understanding fingerprint skin characteristics and image quality
DESCRIPTION
TRANSCRIPT
UNDERSTANDING FINGERPRINT SKIN CHARACTERISTICS AND
IMAGE QUALITYADAM GRAHAM
STEPHEN ELLIOTT
•Problem Statement
•Motivation
• Literature Review
•Demographics
•Analysis
OVERVIEW
•Relationship between moisture, oiliness, and elasticity
•Relationship between individual skin characteristics and quality
INTRODUCTION
•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.
WHY ARE YOU DOING THIS?
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]
•This research examines skin moisture, oiliness, temperature, and elasticity and their relationship to fingerprint fidelity image quality
PROBLEM STATEMENT
•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]
SIGNIFICANCE
SKIN STRUCTURE
Figure 1: Layers of the skin[11]
•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.
PORES
•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]
SEBACEOUS GLANDS
•Oiliness is defined as excessively high in naturally secreted oils. [10]
OILINESS
•Senior & Bolle (2001) stated that oil on the fingerprint often leads to poor image quality.[16]
LITERATURE
•Wang (2013) stated that oil on the fingerprint often leads to poor image quality.[15]
LITERATURE
•Yun & Cho (2006) stated that oil on the fingerprint often leads to poor image quality.[22]
LITERATURE
•Moisture is defined as liquid diffused or condensed in a relatively small quantity. [9]
MOISTURE
•Kang et al. (2003) stated that when moisture is lower, image quality will be greatly reduced rather than when the moisture is higher.[7]
LITERATURE
•Elasticity is defined as resilience, or the ability of something to return to its original shape after it has been manipulated. [4][13]
ELASTICITY
•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]
LITERATURE
•When you age, the skin loses its elastic properties and becomes increasingly dry (Scheidat et al., 2011).
LITERATURE
•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]
IMAGE QUALITY
•Fidelity image quality is described as the degree to which a sample is an accurate representation of its source. [17]
FIDELITY IMAGE QUALITY
•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]
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, 2006
x x
LITERATURE SUMMARY
Table 4: Literature review summary
•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
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
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
RESEARCH QUESTIONS
•Devices
•Digital Persona UareU 4000
•Moritex MSA Pro
• Triplesense
FINGERPRINT
DIGITAL PERSONA UareU 4000
Model Number U.are.U 4000Manufacturer digitalPersonaIn-house ID 14Scan Area 15 x 18 mmDimensions 79 x 49 x 19 mm
Compliance FCC Class B, CE, ICES, BSMI, MIC, USB
Communication USB 2.0Power 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
Device Specifications
MORITEX MSA PRO
Model Number MSA Pro
Manufacturer Moritex
In-house ID 512
Scan Area -
Dimensions 226 x 81 x 77 mm
ComplianceCommunication USB 2.0
Power Supply 5.0V DC
Table 2: Specification table for Moritex MSA
Pro skin analysis counseling systemFigure 2: Moritex MSA
Pro skin analysis counseling system
Device Specifications
TRIPLESENSE
Model Number K10229
Manufacturer Schott
In-house ID 486
Scan AreaDimensions 63 x 54.6 x 157.3 mm
ComplianceCommunication USB 2.0
Power Supply 2xAAA Battery Operated
Table 3: Specification table for Triplesense skin analysis sensor
Figure 3: Triplesense skin analysis sensor
Device Specifications
DESCRIPTION OF DATASETS
• 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.
DATASET 1
DEMOGRAPHICS
AGE
Figure 4: Age breakdown for Dataset 11
[1] Datarun 1456
GENDER
[1] Datarun 1456
Figure 5: Gender breakdown for Dataset 11
ETHNICITY
[1] Datarun 1456
Figure 6: Ethnicity breakdown for Dataset 11
• 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.
DATASET 2
DEMOGRAPHICS
AGE
[1] Datarun 1457
Figure 7: Age breakdown for Dataset 21
GENDER
[1] Datarun 1457
Figure 8: Gender breakdown for Dataset 21
• 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
DEMOGRAPHICS
AGE
Figure 9: Age breakdown for Dataset 31
[1] Datarun 1455
GENDER
Figure 10: Gender breakdown for Dataset 31
[1] Datarun 1455
ETHNICITY
Figure 11: Ethnicity breakdown for Dataset 31
[1] Datarun 1455
ANALYSIS
•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
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
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
RESEARCH QUESTIONS
•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]
CORRELATION
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.
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.
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.
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
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
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
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
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
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
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
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
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
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
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
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
•Linear regression is conducted to test the null hypothesis with all variables included.
LINEAR REGRESSION
•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]
BACKWARD ELIMINATION
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%
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%
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%
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%
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%
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%
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%
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%
• 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.
CONCLUSIONS
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, 2006
x x
• 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.
RECOMMENDATIONS
• [1] Aware. (2009). Identification Flats: A Revolution in Fingerprint Biometics. Retrieved from http://www.aware.com/biometrics/pdfs/WP_IDFlats.pdf
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• [3] Chen, Y., Dass, S., & Jain, A. (2005). Fingerprint Quality Indices for Predicting Authentication Performance. In T. Kanade, A. Jain, & N. K. Ratha (Eds.), Audio- and Video-Based Biometric Person Authentication (pp. 160–170). Springer Berlin Heidelberg. doi:10.1007/11527923_17
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• [9] Moisture. (2014). merriam-webster.com. Retrieved from http://www.merriam-webster.com/dictionary/moisture
• [10] Oiliness. (2014). merriam-webster.com. Retrieved from http://www.merriam-webster.com/dictionary/oiliness
REFERENCES
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• [21] Wang, L. (2013). The Effect of Force on Fingerprint Image Quality and Fingerprint Distortion. International Journal of Electrical and Computer Engineering (IJECE), 3(3), 294–300. Retrieved from http://iaesjournal.com/online/index.php/IJECE/article/view/2489/pdf
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