understanding fingerprint skin characteristics and image quality

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

Steve Elliott

What about skin moisture? Is this the same?

•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

Steve Elliott

Again, does this incorporate the skin 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


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


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


• 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


GENDER

[1] Datarun 1457


• DHS2012 Dataset: 77 participants


• 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


[1] Datarun 1455

GENDER


[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 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



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





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



• 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




Pearson r

P-value

Dataset 1

-0.124 0.000

Dataset 2

0.068 0.000

Dataset 3

CORRELATION BETWEEN OILINESS AND QUALITY




Pearson r

P-value

Dataset 1

0.106 0.000

Dataset 2

-0.025 0.089

Dataset 3

CORRELATION BETWEEN OILINESS AND AGE





Pearson r

P-value

Dataset 1

-0.048 0.055

Dataset 2

0.018 0.241

Dataset 3

CORRELATION BETWEEN OILINESS AND ELASTICITY


• 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




• 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


• There is a slight negative correlation.


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




• 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



• There is a slight correlation for Dataset 2 and Dataset 3.

• Dataset 1 has a very high negative correlation.


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




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%


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%


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%


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


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%


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%


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


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

• [2] Blomeke, C. R., Modi, S. K., & Elliott, S. J. (2008). Investigating the Relationship Between Fingerprint Image Quality and Skin Characteristics. In International Carnahan Conference on Security Technology (pp. 158–161).

• [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

• [4] Elasticity. (2014). merriam-webster.com. Retrieved from http://www.merriam-webster.com/dictionary/elasticity

• [5] Elliott, S. J., & Kukula, E. P. (2010). A Definitional Framework for the Human-Biometric Sensor Interaction Model. In B. V. K. Vijaya Kumar, S. Prabhakar, & A. A. Ross (Eds.), Biometric Technology for Human Identification VII (Vol. 7667, pp. 1–8). doi:10.1117/12.850595

REFERENCES

• [6] Gilchrest, B. a. (1996). A review of skin ageing and its medical therapy. The British journal of dermatology, 135(6), 867–75. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/8977705

• [7] Kang, H., Lee, B., Kim, H., Shin, D., & Kim, J. (2003). A Study on Performance Evaluation of Fingerprint Sensors Performance Evaluation Model for Biometric Products. In Audio- and Video-Based Biometric Person Authentication (pp. 574–583). Springer-Verlag.

• [8] Modi, S. K., Elliott, S. J., Whetsone, J., & Kim, H. (2007). Impact of Age Groups on Fingerprint Recognition Performance. In 2007 IEEE Workshop on Automatic Identification Advanced Technologies (pp. 19–23). doi:10.1109/AUTOID.2007.380586

• [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

• [11] OpenStax College. (2013). Layers of the Skin. Retrieved from http://cnx.org/content/m46060/latest/

• [12] Pore. (2014). merriam-webster.com. Retrieved from http://www.merriam-webster.com/dictionary/pore

• [13] Resilience. (2014). merriam-webster.com. Retrieved from http://www.merriam-webster.com/dictionary/resilience

• [14] Ryu, H. S., Joo, Y. H., Kim, S. O., Park, K. C., & Youn, S. W. (2008). Influence of age and regional differences on skin elasticity as measured by the Cutometer. Skin Research and Technology, 14(3), 354–358. doi:10.1111/j.1600-0846.2008.00302.x

• [15] Scheidat, T., Heinze, J., Vielhauer, C., Dittmann, J., & Kraetzer, C. (2011). Comparative Review of Studies on Aging Effects in Context of Biometric Authentication. In D. Akopian, R. Creutzburg, C. G. M. Snoek, N. Sebe, & L. Kennedy (Eds.), (Vol. 7881, pp. 788110–1 – 788110–9). doi:10.1117/12.872417

• [16] Senior, A., & Bolle, R. (2001). Improved Fingerprint Matching by Distortion Removal. IEICE Transactions on Information and Systems, E84(7), 825–831.

REFERENCES

• [17] Tabassi, E., & Grother, P. (2009). Fingerprint Image Quality. In Encyclopedia of Biometrics. doi:10.1007/978-0-387-73003-5_257

• [18] Thody, A. J., & Shuster, S. (1989). Control and function of sebaceous glands. Physiological Reviews, 69, 383–416.

• [19] Walpole, R., Myers, R., Myers, S., & Ye, K. (2007). Correlation. In Probability and Statistics for Engineers and Scientists 8th Ed. (8th ed., pp. 432–436). Pearson Prentice Hall.

• [20] Walpole, R., Myers, R., Myers, S., & Ye, K. (2007). Sequential Methods for Model Selection. In Probability and Statistics for Engineers and Scientists (8th ed., pp. 479–485). Pearson Prentice Hall.

• [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

• [22] Yun, E.-K., & Cho, S.-B. (2006). Adaptive Fingerprint Image Enhancement with Fingerprint Image Quality Analysis. Image and Vision Computing, 24(1), 101–110. doi:10.1016/j.imavis.2005.09.017

REFERENCES

understanding fingerprint skin characteristics and image quality

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