Presentation is loading. Please wait.

Presentation is loading. Please wait.

ONLINE FINGERPRINT VERIFICATION Sharat Chikkerur Center for Unified Biometrics and Sensors University at Buffalo www.cubs.buffalo.edu Advisor: A. N. Cartwright.

Similar presentations


Presentation on theme: "ONLINE FINGERPRINT VERIFICATION Sharat Chikkerur Center for Unified Biometrics and Sensors University at Buffalo www.cubs.buffalo.edu Advisor: A. N. Cartwright."— Presentation transcript:

1 ONLINE FINGERPRINT VERIFICATION Sharat Chikkerur Center for Unified Biometrics and Sensors University at Buffalo Advisor: A. N. Cartwright Committee: V. Govindaraju, A. H. Titus, L. Kondi

2 Abstract  Background  Traditional password/token based authentication schemes are insecure and are being replaced by biometric authentication mechanisms  Fingerprints were one of the first biometrics to be widely used  Despite 40 years of research, fingerprint recognition is still an open problem.  Challenges  Feature extraction is very unreliable in poor quality prints  Matching fingerprints under non linear distortion is difficult  Contributions  New fingerprint image enhancement using STFT analysis.  New feature extraction algorithm based on chain code contours  New graph based matching algorithm robust to non linear distortion

3 Outline  Introduction  Biometrics  Fingerprints 101  Fingerprint Image Enhancement  Minutia Feature Extraction  Matching Algorithm  Conclusion  Software Demos

4 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, Face  Measurement Biometric  Dependent on environment/interaction  Behavioral Biometrics  Handwriting, Signature, Speech, Gait  Performance/Temporal biometric  Dependent on state of mind  Chemical/Biological Biometrics  Skin spectroscopy  DNA, blood-glucose

5 Fingerprints as a Biometric  High Universality  A majority of the population (>96%) have legible fingerprints  More than the number of people who possess passports, license and IDs  High Distinctiveness  Even identical twins have different fingerprints (most biometrics fail)  Individuality of fingerprints established through empirical evidence  High Permanence  Fingerprints are formed in the fetal stage and remain structurally unchanged through out life.  High Performance  One of the most accurate forms of biometrics available  Best trade off between convenience and security  High Acceptability  Fingerprint acquisition is non intrusive. Requires no training.

6 Fingerprints 101: Fingerprint Classes A fingerprint is made up of system of oriented friction ridges A fingerprint can be classified based on type the ridge flow pattern Classification helps in narrowing down possible matches In reality, the class distribution is skewed (>65% are loops) Used only in law enforcement applications

7 Fingerprints 101: Ridge Characteristics Fingerprints can be distinguished based on the ridge characteristics Ridge characteristics mark local discontinuities in the ridge flow No two individuals have the same pattern of ridge characteristics at the same relative locations Local Features Global Features

8 Prior Related Work: Matching Paradigms  Manual  Human experts use a combination of visual, textural, minutiae cues and experience for verification  Still used in the final stages of law enforcement applications  Image based  Utilizes only visual appearance.  Requires the complete image to be stored (large template sizes)  Texture based  Treats the fingerprint as an oriented texture image  Less accurate than minutiae based matchers since most regions in the fingerprints carry low textural content  Minutiae based  Uses the relative position of the minutiae points  The most popular and accurate approach for verification  Resembles manual approach very closely.

9 Image Based Matching: Optical Correlation  Advantages  Image itself is used as the template  Requires only low resolution images  Optical correlation makes it extremely fast (Choudary and Awwal ’99, Lee et al. 99, Roberge et al. 99, Baze et al.00)  Disadvantages  Image itself is used as the template (template size about 30 KB)  Requires accurate alignment of the two prints (unreliable in poor prints)  Not robust to changes in scale, orientation and position.

10 Texture Based Matching: Filterbanks  Advantages  Uses texture information (lost in optical and minutiae based schemes)  Performs well with poor quality prints  Features are statistically independent from minutiae and can be combined with minutiae matchers for higher accuracy (Jain et al. 00, Jain et al 01)  Disadvantages  Requires accurate alignment of the two prints (unreliable in poor prints)  Not invariant to translation, orientation and non-linear distortion.  Less Accurate than minutiae based matchers

11 Minutiae Based Matching  Advantages  Invariant to translation, rotation and scale changes  Very accurate (Ratha et al 96, Jain et al. 97, Jian Yau 00, Bazen and Garez 03)  Disadvantages  Minutiae extraction is error prone is low quality images  Not robust to non-linear distortion.  Does not use visual and textural cues

12 General Architecture

13 Outline  Introduction  Fingerprint Image Enhancement  Need for Enhancement  Prior Related Work  Proposed Algorithm: STFT Analysis  Experimental Evaluation  Minutia Feature Extraction  Matching Algorithm  Software Demos

14 Need for Enhancement What you see What you ‘think’ you see

15 Reality: What you usually get.. High contrast printTypical dry print Low contrast printTypical Wet PrintCreases Faint print

16 Challenges  Challenges  Fingerprint image is non stationary (has dominant local orientation and frequency)  General purpose image processing algorithms are not useful  Traditional operators and filters assume Gaussian noise model  ‘Noise’ in fingerprint images consists mostly of ridge breaks  Contextual Filters  Existing techniques are based on ‘contextual’ filtering  Filter parameters are adapted to each local neighborhood  Filter parameters in ‘unrecoverable’ regions can be interpolated based on its neighbors

17 Prior Related Work: Spatial Filtering  (Yang et.al 1996, Greenberg et. Al 1999) proposed local anisotropic filtering  Filter kernel adapts at each pixel location  Hong et al, 96/98 proposed the use of Gabor filters for enhancement  Gabor filter has the best joint space-frequency localization  Does not handle high curvature regions well due to block wise approach. Even Symmetric KernelFourier spectrum showing the localization

18 Prior Related Work: Fourier Domain Filtering  Sherlock et al 94, proposed the use of Fourier domain filtering  The image is convolved with a filter bank of directionally selective filters  Image enhanced by selecting a linear combination of filter responses  Watson et al. 94, proposed the use or ‘root filtering’ for enhancement.(Pseudo matched filter)  Does not require the computation of orientation images Root Filtering Fourier Domain Filtering

19 Traditional Approaches Local Ridge Spacing F(x,y) Projection Based Method Enhancement Frequency/Spatial Local Orientation (x,y) Gradient Method [Ratha et al 95]

20 Proposed Approach: Overview STFT Analysis Frequency Image Region Mask Orientation Image Coherence Image Fourier domain Enhancement

21 STFT Analysis  Fingerprint image is non stationary, so we require both space and frequency resolution: time frequency analysis  STFT in 1D  STFT in 2D

22 Surface Wave Model Fingerprint ridges can be modeled as an oriented wave Local ridge orientation Local ridge frequency Local Neighborhoods Validity of the model Surface wave

23 Parameter Estimation  Paradigm: The Fourier domain response can be viewed as a distribution of surface waves. Each term F(r, θ) corresponds to a surface wave of frequency 1/r and orientation θ  We seek to find the most likely surface wave and hence estimate the dominant direction and frequency  We can represent the Fourier spectrum in polar form as F(r,θ) The power spectrum is reduced to a joint probability density function using  The angular and frequency densities are given by marginal density functions

24 Ridge Orientation Image

25 Region Mask  The surface wave approximation does not hold in the background region  The region mask is obtained by simple thresholding of the block energy image

26 Frequency Image [Jain et al 00]

27 Coherence Image Block processing is unreliable in regions of high curvature Sherlock and Monro 94, relax filter parameters near the singular locations Estimation of singular point is difficult in poor images! We use an angular coherence measure proposed by Rao and Jain 90

28 Enhancement

29 Additional Enhancement Results

30 Qualitative Comparison Original Image Root Filtering

31 Qualitative Comparison(cont.) Gabor Filter based EnhancementProposed Approach

32 Objective Evaluation We evaluated the effect of enhancement on 800 images from FVC2002 DB3 The evaluation consists of 2800 genuine test and 4950 impostor tests It can be seen that the matcher performance improves with enhancement

33 Outline  Introduction  Fingerprint Image Enhancement  Minutia Feature Extraction  Prior Related Work  Chain code contour  Experimental Evaluation  Matching Algorithm  Conclusion  Software Demos

34 Background  Minutiae represent local discontinuities in ridge flow  Minutiae features are the most widely used fingerprint representation  There are several standards such as CBEFF (file format) and ANSI- NIST (interchange format) standards for minutiae based fingerprint representation  Minutiae extraction approaches may be broadly categorized into  Binarization based approaches  Direct gray scale extraction

35 Prior Related Work  Binarization Approaches MINDTCT,NIST NFIS, (Garris et. Al, 02)  Directionally adaptive binarization  Template matching is used to detect minutiae Adaptive Flow Orientation technique (Ratha et. al., 95)  Binarization is performed by peak detection  Peak detection leads to false positives in regions of poor ridge constrast.  Direct Gray Scale Ridge Following Ridge Following (Maio and Maltoni 97, Jiang and Yau 01)  Based on ridge pursuit  Has low computational complexity.  Cannot handle poor contrast prints and images with poor ridge structure.  Relies on a good orientation map for ridge pursuit

36 Binarization Method Binarization Thinning Minutia Detection Acquisition

37 Proposed Approach: Chain Code Contours  Provides a lossless description of the contour and also gives direction and curvature information.  Translation and rotation invariant  Used in computer vision for encoding object boundaries  Used for character recognition (Madhavanth et. al 99)

38 Minutiae Detection using Chain Codes  Minutiae are encountered as points of ‘significant’ turn on the contour  Left turn: Ridge ending  Right turn: Bifurcation

39 Determining Turn Points

40 Results

41 Results (cont.)

42 Experimental Evaluation  Test Data  150 prints from FVC2002(DB1) were randomly selected for evaluation.  Ground truth was established using a semi automated truthing tool.  Results compared using NIST NFIS open source software.  Metrics  Proposed by Sherlock et. Al 94  Sensitivity: Ability of the algorithm to detect true minutiae  Specificity : Ability of the algorithm to avoid false positives  Flipped : Minutiae whose type has been exchanged

43 Quantitative Analysis : Results  Examples File NameNISTProposed method ActualTPFPMFTPFPMF 10_8.tif _6.tif _8.tif _6.tif _6.tif _7.tif _7.tif _6.tif _8.tif _7.tif

44 Results Summary results  Count TP(NIST) > proposed : 40 of 150  Count E(NIST) < proposed : 40 of 150 MetricNISTProposed Sensitivity(%) Specificity(%) Flipped(%) Sensitivity distributionOverall statistics

45 Outline  Introduction  Fingerprint Image Enhancement  Minutia Feature Extraction  Matching Algorithm  Prior Related Work  New Representation: K-plet  Local Matching: Dynamic Programming  Consolidation: Coupled BFS  Experimental Evaluation  Conclusion  Software Demos

46 Minutiae Based Matching  Challenges  Minutiae extraction is error prone is low quality images  Not robust to non-linear distortion.  Intra-user variation

47 Challenges: Non-linear Distortion

48 Challenges: Quality and Intra-user variance Variation in quality Intra-user variation

49 Prior Related Work :Global Matching  Global Matching  Point correspondences not known : combinatorial problem Relaxation approach (Ranade and Rosenfield 93)  Likelihood of each match is either decreased or increased at each iteration based on compatibility of rest of the points  Iterative approach makes it too slow to be practical Generalized Hough Transform (Ratha et al. 96)  All possible transformation represented as a quantized search space  Searches for the most optimal transform in the search space  Very fast Ridge Alignment (Jain et al. 97)  Performs explicit alignment before matching  Each minutiae is associated with its ridge (represented by a curve)  The alignment is based on ridge correspondence  Global matching is then performed using string edit distance

50 Prior Related Work: Local Matching  (Jiang and Yau 00)  11 dimensional local features derived from reference minutiae and two closest neighbors  Best match is used only for explicit alignment  (Jea and Govindaraju 04)  5 dimesional features S i (r i0, r i1, φ i0, φ i1, δ i ) derived from two closest neighbors  Alignment is still required  (Ratha et al. 00)  ‘Star’ representation derived from all minutiae within a particular radius  Consolidation by checking consistency  (Garris et. al 03: BOZORTH3)  Line features  Consolidation by linking consisting matches

51 Proposed Algorithm  Representation  K-Plet  Features invariant to rotation and translation  Local relationship formally represented by a directed graph  Local Matching  Posed as a string alignment problem and solved by dynamic programming  Matches all neighbors simultaneously  Consolidation  Coupled Breadth First Search  Breadth first search is used to propagate the matches  Similar to human verification  No explicit alignment required at any stage

52 Neighborhood Representation: K-plet

53 K-plet r Θ Φ

54 Local Matching  All local neighbors have to be matched simultaneously. Greedy approach does not work when conflicts occur  These can solved by finding the alignment through optimization process such as by solving a string alignment problem  Example of alignment:  S (acbcdb) – (ac__bcdb)  T (cadbd) - (_cadb_d_)  Trivial solution requires exponential time  Each match is given a cost. Alignment solved through recurrence relation

55 The ‘Graphical’ View

56 Graphical Matching: Coupled BFS

57 Coupled BFS

58 Graphical Matching: Coupled BFS

59

60

61 Important Differences  Traditional Breadth First Search  Traversal Defined only over a single graph  All neighbors are considered for expanding the path  Coupled Breadth First Search  Traversal proceeds in two directed graphs simultaneously  Only ‘matched’ neighbors are considered for expanding the path  Constant number of neighbors provides a bound for the traversal complexity

62 Experimental Evaluation  800 prints from FVC2002(DB1)  2800 genuine tests,4950 impostor tests  Compared with BOZORTH 3 Error Rates BOZORTH3: 3.6% EER, 5.0% FMR100 Proposed: 1.5% EER, 1.65% FMR100

63 Software  CUBS Truthing Tool  CUBS Minutiae Truthing Tool  CUBS Fingerprint Verification Demo  Matlab code for Fingerprint Enhancement  Matlab Toolbox for Fingerprint Verification   1179 downloads since 01/30  637 downloads since 03/22

64 Conclusion  Contributions  New Fingerprint Image Enhancement using STFT Analysis.  Simultaneously estimates all intrinsic images  Increases recognition rate of existing matchers  New Feature Extraction Algorithm using Chain code Contour  Obviates need for thinning  Performs favorably with NIST feature extractor  New Graph based matching algorithm  Robust to non linear distortion  Formal technique for propagating local matches  Performs better than NIST BOZORTH3 matcher over FVC DB1 database

65 Acknowledgements  Tsai Yang Jea (Alan)  Chaohang Wu  Sergey Tulyakov  Faisal Farooq  Amit Mhatre  Karthik Sridharan  Sankalp Nayak  Rest of the research group at CUBS

66 Thank You


Download ppt "ONLINE FINGERPRINT VERIFICATION Sharat Chikkerur Center for Unified Biometrics and Sensors University at Buffalo www.cubs.buffalo.edu Advisor: A. N. Cartwright."

Similar presentations


Ads by Google