Biometrics and Sensors Venu Govindaraju CUBS, University at Buffalo [email protected]

Organization Biometrics and Sensor research at UB Biometrics Fingerprint Verification Signature Verification Hand Geometry Multimodal biometrics Securing Biometric Data Sensors and Devices

Research at UB Multimodal Identification Biometrics Fingerprint Signature Hand Geometry Sensors Materials and Light Sources Analog VLSI and Optical Detectors Packaging and Reliability Engineering

Applications And Scope of Biometrics TechnologiesHorizontal ApplicationsKey Vertical Markets FingerprintCivil IDGovernment Sector Facial RecognitionSurveillance and ScreeningTravel and Transportation Iris ScanPC / Network AccessFinancial Sector MiddlewareRetail / ATM / Point of SaleHealth Care AFISeCommerce / TelephonyLaw Enforcement Voice ScanPhysical Access / Time and Attendance Hand GeometryCriminal ID Signature Verification Keystroke Dynamics

Scope of Research In Biometrics BiometricsState of the artResearch Problems Fingerprint 0.15% FRR at 1% FAR (FVC 2002) Fingerprint Enhancement Partial fingerprint matching Face Recognition 10% FRR at 1% FAR (FRVT 2002) Improving accuracy Face alignment variation Handling lighting variations Hand Geometry 4% FRR at 0% FAR (Transport Security Adminstration Tests) Developing reliable models Identification problem Signature Verification 1.5% (IBM Israel) Developing offline verification systems Handling skillful forgeries Chemical Biometrics No open testing done yet Development of sensors Materials research

Biometrics Biometrics and Sensor research at UB Biometrics Fingerprint Verification Signature Verification Hand Geometry Multimodal biometrics Securing Biometric Data Sensors and Devices

Conventional Security Measures Token Based Smart cards Swipe cards Knowledge Based Username/password PIN Disadvantages of Conventional Measures Tokens can be lost or misused Passwords can be forgotten Multiple tokens and passwords difficult to manage

Biometrics Definition Biometrics is the science of verifying and establishing the identity of an individual through physiological features or behavioral traits Examples Physical Biometrics Fingerprint, Hand Geometry,Iris,Face Behavioral Biometrics Handwriting, Signature, Speech, Gait Chemical Biometrics DNA, blood-glucose

Fingerprint Verification Biometrics and Sensor research at UB Biometrics Fingerprint Verification Signature Verification Hand Geometry Multimodal biometrics Securing Biometric Data Sensors and Devices

Fingerprint Verification Fingerprints can be classified based on the ridge flow pattern Fingerprints can be distinguished based on the ridge characteristics

Fingerprint Image Enhancement Preprocessing Enhancement Feature Extraction Matching High contrast printTypical dry print Low contrast printTypical Wet Print

Traditional Approach Preprocessing Enhancement Feature Extraction Matching Local Ridge Spacing F(x,y) Projection Based Method Enhancement Frequency/Spatial Local Orientation (x,y) Gradient Method

Fourier Analysis Approach Preprocessing Enhancement Feature Extraction Matching FFT Analysis Energy Map E(x,y) Orientation Map O(x,y) Ridge Spacing Map F(x,y) FFT Enhancement

Fourier Analysis Applied to fingerprints Fingerprint ridges can be modeled as an oriented wave Local ridge orientation Local ridge frequency

Fourier Analysis Energy Map Preprocessing Enhancement Feature Extraction Matching Original ImageEnergy MapThresholded Map

Preprocessing Enhancement Feature Extraction Matching Original ImageLocal Ridge Frequency Map Fourier Analysis Frequency Map

Preprocessing Enhancement Feature Extraction Matching Original Image Local Ridge Orientation Map Fourier Analysis-Orientation Map

Preprocessing Enhancement Feature Extraction Matching Original ImageEnhanced Image FFT Based Enhancement

Common Feature Extraction Methods Thinning-based Method Thinning produces artifacts Shifting of Minutiae coordinates Preprocessing Enhancement Feature Extraction Matching Direct Gray-Scale Extraction Method Difficult to determine location and orientation Binarized Image is noisy.

Chaincoded Ridge Following Method Preprocessing Enhancement Feature Extraction Matching

Minutiae Detection Several points in each turn are detected as potential minutiae candidate One of each group is selected as detected minutiae. Minutiae Orientation is detected by considering the angle subtended by two extreme points on the ridge at the middle point. Preprocessing Enhancement Feature Extraction Matching

Pruning Detected Minutiae Ending minutiae in the boundary of fingerprint images need to be removed with help of FFT Energy Map Closest minutiae with similar orientation need to be removed Preprocessing Enhancement Feature Extraction Matching

Pure localized feature Derived from minutiae representation Orientation invariant Denote as (r 0, r 1, 0, 1, ) r 0, r 1 : lengths of MN 0 and MN 1 0, 1 : relative minutiae orientation w.r.t. M : angle of N 0 MN 1 Secondary Features Preprocessing Enhancement Feature Extraction Matching

Dynamic Tolerance Areas Tolerance Area is dynamically decided w.r.t. the length of the leg. Longer leg: Tolerates more distortion in length than the angle. Shorter leg: tolerates less distortion in length than the angle. A B O Preprocessing Enhancement Feature Extraction Matching Dynamic tolerance Dynamic Windows

Feature Matching Preprocessing Enhancement Feature Extraction Matching 1.For each triangle, generate a list of candidate matching triangles 2.To recover the rotation between the prints. Find the most probable orientation difference 3.Apply the results of the pruning and match the rest of the points based on the reference points established.

OD=0.7865 Validation Preprocessing Enhancement Feature Extraction Matching 1.For each triangle, generate a list of candidate matching triangles 2.To recover the rotation between the prints. Find the most probable orientation difference 3.Apply the results of the pruning and match the rest of the points based on the reference points established.

Minutia Matching Preprocessing Enhancement Feature Extraction Matching 1.For each triangle, generate a list of candidate matching triangles 2.To recover the rotation between the prints. Find the most probable orientation difference 3.Apply the results of the pruning and match the rest of the points based on the reference points established

Data Sets Fig(a) Sensors and technology used in acquisition Fig(b) Paired fingerprintsFig(c) Database sets

Preliminary Results Min Total Error = 1.16% ERR = 1.0% FRR at 0 FAR = 5.0% FARFRR Threshold Min Total Error = 0.19% FRR at 0 FAR = 0.38% State of the art

Signature Verification Biometrics and Sensor research at UB Biometrics Fingerprint Verification Signature Verification Hand Geometry Multimodal biometrics Securing Biometric Data Sensors and Devices

Signature Verification Off line Signature Verification Online Signature verification

Preprocessing Make signature invariant to scale, translation and rotation. mean-std norm. Resampling Smoothing Preprocessing Template generation Matching Preprocessing 0-160 -1.5-3.5 (-170)- (-125) (-3.0)- (4.0)

Preprocessing Template generation Matching Extracting features. Usually we can not expect more than 6 genuine signatures for training for each subject. This is unlike handwriting recognition Decide the consistent features. There are over 100 features for signature, such as Width, Height, Duration, Orientation, X positions, Y positions, Speed, Curvature, Pressure, so on. Template Generation- Challenges

Simple Regression Model Similarity by R 2 : 91% R2=R2= Preprocessing Template generation Matching Y = (y 1, y 2, , y n ) X = (x 1, x 2, , x n ) Matching Similarity Measure Similarity by R 2 : 31%

Advantages: Invariant to scale and translation. Similarity (Goodness-of-fit) makes sense. Disadvantages: One-one alignment, brittle. One-One alignmentDynamic alignment Preprocessing Template generation Matching Traditional Regression approach

DTW warping path in a n-by-m matrix is the path which has min cumulative cost. The unmarked area is the constrain that path is allowed to go. ( y 2 is matched x 2, x 3, so we extend it to be two points in Y sequence.) Preprocessing Template generation Matching Similarity = R 2 Dynamic Regression approach(1) Where (x 1i, y 1i, v 1i ) are points in the sequence And a, b, c are the weights, e.g., 0.5, 0.5, 0.25

Offline Signature Verification Shapes can be described using structural or statistical features We use an analytical approach that uses the attributes of structures. Extracting structural features

Attributes of structural features Statistical analysis of the feature attributes Attributes of structural features

Hidden Markov Models and SFSA Obtaining a stochastic model Outgoing transitional probabilities The occurrence of the structural features can be modeled as a HMM The HMM can be converted to a SFSA by assigning observation and probability to the transitions instead of to the states

Hand Geometry Biometrics and Sensor research at UB Biometrics Fingerprint Verification Signature Verification Hand Geometry Multimodal biometrics Securing Biometric Data Sensors and Devices

Used where Robustness, Low cost are the concerns. Comparatively less accurate. Combination with other Biometric techniques, increases accuracy. Sufficient for verification where finger print use may infringe on privacy. Hand Geometry

A snapshot of the top and side views of the users right hand gives the contours outlining the hand. Features necessary to identify the hand are extracted from these contours. Using simple image processing techniques, the contours of the set of two images of the hand are obtained. Hand-verification is done by correlating these features. Research: New features and algorithms for better discrimination between two hands. Feature Extraction

Multimodal Biometrics Biometrics and Sensor research at UB Biometrics Fingerprint Verification Signature Verification Hand Geometry Multimodal biometrics Securing Biometric Data Sensors and Devices

Combination of biometric matchers Fingerprint matching Hand geometry matching Signature matching Alice Bob : 26 12 : Alice Bob : 0.31 0.45 : Alice Bob : 5.54 7.81 : Alice Bob : 0.95 0.11 : Combination algorithm Combination of the matching results of different biometric features provides higher accuracy.

Sequential combination of matchers Fingerprint matching Hand geometry matching Signature matching Alice Bob : 0.95 0.11 : Combination algorithm 1 Desired confidence achieved? Combination algorithm 2 Desired confidence achieved? Combination algorithm 3 No Yes

Securing Biometric Data Biometrics and Sensor research at UB Biometrics Fingerprint Verification Signature Verification Hand Geometry Multimodal biometrics Securing Biometric Data Sensors and Devices

Securing password information It is impossible to learn the original password given stored hash value of it.

Securing fingerprint information Wish to use similar functions for fingerprint data:

Obstacles in finding hash functions Since match algorithm will work with the values of hash functions, similar fingerprints should have similar hash values rotation and translation of original image should not have big impact on hash values partial fingerprints should be matched Fingerprint spaceHash space h f1f1 f2f2 h(f 1 ) h(f 2 )

Sensors and Devices Biometrics and Sensor research at UB Biometrics Fingerprint Verification Securing Biometric Data Signature Verification Hand Geometry Sensors and Devices

Sensors and Biometrics Fingerprint Optical Sensors Capacitive Sensors Thermal Sensors Ultrasound Sensors Signature Digitizer Tablet Digitizer Pen Offline scanning Face Recognition Optical Digital camera Thermal cameras Chemical Biometrics Sensor Arrays Smart Devices (Research at UB)

CMOS CCDs Photodiodes Image Processing Tissues Cells Proteins DNA and RNA Organic and Inorganic Dyes Molecular Imprinting Light Sources (OLEDs, LEDs, Lasers) Signal Generators Driver Circuits Power Supply Biosurfaces Biofouling Immobilization and Stabilization Transduction mechanism Multi-Analyte detection Photonic Bandgap (PBG) Resonators Evanescent Wave Devices (PBG) Biosurfaces - Biofouling Bioinspired Pattern Recognition Biomimetics Artificial Vision, Smell. Bioinspired Super Correlator Biosurfaces Biofouling Nano-LEDs Bioinspired Photovoltaics, Biofuel Cells Environmental Testing Low Power Light Sources Detector System Sensing Layer Stimulator and Support System Analyte c) Deviceb) Enabling Technologiesa) Fundamental Knowledge Sensors

Blocking Filter Output Device Stimulator and Support System Detector System Sensing Layer Sensor Components

CMOS Integrated Sensor System

60 m 1.2 m thick Sensor System Components

Response (%) *** * * ** * Protein * Analyte The sensors selectively respond to Ovalbumin Orders of magnitude greater than other components Each site can individually respond to different analytes PIXIES Protein Imprinted Xerogels with Integrated Emission Sites

A unique collaborative initiative that enables state-of-the-art Biometric Science and Technology Creating a multi-disciplinary environment attracting faculty and students from engineering and sciences Preparing and educating future Biometric Scientists and Engineers Targeting all the aspects of Biometrics from authentication to materials and including them into a packaged device Summary

www.cubs.buffalo.eduwww.cubs.buffalo.edu www.photonics.buffalo.eduwww.photonics.buffalo.edu www.cedar.buffalo.eduwww.cedar.buffalo.edu www.packaging.buffalo.eduwww.packaging.buffalo.edu Financial support of: National Science Foundation (NSF) Office of Naval Research (ONR) Calspan UB Research Center (CUBRC) University at Buffalo Center for Advanced Technology (UBCAT) Acknowledgements Websites

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Biometrics and Sensors Venu Govindaraju CUBS, University at Buffalo [email protected]