagerie and zoo plots that will be revisited in order to understand\nNeeds to be understood as most of my results use the graphs or are based off of them\n- It can be a bit confusing to understand so I want to revisit\n","51":"individual 117, whose classification changes from a dove to a phantom at different force levels. \n","40":"Within the zoo plots, cut-off values are visible by the shaded (red) areas for each classification. \nSome individuals miss a classification by a marginal amount as they are adjacent to the border. \nThe issue with borderline cases is they can be stable but do not reflect the characteristics of the animal classification to which they are assigned well. \n","18":"The results in Figure 4.5 show only one individual classified as a worm, 135RI. \nIn the previous three force levels, individual 135RI was classified as normal in the zoo plots.\n","46":"individual 135 was examined for instability within the normal classification. The zoo plots are shown to demonstrate how the stability score index is conceptualized. The stability score and related coordinates for the 5 N and 7 N levels for individual 135 \n","13":"154 Subjects reported gender along with having all digits on all force levels\nMale\n81\nFemale\n73\n","2":"With this slide I want to try and paint the overall picture of how I got my idea, as well as why I am doing this.\nDHS funded us for a data collection we did back in 2009 where we looked at fingerprint quality over varying force levels.\nDr Elliott, Dr. Kukula, and Dr. Modi were able to obtain a patent so you know this research was important in the eyes of the university as well as the US Patent Office\nwanted us to examine a sort of new performance analysis measurement called the biometric zoo menagerie\nzoo- a way of classifying individuals based upon how well they are matched to themselves and others in their specific dataset\nRoc curves- most used performance analysis tools- How well the system is performing as a whole\nWEAKENSSES- just a screenshot of performance\nKevin – I am trying to paint a picture for the committee, especially Dr Dyrenfurth so he can wrap his brain around it. He will get it, but you need to “tell a story” –so think 646 presentation with a bit of introduction. Dr. Sutton in my discussion with him wanted to have a “problem”, “why it is important”, and “impact” style slides, and I have tried to build this up here. Feel free to change anything here – as this is your day to shine. \nFor this slide – I thought it would be interesting to say – we have done the preliminary research – you can say you helped with this research. This gives them the impact – as if DHS has already funded preliminary work then they must think it is important. If earlier researchers got a patent on force measurements, then the university must have thought it would be important, as well as US Patent Office. I am not trying to “toot” my own horn here and show off that I have a patent, so if you want to remove it I wont be offended . I kind of put in your own words – so feel free to change, why you decided to look at this in more detail – the third and fourth indented bullets I thought would be something that you could adjust. It sets the scene on why you are interested in this, and why you have a vested interest in completing this research. \n","52":"individual 117, whose classification changes from a dove to a phantom at different force levels. For individual 117, both the zoo plots and the stability score reflect a high level of instability. \n","41":"Individual 172 has an impostor score of 9.0675, and 140 has an impostor score of 9.0661, a difference of .0014.\nIf these individuals were to take each other’s impostor scores at the next force level they would change classifications, which would not be the case if they are moving an insignificant amount. \n","58":"lousy fingerprints – how long should we take to get good images. \ngreencard fingerprints 3 times. What is the cost? What if I had a stability score – and then they would know. \nAt Global Entry a person could have a score? Is a score representative of the subject. These are what I want to investigate. \n","14":"Additional question to provide evidence for instability\n1st question answered within zoo plots\n2nd question answered in individual cases\n","53":"subject 178 was examined due to their ability to have similar performance across force levels, but classified differently. This weakness of the zoo is ignored when using the stability score index\n","42":"- Not all subjects fall into the instability cases. An example of an individual that has small deviations in instability is provided within this section. \nNo subjects were able to obtain the same genuine and impostor scores across force levels but some had significantly smaller movements in the zoo plots \nThe weakness by just examining the animal classification of this particular case is it would appear to have an unstable performance, due to being classified differently.\nIndividual 178 is relatively stable and can be shown later with a stability score.\n","31":"- Big jump from 7N to 9N\n- later examined with SSI\n","20":"change in the counts shown in the table and the shifts on the zoo plots supports the presence of instability in the dataset.\n-5n and 7n dove counts the same but not the same subjects\n","9":"Zoo Plots used for my analysis\nPoint of classifications (red shaded areas show the limits for each animal)\n","48":"- a star shows the placement of individual 135 on the 5 N force level. This shows the instability established earlier. \n- Score obtained from Euclidean distance divided by the maximum movement gives a stability score index of 0.3512\n","15":"distribution of the 5 N zoo plot. \nThis is the baseline data used for the stability scores:\nMinimum Genuine (X-axis): 44\nMaximum Genuine (X-axis): 1950\nMinimum Impostor (Y-axis): 2.4\nMaximum Impostor (Y-axis): 10.3\nThere is a dispersed population across the impostor and genuine scores, varying for each classification. \n","4":"Before I go into Results, I want to revisit some key definitions and terms\nMost of what you will see will involve the zoo plots\nWant to make sure this is understood before going into numbers\n","54":"small deviation from 7N to 9N. Regardless of how the zoo plots classified subject 178, they will still result in a stability score closer to zero, indicating stability. Inserting the coordinates into the formula, a stability score of 0.0308 was obtained. \n","21":"- different cases of instability exist across the force levels for certain individuals because some move classifications and others do not. \n- illustration cases of instability of zoo plots for certain individuals that will later be quantified\n- four cases: instability in the normal classification, intra-animal instability, inter-animal instability, and also an additional case that will examine borderline situations.\n"}
  • (2013) Examination of Stability in Fingerprint Recognition Across Force Levels

    1. 1. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation EXAMINATION OF STABILITY IN FINGERPRINT RECOGNITION ACROSS FORCE LEVELS By: Kevin O’Connor
    2. 2. PRELIMINARY INVESTIGATION • Builds on previously funded research by DHS S&T Directorate – Patent issued on force concept by previous research – Zoo analysis for fingerprint performance – ROC Curves – performance analysis from a population viewpoint – I noticed that the zoo plots were different at different force levels – Wanted to know why, and then try and quantify it
    3. 3. RESEARCH QUESTION • Is there instability of individual’s performance scores in a fingerprint recognition system?
    4. 4. ZOO MENAGERIE DEFINITIONS • G= Avg genuine performance measure – H and L= High and Low, respectively • I= Avg Imposter performance measure – H and L= High and Low, respectively
    5. 5. DOVES Genuine Impostor Top 25% GH IH Bottom 25% GL IL
    6. 6. WORMS Genuine Impostor Top 25% GH IH Bottom 25% GL IL
    7. 7. CHAMELEONS Genuine Imposture Top 25% GH IH Bottom 25% GL IL
    8. 8. PHANTOMS Genuine Imposture Top 25% GH IH Bottom 25% GL IL
    9. 9. Phantoms Phantoms Worms Worms Doves Doves Chameleons Chameleons
    10. 10. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation RESULTS
    11. 11. IDENTIFICATION OF VARIABLES • Force {5N, 7N, 9N, 11N, 13N} • Animal characteristics
    12. 12. WORKFLOW: CLEAN DATA • 154 subjects distilled from 260 – – – – Missing prints Wrong placement IRB info not filled out Subject information not listed
    13. 13. GENDER REPORT Distribution of Subjects Reporting Gender Category F M 47.4% 52.6%
    14. 14. ADDITIONAL QUESTIONS • Do animal classifications change at different force levels for the same subject? • Is there a score that can be assigned an individual to provide guidance to their inherent variability?
    15. 15. 5 N RESULTS Phantoms Phantoms Worms Worms Doves Doves Chameleons Chameleons
    16. 16. 7 N RESULTS
    17. 17. 9 N RESULTS
    18. 18. 11 N RESULTS
    19. 19. 13 N RESULTS
    20. 20. ANIMAL CLASSIFICATION BREAKDOWN 5 N Count Animal Classification 7 N Count 9 N Count 11 N Count 13 N Count Chameleons 11 16 22 15 16 Doves 5 5 9 6 6 Normal 119 114 102 119 117 Phantoms 12 16 16 13 11 Worms 7 3 5 1 4 Total 154 154 154 154 154
    21. 21. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation INSTANCES OF INSTABILITY FROM ZOO PLOTS
    22. 22. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation INSTABILITY WITHIN THE NORMAL CLASSIFICATION- 135RI
    23. 23. 5N
    24. 24. 7N
    25. 25. 9N
    26. 26. 11 N
    27. 27. 13 N
    28. 28. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation INTRA-ANIMAL INSTABILITY 34RI
    29. 29. 5N
    30. 30. 7N
    31. 31. 9N
    32. 32. 11N
    33. 33. 13N
    34. 34. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation INTER-ANIMAL INSTABILITY - 117RI
    35. 35. 5N
    36. 36. 7N
    37. 37. 9N
    38. 38. 11N
    39. 39. 13N
    40. 40. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation BORDERLINE CASE
    41. 41. 13 N - INDIVIDUALS 172 AND 140
    42. 42. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation STABILITY
    43. 43. 7N – PHANTOM
    44. 44. 9N – NORMAL
    45. 45. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation STABILITY SCORE INDEX
    46. 46. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation STABILITY SCORE INDEX – 135RI
    47. 47. 5N
    48. 48. 5 N TO 7 N S.S.I = 0.35 12
    49. 49. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation STABILITY SCORE INDEX – 34RI
    50. 50. 7 N TO 9 N S.S.I= 0.129 6
    51. 51. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation STABILITY SCORE INDEX – 117RI
    52. 52. 9 N TO 11 N S.S.I= 0.5537
    53. 53. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation STABILITY SCORE INDEX – 178RI
    54. 54. 7 N TO 9 N S.S.I= 0.0308
    55. 55. SCATTERPLOT OF STABILITY SCORES FOR EACH INDIVIDUAL St abi l i t y Scor e acr oss For ce Level s 0.6 Variable 5-7N 7-9N 9-11N 11-13N St abilit y Scor e I ndex 0.5 0.4 0.3 0.2 0.1 0.0 007RI 50 100 150 Subject I D 200 259RI 250
    56. 56. CONCLUSIONS • Results show instability in the performance of individuals in the right index – Zoo plots of 5 force levels – Table Breakdown – Cases of Instability • Stability score index developed to quantify instability
    57. 57. FUTURE WORK • Further studies could observe other digits of the hand (left index, left middle, right middle, etc.) • Future research could examine other force levels • Examine other matching algorithms • Change variable (habituation, time, multiple sensors, multiple modalities)
    58. 58. RELEVANT IMPACT • I want to understand whether a subject is a “poor” performer, or whether there is something that can be done. • If someone is stable, this helps reduce the time for data collection.

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