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.

1. UNDERSTANDING FINGERPRINT
SKIN CHARACTERISTICS AND
IMAGE QUALITY
ADAM GRAHAM
STEPHEN ELLIOTT

2. OVERVIEW
• Problem Statement
• Motivation
• Literature Review
• Demographics
• Analysis

3. INTRODUCTION
•Relationship between moisture, oiliness, and
elasticity
•Relationship between individual skin
characteristics and quality

4. WHY ARE YOU DOING THIS?
• This research is being conducted to determine whether
there is a relationship between moisture, oiliness,
elasticity, and image quality.
• The relationship or lack thereof will determine whether
the skin characteristic data is worthwhile to collect.

5. MOTIVATION
• Articles have linked skin moisture, oiliness, and elasticity to
image quality but most do not have data on skin measurements
to statistically prove the interaction effects.[5][7][15][16][21][22]
• There is no methodology or consistent measure for collecting
these fingerprint skin measurements.
• Measurements are collected across different types of
devices.[2][7][14]
• Collecting poor quality data can be time consuming and
expensive. It costs about $2.00 per capture using traditional
capture stations.[1]

6. PROBLEM STATEMENT
• This research examines skin moisture, oiliness,
temperature, and elasticity and their relationship
to fingerprint fidelity image quality

7. SIGNIFICANCE
• This problem is important because collecting the skin
characteristic data is time consuming and if
unnecessary, can save that time.
• Collecting poor fingerprint data can be costly.
• Image quality affects performance therefore the best
image quality should always try to be achieved. [8]

8. SKIN STRUCTURE
Figure 1: Layers of the
skin[11]

9. PORES
• A pore is defined as a very small opening on the
surface of your skin that liquid comes out through
when you sweat. [12]
•These pore structures are what creates the
moisture on a fingerprint.

10. SEBACEOUS GLANDS
• A sebaceous gland is the organ responsible for
producing the oil content (sebum) on the skin.[18]
• Free sebaceous glands open directly onto the
skin’s surface (pg 385)[18]

11. OILINESS
• Oiliness is defined as excessively high in naturally
secreted oils. [10]

12. LITERATURE
• Senior & Bolle (2001) stated that oil on the
fingerprint often leads to poor image quality.[16]

13. LITERATURE
•Wang (2013) stated that oil on the fingerprint
often leads to poor image quality.[15]

14. LITERATURE
•Yun & Cho (2006) stated that oil on the fingerprint
often leads to poor image quality.[22]

15. MOISTURE
• Moisture is defined as liquid diffused or condensed
in a relatively small quantity. [9]

16. LITERATURE
• Kang et al. (2003) stated that when moisture is
lower, image quality will be greatly reduced rather
than when the moisture is higher.[7]

17. ELASTICITY
• Elasticity is defined as resilience, or the ability of
something to return to its original shape after it
has been manipulated. [4][13]

18. LITERATURE
•Wang (2013) also stated that elasticity can cause
distortion which leads to poor image quality.[21]
•Wang (2013) stated that too much force or too
little force also affect the image quality.[21]

19. LITERATURE
•When you age, the skin loses its elastic properties
and becomes increasingly dry (Scheidat et al.,
2011).

20. IMAGE QUALITY
• Fingerprint image quality is defined as the
measure of ridge and valley clarity and the ability
to extract the important features of the finger.[3]

21. FIDELITY IMAGE QUALITY
• Fidelity image quality is described as the degree to
which a sample is an accurate representation of its
source. [17]

22. LITERATURE
• Elliott et al. (2008) related moisture, oiliness, and
elasticity to image quality.[5]
• Elliott et al. (2008) stated that there is a
relationship between the skin characteristics and
image quality but it isn’t a linear relationship.[5]

23. LITERATURE SUMMARY
Age Moisture Elasticity Oiliness Image
Quality
Elliott et al.,
2008;
x x x x
Kang et al.,
2003;
x x
Scheidat et
al., 2011;
x x x
Senior &
Bolle, 2001;
x x
Wang, 2013; x x x
Yun & Cho,
x x
2006
Table 4: Literature review summary

24. RESEARCH QUESTIONS
• Correlation between moisture and quality
• Correlation between elasticity and quality
• Correlation between moisture and elasticity
• Correlation between elasticity and age
• Correlation between moisture and age
• Correlation between quality and age

25. RESEARCH QUESTIONS
• Correlation between moisture and oiliness
• Correlation between oiliness and quality
• Correlation between oiliness and age
• Correlation between oiliness and elasticity
• Correlation between temperature and quality
• Correlation between temperature and moisture

26. RESEARCH QUESTIONS
• Correlation between temperature and age
• Correlation between temperature and elasticity
• Correlation between temperature and oiliness
• Which variables have an effect on image quality – linear
regression

27. FINGERPRINT
•Devices
• Digital Persona UareU 4000
• Moritex MSA Pro
• Triplesense

28. DIGITAL PERSONA UareU 4000
Device Specifications
Model Number U.are.U 4000
Manufacturer digitalPersona
In-house ID 14
ScanArea 15 x 18mm
Dimensions 79 x 49 x 19mm
Compliance
FCC Class B, CE, ICES, BSMI, MIC,
USB
Communication USB 2.0
Power Supply 5.0V ±5%supplied by USB
Figure 1: Digital Persona
UareU 4000 optical
fingerprint sensor
Table 1: Specification table
for Digital Persona UareU
4000 optical fingerprint
sensor

29. MORITEX MSA PRO
Device Specifications
Model Number MSA Pro
Manufacturer Moritex
In-house ID 512
ScanArea -
Dimensions 226 x 81 x 77mm
Compliance
Communication USB 2.0
Power Supply 5.0V DC
Table 2: Specification table
for Moritex MSA Pro skin
analysis counseling system
Figure 2: Moritex MSA Pro
skin analysis counseling
system

30. TRIPLESENSE
Device Specifications
Model Number K10229
Manufacturer Schott
In-house ID 486
ScanArea
Dimensions 63 x 54.6 x 157.3mm
Compliance
Communication USB 2.0
Power Supply 2xAAA BatteryOperated
Table 3: Specification table
for Triplesense skin analysis
sensor
Figure 3: Triplesense skin
analysis sensor

31. DESCRIPTION OF DATASETS

32. DATASET 1
• 70 participants
• Participants were asked for their demographic information after completing
the detailed consent form.
• Skin characteristics were collected next using the Triplesense device.
• Participants were given a practice session on how to use the fingerprint sensor
and then asked to present their dominant index finger on the device.
• 21 images were collected from the participant.

33. DEMOGRAPHICS

34. AGE
Figure 4: Age breakdown for Dataset 11
[1] Datarun 1456

35. GENDER
[1] Datarun 1456
Figure 5: Gender breakdown for Dataset 11

36. ETHNICITY
[1] Datarun 1456
Figure 6: Ethnicity breakdown for Dataset 11

37. DATASET 2
• 188 subjects
• Participants were asked for their demographic information after completing the detailed consent form.
• Skin characteristics were collected next using the Triplesense device.
• Participants were asked to present their dominant index finger on the first device from a pre-randomized
order of devices. Peak pressure was also recorded while interacting with the sensor using a pressure
measuring device.
• The participant then had their skin characteristics collected again and proceeded to the remaining devices,
having their skin characteristics measured before using each device.
• The data collection concluded after all devices had been used.

38. DEMOGRAPHICS

39. AGE
[1] Datarun 1457
Figure 7: Age breakdown for Dataset 21

40. GENDER
[1] Datarun 1457
Figure 8: Gender breakdown for Dataset 21

41. • DHS2012 Dataset: 77 participants
• Participants were asked for their demographic information after completing the detailed consent form.
• Skin characteristics were collected next using the Moritex MSA Pro device.
• After having their skin characteristics collected, the participant proceeded to the passport and driver’s
license scanning station.
• Upon having their identification scanned, the participant proceeded to the fingerprint station.
• The participants had their fingerprints collected on up to 8 different sensors. Fingerprints were captured on
the participants left index, left middle, right index, and right middle fingers.
• Participants were given 18 attempts to collect 6 captures of each fingerprint, thus totaling 24 images on
each device.
DATASET 3

42. DEMOGRAPHICS

43. AGE
Figure 9: Age breakdown for Dataset 31
[1] Datarun 1455

44. GENDER
Figure 10: Gender breakdown for Dataset 31
[1] Datarun 1455

45. ETHNICITY
Figure 11: Ethnicity breakdown for Dataset 31
[1] Datarun 1455

46. ANALYSIS

47. RESEARCH QUESTIONS
• Correlation between moisture and quality
• Correlation between elasticity and quality
• Correlation between moisture and elasticity
• Correlation between elasticity and age
• Correlation between moisture and age
• Correlation between quality and age

48. RESEARCH QUESTIONS
• Correlation between moisture and oiliness
• Correlation between oiliness and quality
• Correlation between oiliness and age
• Correlation between oiliness and elasticity
• Correlation between temperature and quality
• Correlation between temperature and moisture

49. RESEARCH QUESTIONS
• Correlation between temperature and age
• Correlation between temperature and elasticity
• Correlation between temperature and oiliness
• Which variables have an effect on image quality – linear
regression

50. CORRELATION
• A correlation is described as a measure of strength
of a relationship between two variables by means
of a single number called a correlation
coefficient.[19]

51. CORRELATION BETWEEN MOISTURE
AND QUALITY
STATISTICAL RESULTS
Pearson r P-value
Dataset 1 0.101 0.000
Dataset 2 -0.050 0.001
Dataset 3 -0.179 0.000
CONCLUSION
• There isn’t consistency between
the 3 datasets in the trend
direction.
• There is a slight correlation.

52. CORRELATION BETWEEN ELASTICITY
AND QUALITY
STATISTICAL RESULTS
Pearson r P-value
Dataset 1 -0.020 0.419
Dataset 2 -0.009 0.542
Dataset 3 -0.214 0.000
CONCLUSION
• There is a slight negative
correlation for Datasets 1 and 2.
• Dataset 3 has low correlation.
• Only Dataset 3 is significant with p-value
of 0.000.

53. CORRELATION BETWEEN MOISTURE
AND ELASTICITY
STATISTICAL RESULTS
Pearson r P-value
Dataset 1 0.179 0.000
Dataset 2 0.097 0.000
Dataset 3 -0.209 0.000
CONCLUSION
• There isn’t consistency between the 3
datasets in the trend direction.
• There is a slight positive correlation
for Dataset 1 and 2.
• Dataset 3 has a low negative
correlation.

54. CORRELATION BETWEEN ELASTICITY
AND AGE
STATISTICAL RESULTS CONCLUSION
• There isn’t consistency between
the 3 datasets in the trend
direction.
• There is a slight correlation.
Pearson r P-value
Dataset 1 -0.147 0.000
Dataset 2 0.060 0.000
Dataset 3 -0.146 0.000

55. CORRELATION BETWEEN MOISTURE
AND AGE
STATISTICAL RESULTS CONCLUSION
• There isn’t consistency between
the 3 datasets in the trend
direction.
• There is a slight correlation.
• Only Dataset 3 is significant with p-value
of 0.000.
Pearson r P-value
Dataset 1 -0.013 0.607
Dataset 2 0.014 0.344
Dataset 3 0.129 0.000

56. CORRELATION BETWEEN QUALITY AND
AGE
STATISTICAL RESULTS CONCLUSION
• There isn’t consistency between the 3
datasets in the trend direction.
• There is a slight positive correlation
for Dataset 3.
• Dataset 1 and Dataset 2 have a low
negative correlation.
Pearson r P-value
Dataset 1 -0.203 0.000
Dataset 2 -0.238 0.000
Dataset 3 0.161 0.000

57. CORRELATION BETWEEN MOISTURE
AND OILINESS
STATISTICAL RESULTS CONCLUSION
• There isn’t consistency between
the 2 datasets in the trend
direction.
• There is a slight correlation.
Pearson r P-value
Dataset 1 -0.124 0.000
Dataset 2 0.068 0.000
Dataset 3

58. CORRELATION BETWEEN OILINESS
AND QUALITY
STATISTICAL RESULTS CONCLUSION
• There isn’t consistency between
the 2 datasets in the trend
direction.
• There is a slight correlation.
Pearson r P-value
Dataset 1 0.106 0.000
Dataset 2 -0.025 0.089
Dataset 3

59. CORRELATION BETWEEN OILINESS
AND AGE
STATISTICAL RESULTS CONCLUSION
• There isn’t consistency between
the 2 datasets in the trend
direction.
• There is a slight correlation.
• Only Dataset 1 is significant with p-value
of 0.055.
Pearson r P-value
Dataset 1 -0.048 0.055
Dataset 2 0.018 0.241
Dataset 3

60. CORRELATION BETWEEN OILINESS
AND ELASTICITY
STATISTICAL RESULTS CONCLUSION
• There is a slight negative
correlation between the 2
datasets.
Pearson r P-value
Dataset 1 -0.164 0.000
Dataset 2 -0.126 0.000
Dataset 3

61. CORRELATION BETWEEN
TEMPERATURE AND QUALITY
STATISTICAL RESULTS CONCLUSION
• There isn’t consistency between
the 3 datasets in the trend
direction.
• There is a slight correlation.
• Dataset 1 is not significant with p-value
of 0.221.
Pearson r P-value
Dataset 1 -0.031 0.221
Dataset 2 0.136 0.000
Dataset 3 0.132 0.000

62. CORRELATION BETWEEN
TEMPERATURE AND MOISTURE
STATISTICAL RESULTS CONCLUSION
• There is a slight negative
correlation.
• Dataset 2 is not significant with p-value
of 0.190.
Pearson r P-value
Dataset 1 -0.109 0.000
Dataset 2 -0.020 0.190
Dataset 3 -0.128 0.000

63. CORRELATION BETWEEN
TEMPERATURE AND AGE
STATISTICAL RESULTS CONCLUSION
• There isn’t consistency between
the 3 datasets in the trend
direction.
• There is a slight correlation.
• Dataset 1 is not significant with p-value
of 0.052
Pearson r P-value
Dataset 1 0.049 0.052
Dataset 2 -0.173 0.000
Dataset 3 0.103 0.000

64. CORRELATION BETWEEN
TEMPERATURE AND ELASTICITY
STATISTICAL RESULTS CONCLUSION
• There isn’t consistency between the 2
datasets in the trend direction.
• There is a slight correlation for Dataset 2
and Dataset 3.
• Dataset 1 has a very high negative
correlation.
• Dataset 3 is not significant with p-value of
0.426.
Pearson r P-value
Dataset 1 -0.999 0.000
Dataset 2 0.055 0.000
Dataset 3 -0.019 0.426

65. CORRELATION BETWEEN
TEMPERATURE AND OILINESS
STATISTICAL RESULTS CONCLUSION
• There isn’t consistency between
the 2 datasets in the trend
direction.
• There is a slight correlation.
Pearson r P-value
Dataset 1 0.071 0.005
Dataset 2 -0.080 0.000
Dataset 3

66. LINEAR REGRESSION
• Linear regression is conducted to test the null
hypothesis with all variables included.

67. BACKWARD ELIMINATION
• Upon performing a linear regression, backward
elimination can be completed to remove the variables
deemed to be insignificant based upon the chosen
significance level.
• In backward elimination, one variable is removed and
the linear regression is re-run until all the variables are
significant.[20]

68. LINEAR REGRESSION
DATASET 2 – ALL PREDICTORS
Predictor P-value
Constant 0.000
Moisture 0.134
Oiliness 0.865
Elasticity 0.285
Temperature 0.000
Age 0.000
Gender 0.000
S 7.84455
R-Sq 8.86%
R-Sq (adj) 8.73%

69. BACKWARD ELIMINATION
DATASET 2 – OILINESS REMOVED
Predictor P-value
Constant 0.000
Moisture 0.134
Elasticity 0.285
Temperature 0.000
Age 0.000
Gender 0.000
S 7.84367
R-Sq 8.86%
R-Sq (adj) 8.75%

70. BACKWARD ELIMINATION
DATASET 2 – ELASTICITY REMOVED
Predictor P-value
Constant 0.000
Moisture 0.134
Temperature 0.000
Age 0.000
Gender 0.000
S 7.84380
R-Sq 8.83%
R-Sq (adj) 8.75%

71. BACKWARD ELIMINATION
DATASET 2 – MOISTURE REMOVED
Predictor P-value
Constant 0.000
Temperature 0.000
Age 0.000
Gender 0.000
S 7.84530
R-Sq 8.78%
R-Sq (adj) 8.72%

72. LINEAR REGRESSION
DATASET 1 – ALL PREDICTORS
Predictor P-value
Constant 0.000
Moisture 0.123
Oiliness 0.001
Elasticity 0.384
Temperature 0.125
Age 0.000
Gender 0.000
S 9.44329
R-Sq 7.57%
R-Sq (adj) 7.21%

73. BACKWARD ELIMINATION
DATASET 1 – ELASTICITY REMOVED
Predictor P-value
Constant 0.000
Moisture 0.000
Oiliness 0.000
Temperature 0.127
Age 0.000
Gender 0.000
S 9.44256
R-Sq 7.52%
R-Sq (adj) 7.23%

74. BACKWARD ELIMINATION
DATASET 1 – TEMPERATURE REMOVED
Predictor P-value
Constant 0.000
Moisture 0.000
Oiliness 0.000
Age 0.000
Gender 0.000
S 9.44658
R-Sq 7.39%
R-Sq (adj) 7.15%

75. LINEAR REGRESSION
DATASET 3 – ALL PREDICTORS
Predictor P-value
Constant 0.041
Moisture 0.000
Oiliness -
Elasticity 0.000
Temperature 0.027
Age 0.000
Gender 0.000
S 9.00848
R-Sq 30.51%
R-Sq (adj) 30.32%

76. CONCLUSIONS
• Across the datasets, the values in the skin characteristics weren’t consistent. In
the linear regression, each dataset produced a different set of predictors that
remained significant. This suggests that the measurements may not be
equivalent or there is no consistency in the way measurements are conducted.
• It isn’t clear which skin characteristics have an effect on the fingerprint image
quality due to the inconsistency between the datasets.
• After getting to a significant set of predictors, quality in the datasets is only
explained by between 7.39% and 30.51%. This leaves us with another 69.49% to
82.61% of unexplained variation in image quality.

77. CONCLUSIONS TO THE LITERATURE
Age Moisture Elasticity Oiliness Image
Quality
Elliott et al.,
2008;
x x x x
Kang et al.,
2003;
x x
Scheidat et
al., 2011;
x x x
Senior &
Bolle, 2001;
x x
Wang, 2013; x x x
Yun & Cho,
x x
2006

78. RECOMMENDATIONS
• Since the data shows different variables affecting image quality, through linear regression
and backward elimination, this signals that there may be other variables to look at. The
data could be collected further with a more controlled study, although may produce the
same or varying results since these variables only explain a small portion of image quality.
• The lack of consistency provides enough reason not to continue collecting the skin
characteristic data as there isn’t a clear picture on the effects on image quality.
• The inconsistency and lack of explanation on image quality suggest that it isn’t a good use
of time and money to collect this data.

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