Download presentation
Presentation is loading. Please wait.
Published byLeon Simpson Modified over 8 years ago
1
ENTROPY OF FINGERPRINT SENSORS STEPHEN ELLIOTT, KEVIN O’CONOOR, ZACH MOORE, JEFF CHUDIK, TORREY HUTCHISON, AND NICK THOMPSON
2
Do different fingerprint sensors affect the bits of entropy of a fingerprint? RESEARCH QUESTION
3
Industry has been pushing for biometrics to replace passwords More convenient, but are biometrics still as secure as a traditional password? STATEMENT OF THE PROBLEM
4
INTRODUCTION
5
The purpose is to discover whether or not different fingerprint sensors will produce different results for the amount of bits of entropy across the same subjects on the right index finger in all trials STATEMENT OF PURPOSE
6
When relating entropy and passwords, the higher the entropy, the more secure the password needs to be for equivalency ENTROPY AND PASSWORDS
7
RANDOMLY SELECTED PASSWORDS
8
The logic of defining entropy of a user selected password is an estimate The first character is taken to be 4 bits of entropy The entropy of the next 7 characters are 2 bits per character The 9th through the 20th character is 1.5 bits per character For characters 21 and above entropy is 1 bit per character An additional 6 bits of entropy is added for the composition rule. The composition rule requires lower-case, upper-case, and non-alphabetic characters [3] USER SELECTED PASSWORDS 94 CHARACTERS
9
3 bits of entropy for the first character 2 bits of entropy for the next three characters 1 bit of entropy for the rest of the characters USER SELECTED PASSWORDS 10 CHARACTERS
10
METHODOLOGY
11
151 Subjects Each supplied their right index finger 6 times on 8 different sensors All sensors produced consistent image sizes within each sensor DATA COLLECTION
12
DatarunArea (Pixels)Pixel CountType 1752300x428128,400Thermal Swipe 1754640x480307,200Optical Touch 1755330x357117,810Optical Touch 1756300x30090,000Capacitive Touch 1757320x480153,600Optical Touch 1758248x29272,416Optical Touch 1759186x27050,220Capacitive Swipe 1760256x36092,160Capacitive Touch SENSOR IMAGE SIZES AND TYPE
13
VeriFinger SDK V.5 outputted the minutiae data including the x, y, θ, and type of minutiae point x and y are the location of the point in the image Theta is the angle of the minutiae point Theta is classified as either 1, 2, 3, or 4 depending on the angle Type is either ridge ending or bifurcation Ending = 1 Bifurcation = 2 MINUTIAE DATA
14
Angle 1: 0° - 89° Angle 2: 90° - 179° Angle 3: 180° - 269° Angle 4: 270° - 359° 14 32
15
Keyspace needs to be determined Based on two parameters Possible pixel locations, denoted by L, which is the surface area of the image (varied between sensors) Possible characteristics about a minutiae point, denoted by C, which is defined by type and angle as defined earlier ENTROPY CALCULATION
17
RESULTS
18
SAMPLES FROM EACH SENSOR The same subject across all 8 sensors
19
DatarunTypeAngle1Angle 2Angle3Angle 4EndBifA1endA1bifA2endA2bifA3endA3bifA4endA4bifAvg MinutiaeEntropy Entropy Per Minutiae 1761 Thermal Swipe0.2350.2710.2940.2000.5230.4770.1230.1120.1420.1290.1540.1400.1050.09540.00064.5281.613 1762 Optical Touch0.2440.2880.2710.1980.6940.3060.1690.0750.2000.0880.1880.0830.1370.06039.00070.3611.804 1763 Optical Touch0.2880.2890.2670.1570.6350.3650.1830.1050.1830.1050.1690.0970.1000.05730.00052.2371.741 1764 Capacitive Touch0.3120.2990.2580.1320.6540.3460.2040.1080.1950.1030.1690.0890.0860.04624.00043.3191.805 1765 Optical Touch0.2520.2830.2750.1900.6170.3830.1560.0970.1750.1080.1700.1050.1170.07338.00063.5081.671 1766 Optical Touch0.2960.280 0.1430.5900.4100.1750.1210.1650.1150.1650.1150.0850.05927.00045.5911.689 1767 Capacitive Swipe0.2770.3180.2550.1500.5960.4040.1650.1120.1900.1280.1520.1030.0890.06025.00042.8021.712 1768 Capacitive Touch0.2590.2780.2810.1810.6090.3910.1580.1010.1690.1090.1710.110 0.07135.00057.9951.657 ENTROPY CALCULATIONS TABLE
20
ENTROPY AND PASSWORD LENGTH User ChosenRandomly Chosen 94 Char. Alphabet10 Char. Alphabet94 Char. Datarun Avg. Minutiae EntropyNo Checks Dict. & Composition Rule 17614064.549436019.49.8 17623970.455486521.210.7 17633052.236304715.78.0 17642443.327213813.06.6 17653863.548425919.19.7 17662745.630244113.77.0 17672542.827213812.96.5 17683558.042365317.58.8
21
The following graph shows the average quality scores at each minutiae count. AVERAGE QUALITY BY MINUTIAE COUNT
23
CONCLUSIONS
24
When analyzing the data there seemed to be some sensors that had low entropy but high minutiae This could have to do with the sensor type specifically or rather a function of other variables such as image quality and minutiae count CONCLUSIONS
25
Examine all variables that affect entropy to look for a confounding effect Examine joint entropy equation for flaws Examine cause of linear relationship for entropy and whether it should exist or be a bell curve FUTURE WORK
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.