finger print pore extraction methods
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finger print pore extraction methods finger print pore extraction methods Presentation Transcript

  • ADAPTIVE FINGERPRINT PORE MODELING AND EXTACTIONARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 1
  • Introduction• Biometrics: Biometrics is a study of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral characteristics.• Biometrics can be sorted into two classes: • Physiological Examples: face, fingerprint, hand geometry and iris recognition • Behavioral Examples: signature and voiceARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 2
  • Introduction• Properties of biometrics 1. Universality Every person should have the biometric characteristic2. Uniqueness No two persons should be the same in terms of the biometric characteristic3. Permanence The biometric characteristic should be invariant over time4. Collectability The biometric characteristic should be measurable with some (practical) sensing device5. Acceptability One would want to minimize the objections of the users to the measuring/collection of the biometric6. Circumvention which reflects how easy it is to fool the system by fraudulent methods.ARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 3
  • General Biometric System Biometric Feature Extraction Sensor Database Enrollment Biometric Feature Extraction Sensor Matching ID : 8809 Authentication Result Authentication ResultARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 4
  • FINGERPRINT AS A BIOMETRIC • A fingerprint is an impression of the friction ridges, from the surface of a fingertip. • It is used for personal identification • Easy in acquisition • High matching accuracy rate • Do not change over time • Dominate biometric market by accounting for 52% of authentication systemsARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 5
  • FINGERPRINT AS A BIOMETRIC Fingerprint representation The types of information that can be collected from a fingerprint’s friction ridge impression can be categorized as level 1, Level 2, Level 3. • Level 1 The fingerprint pattern exhibits one or more regions where the ridges lines assume distinctive shapes characterized by high curvature, frequent termination. • Level 2 ridge ending and ridge bifurcations • Level 3 Fine intra ridge detailsARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 6
  • FINGERPRINT AS A BIOMETRIC FINGERPRINT AS A BIOMETRICARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 7
  • FINGERPRINT AS A BIOMETRIC “Most of fingerprint identification systems (like AFIS) rely on minutiae (Level 1&2) only. While this information is sufficient for matching fingerprints in small databases, it is not discriminatory enough to provide good results on large collections of fingerprint images.“ [M. Ray, P. Meenen, R. Adhami - “A Novel Approach to Fingerprint Pore Extraction“, IEEE, Mar. 2005]ARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 8
  • FINGERPRINT AS A BIOMETRIC • both show a bifurcation at the same location – Examination based on Level 1&2 features – match – In combination with Level 3 features (e.g. relative pore position) – no matchARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 9
  • Physiology – Fingerprint formation • Fingerprints begin forming on the fetus 13th week of development • Ridge units are fusing together as they grow forming ridges • Each ridge unit contains a pore which originates from a sweat gland from the dermis • Pores are only found on ridges not in valleysARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 10
  • Physiology – Some facts • typical fingerprint: 150 ridges • A ridge ~ 5 mm long contains appr. 10 ridge units • Ridge width: ~ 0.5 mm • Average number of pores / cm ridge ~ 9-18 pores • Pores do not disappear, move or generate over time [Ashbaugh, D., Quantitative-Qualitative Friction Ridge Analysis, 1999, CRC Press] [Locard, Les pores et lidentification des criminals, Biologica, vol.2, pp. 257-365, 1912]ARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 11
  • Pore Extraction methods 1. skeleton-tracking-based methods - First binaries and skeletonize the fingerprint image and then track the fingerprint skeletons. - Computationally expensive. - very sensitive to noise. - work well on very high resolution fingerprint images. 2. Filtering-based methods - filter fingerprint imagesARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 12
  • Isotropic pore models Invariant with respective to direction 1. Ray’s Model:- which is used 2-dimensional Gaussian functions for pore extraction. 2. Jain’s model:- Jain proposed to use the Mexican hat wavelet transform to extract pores based on the observation that pore regions. 3. DoG Model:- (Difference of Gaussian filter ) Is to use a band-pass filter to detect circle-like features.ARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 13
  • Proposed system Propose to develop a novel dynamic anisotropic pore model which describes the pores more flexibly and accurately by using orientation and scale parameters and an adaptive pore extraction method can detect pores more accurately and robustly.ARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 14
  • Dynamic anisotropic pore model (DAPM) • Previous models are isotropic and static (uses unitary scale) • This new pore model has two parameters to adjust scale and orientation, • These two parameters are adaptively determined according to the local ridge features (i.e. ridge orientation and frequency) • DAPM is defined Eq. (1) is the Reference Model (i.e. the zero-degree model) Eq. (2) is the rotated model Here, is the scale parameter which is used to control the pore size. It can be determined by the local ridge frequency. Is the orientation parameter which is used to control the direction of the pore model.ARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 15
  • Adaptive pore extraction method • Pore extraction is essentially a problem of object detection. • DAPM parameter estimation:- To instantiate the pore model initialize two parameters orientation and scale. - Orientation parameter :-Set the local fingerprint ridge orientation - Scale parameters :- Use the maximum valid pore scale • Implementation issue:- With estimated parameter an adaptive pore model can be instantiated for each pixel and apply to matched Filter to extracted pore from the fingerprint image. - computational cost:- Calculations as pixel wise wayARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 16
  • Adaptive pore extraction method • Implementation issue:- - Accurate estimate Difficult to get accurate estimate by pixel wise • To deal with these issue, propose Block wise approach • Three kinds of blocks on fingerprint image 1) Well-defined blocks 2) Ill-posed blocks Foreground fingerprint region 3) Background blocksARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 17
  • Adaptive pore extraction method • well- defined block: It is able to directly estimate a dominant ridge orientation and a ridge frequency. • Ill-posed block: There is not a dominant ridge orientation but the ridge frequency can be estimated by interpolation of the frequencies on its neighboring blocks.ARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 18
  • Adaptive pore extraction method • Pore Extraction algorithm:ARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 19
  • Adaptive pore extraction method • Partition: The first step is to partition the fingerprint image into a number of blocks, each being a well-defined block, an ill-posed block or a background block • Ridge orientation and frequency estimation: The ridge orientation field of the fingerprint image is calculated. Meanwhile, the mean ridge frequencies on all foreground blocks are estimated, which form the ridge frequency map of the fingerprint image. • Ridge map extraction The binary ridge map of the fingerprint image is calculated • Pore detection: The foreground fingerprint blocks are processed one by one to detect pores on themARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 20
  • Adaptive pore extraction method • Post Processing Record the extracted pores by recording the coordinates of their mass centersARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 21
  • Thank youARVIND S. SARDAR 10.10.2012 Adaptive fingerprint pore modelling and extraction 22