Fingerprint Sensing Techniques, Devices and Applications Rahul Singh kingtiny@cs.cmu.edu 30th April 2003

Fingerprint Biometric First used in China in 700 AD Proposed in Europe in 1858, implemented in Germany in 1903. Unique – So far no two prints from different fingers have been found that are identical

Fingerprint Biometric Characteristics Fingerprint is the representation of the epidermis of a finger Set of (almost/often) parallel ridge lines Ridges produce local patterns Source: http://www.biometrika.it/eng/wp_fingintro.html

Fingerprint Biometric Characteristics Five main classes of fingerprints Arch Tented Arch Left Loop Right Loop Whorl Source: http://www.biometrika.it/eng/wp_fingintro.html

Fingerprint Sensing Two stages Capture Fingerprint image Process image and extract features Store data for comparison or compare with stored templates

Types of Fingerprint Sensors Optic Reflexive Finger lies on a prism. Total internal reflection produces image of fingerprint on a camera chip Optic Transmissive with Fiber Optic Plate Light source illuminates through the finger Finger lies on fiber-optic plate that transmits image data to camera chip Optical Line Pixel array measures the light reflected by the finger Source: http://home.t-online.de/home/manfred.bromba/fpfaqe.htm

Types of Fingerprint Sensors Capacitive Line Capacitor array measures the capacitance at each pixel Thermal Line Finger is moved across a narrow array of thermal sensors Temperature varies across the grooves and ridges Thermal sensors measure the temperature differences over time Pressure Sensitive Sensor measures the pressure per pixel Dynamic Capacitive Capacitance is measured by A/C voltage Source: http://home.t-online.de/home/manfred.bromba/fpfaqe.htm

Types of Fingerprint Sensors Static Capacitive Type 1 One electrode per pixel Capacitance measured w.r.t neighboring pixel. If pixel is on a groove capacitance is small If pixel is on a ridge then capacitance is large Static Capacitive Type 2 Same as above except capacitance is measure w.r.t ground Acoustic (Ultrasound) Image of fingerprint is recorded by very high frequency sound Source: http://home.t-online.de/home/manfred.bromba/fpfaqe.htm

Capacitive Sensing Fingerprint consists of tightly spaced ridges and valleys Sensor consists of a capacitive array Capacitive array acts as one plate of a capacitor while the finger acts as the other Each pixel in the array is charged to a reference voltage and allowed to discharge with a reference current The rate of change of potential at each pixel is proportional to the capacitance seen by the array

Capacitive Sensing Charge amp reset. Inverter O/P settles to threshold Ref. charge applied to I/P O/P Voltage proportional to feedback capacitance Inverter O/P = upper saturation level if there is no feedback capacitance Inverter O/P = close to logical threshold when feedback capacitance is large Source: http://www-micro.deis.unibo.it/~tartagni/Finger/FingerSensor.html

Capacitive Sensing 300 x 300 pixel array (90,000 pixels) 500 dpi Fingerprint image Source: http://www.fme.fujitsu.com/products/biometric/pdf/Find_FPS.pdf

Optical Sensing Finger touches light emitting TactileSense polymer Hello1 Hello2 Finger touches light emitting TactileSense polymer Photodiode array embedded in the glass detects illumination Image is captured and transferred for storage Source: [Tactilesense] http://www.ethentica.com/tactwhtpr.pdf

Optical Sensing Sensing by projecting an image of the fingerprint onto a camera by total internal reflection. Source: http://www.biometrika.it/eng/wp_fingintro.html

Optical Vs Capacitive Capacitive Optical Sensors Greater miniaturization Newer technology Can be embedded into small devices Prone to dirt etc since finger touches silicon Relatively cheap Optical Sensors Larger sensing area since manufacturing large pure silicon chips is expensive More robust. Longer life More expensive Better image quality and higher resolution

Factors affecting the scan Image quality Sharpness Contrast Distortion Source: http://www.biometrika.it/eng/wp_scfing.html

Factors affecting the scan Resolution – higher is better Too low and we cannot detect the minutiae Sensing area Average fingerprint is about 0.5” x 0.7” Large area (1.0” x 1.0”) ensures that overlap effects (leading to false rejections) are reduced Source: http://www.biometrika.it/eng/wp_scfing.html

Data Storage and Matching Minutiae or Galton Characteristics Termination of Ridge lines Bifurcation of Ridge lines Source: http://www.biometrika.it/eng/wp_fingintro.html

Data Storage and Matching Final data size = 300 to 600 bytes Source: http://www.fme.fujitsu.com/products/biometric/pdf/Find_FPS.pdf

Data Storage and Matching Directional Map Discrete matrix whose elements denote the orientation of the tangent to ridge lines

FX2000 FX2000 – Optical Sensor Database of 100 users (non-experts) Low quality fingerprints Efficiency TASK SPEED_DEFAULT SPEED_FAST Feature extraction 350 ms 260 ms Matching 120 ms 47 ms Identity verification (1:1) 470 ms 307 ms Identification (1:50) 3.35 sec 1.43 sec Accuracy Verification time (1:1) Time to verify the identity Identification time (1:50) Average time to identify an individual. 50 Users. Match is found in the middle. threshold t SPEED_DEFAULT SPEED_FAST FAR FRR 0.3500 0.0049 (0.49%) 0.0005 (0.05%) 0.0032 (0.32%) 0.0009 (0.09%) 0.3750 0.0025 (0.25%) 0.0010 (0.10%) 0.0014 (0.14%) 0.4000 0.0011 (0.11%) 0.0020 (0.20%) 0.4250 0.0006 (0.06%) 0.0019 (0.19%) 0.0002 (0.02%) 0.0034 (0.34%) 0.4500 0.0004 (0.04%) 0.0026 (0.26%) 0.0000 (0.00%) 0.4750 0.0036 (0.36%) 0.0063 (0.63%)

Secugen FDA01/FCA01 Optical sensor Resolution = 500 dpi Verification time = < 1 second Sensing area = 13.6mm x 16.2mm

Authentec FingerLoc AF-S2 AFS8500 Capacitive 68 pin PLCC Resolution: 250 dpi Array size: .512”x.512” AFS8500 144 pin LQFP Array Size: .384” x .384”

Biomouse/Biomouse plus Optical sensor “High speed” matching algorithm – 400 prints per second on pII 400. Resolution = 500 dpi Average template size = 350 bytes Biomouse Plus comes with built in smart card reader

Defeating Fingerprint Scanners Gummi bears defeat fingerprint sensors Japanese cryptographer Gelatin + plastic mould Latent fingerprints from glass Cyanoacrylate Adhesive (superglue fules) Digital camera Adobe Photoshop Photosensitive PCB – etched print in copper Moulded finger with print Source: http://www.theregister.co.uk/content/55/25300.html

Defeating Fingerprint Sensors More sophisticated devices use incorporate biosensing modules prior to fingerprint capture Detect blood flow Detect body heat Sensor shuts down if no life is detected

Types of attack Brute force Latent print Replay Trojan Horses Fake feature Dead feature Other (software leaks, bad security policies etc)

Applications Secure logins via keyboard modules User identification at kiosks Biometric door locks Credit card security Weapon activation Theft protection

Fingerprint Verification for Smart Cards Motorola, Australia Senior Honors thesis Develop biometric security solution (prototype) for Motorola dual-slot phones Users insert credit card into slot 1 for e-commerce Smart card with embedded biometric into slot 2 Fingerprint sensor on phone identifies user and authorizes use of credit card

Fingerprint Verification for Smart Cards Motorola, Australia Enrollment Fingerprint template 1010011010100101 X.509 Certificate Smart Card Fingerprint template 1010011010100101 X.509 Certificate http://www.roma.unisa.edu.au/08216/99u/index.html

Fingerprint Verification for Smart Cards Motorola, Australia Fingerprint template 1010011010100101 Compare Smart Card Fingerprint template 1010011010100101 X.509 Certificate Fingerprint template 1010011010100101 X.509 Certificate Fingerprint template 1010011010100101 http://www.roma.unisa.edu.au/08216/99u/index.html

Questions ?