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Download 1 Fingerprint Recognition CPSC 601 CPSC 601. 2 Lecture Plan Fingerprint features Fingerprint matching

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    Fingerprint Recognition

    CPSC 601

  • *Lecture PlanFingerprint featuresFingerprint matching

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    Fingerprint verification and identification

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    Coarse representation Level 1 features

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    Coarse representation Level 1 features

    ___ ____ ____ ____ ____ ___ _____ ___

    Left loop Right loop Whorl Arch Tented Arch

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    Minutiae Level 2 features

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    Minutia Level 2 features

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    Level 3 features

    Sweat pores

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    Level 3 features

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    Minutiae Detection

    Original image Binary image Skeleton and extracted minutiae

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    Feature extraction process

    Fingerprint imageFingerprint areaFrequency imageOrientation imageRidge pattern &Minutiae points

  • *Feature extraction process

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    Orientation image of fingerprint

    Computation of gradients over a square-meshed grid of size 16 x 16; the element length is proportional to its reliability.

  • *Orientation image of fingerprint

  • *Frequency image ____ _________ _______ __ ___ _______ ________ _______ ___________ _____

    Ridge frequency: inverse of the average distance between 2 consecutive peaks

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    Segmentation

    Segmentation is the process of isolating foreground from background:Image block (16x16 pixels) decompositionThresholding using variance of gradient for each block

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    Why do we need enhancement?

  • * Why do we need enhancement?

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    Need for Enhancement

  • *EnhancementInitial enhancement may involve the normalization of the inherent intensity variation in a digitized fingerprint caused either by the inking or the live-scan device.One such process - local area contrast enhancement (LACE) is useful to provide such normalization through the scaling of local neighborhood pixels in relation to a calculated global mean.An inked fingerprint imageThe results of the LACE algorithm on (a)Histograms of fingerprint images in(a) and (b) above.

  • *Enhancement

    Another type of enhancement is contextual filtering that:1. Provide a low-pass (averaging) effect along the ridge direction with the aim of linking small gaps and filling impurities due to pores or noise. 2. Perform a bandpass (differentiating) effect in a direction orthogonal to the ridges to increase the discrimination between ridges and valleys and to separate parallel linked ridges. 3. Gabor filters have both frequency-selective and orientation-selective properties and have optimal joint resolution in both spatial and frequency domains.

  • *EnhancementGraphical representation (lateral and top view) of the Gabor filter defined by the parameters = 1350, f = 1/5, x = y = 3

  • *EnhancementThe simplest and most natural approach for extracting the local ridge orientation field image, D, containing elements ij, in a fingerprint image is based on the computation of gradients in the fingerprint image.

  • *EnhancementThe local ridge frequency (or density) fxy at point [x, y] is the inverse of the number of ridges per unit length along a hypothetical segment centered at [x, y] and orthogonal to the local ridge orientation xy. A frequency image F, analogous to the orientation image D, is defined if the frequency is estimated at discrete positions and arranged into a matrix. The local ridge frequency varies across different fingers and regions. The ridge pattern can be locally modeled as a sinusoidal-shaped surface and the variation theorem can be exploited to estimate the unknown frequency.

  • *EnhancementThe variation of the function h in the interval [x1, x2] is the sum of the amplitudes 1, 2, 8. If the function is periodic or the function amplitude does not change significantly within the interval of interest, the average amplitude m can be used to approximate the individual . Then the variation can be expressed as 2m multiplied by the number of periods of the function over the interval.

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    Gabor filters

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    Enhancement Results

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    Artifacts

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    Post-processing

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    Extraction of minutiae

    _____ ___ ______ __ _____ ______ __ ___ ______

    count the number of ridge pixels in the window except middle

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    Feature extraction errors

    The feature extraction algorithms are imperfect and often introduce measurement errorsErrors may be made during any of the feature extraction stages, e.g., estimation of orientation and frequency images, detection of the number, type, and position of the singularities and minutiae, segmentation of the fingerprint area from background, etc.Aggressive enhancement algorithms may introduce inconsistent biases that perturb the location and orientation of the reported minutiae from their gray-scale counterpartsIn low-quality fingerprint images, the minutiae extraction process may introduce a large number of spurious minutiae and may not be able to detect all the true minutiae

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    Fingerprint Recognition

    Fingerprint featuresFingerprint matching

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    Intra-variability

    Matching fingerprint images is an extremely difficult problem, mainly due to the large variability in different impressions of the same finger (intra-variability). The main factors are:Displacement (global translation of the fingerprint area)RotationPartial overlapNon-linear distortion:the act of sensing maps the three-dimensional shape of a finger onto the two-dimensional surface of the sensorskin elasticityPressure and skin conditionNoise: introduced by the fingerprint sensing systemFeature extraction errors

  • *Matching illustrationExamples of mating, non-mating and multiple mating minutiae.

  • *An example of matching the search minutiae set in (a) with the file minutiae set in (b) is shown in (c).Matching illustration

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    Difficulty in fingerprint matching

    Small overlap

    Non-linear distortion

    Different skin conditions

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    Finger placement

    A finger placement is correct when user:Approaches the finger to the sensor through a movement that is orthogonal to the sensor surfaceOnce the finger touches the sensor surface, the user does not apply traction or torsion

  • *Non-linear distortion

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    Non-linear distortion

    Three distinct regions:A close-contact region (a) where the high pressure and the surface friction do not allow any skin slippageA transitional region (b) where an elastic distortion is produced by skin compression and stretchingAn external region (c) where the light pressure allows the finger skin to be dragged by the finger movement

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    Fingerprint Matching

    Minutiae-based matching: finding the alignment between the template and the input minutiae sets that results in the maximum number of minutiae pairingsCorrelation-based matching: correlation between corresponding pixels is computed for different alignments (e.g. various displacements and rotations)Ridge feature-based matching: comparison in term of features such as local orientation and frequency, ridge shape, texture information, etc.

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    Local minutiae matching

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    Minutiae correspondence

  • *Pre-alignmentAbsolute pre-alignmentThe most common absolute pre-alignment technique translates and rotates the fingerprint according to the position of the core point and the delta point (if a delta exists)Relative pre-alignmentBy superimposing the singularitiesBy correlating the orientation imagesBy correlating ridge features (e.g. length and orientation of the ridges)

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    Fingerprint matching with absolute pre-alignment

    First align the fingerprints using the global structure.Extract the core-points (prominent symmetry points) to estimate the transformation parameters v, (v from the difference in their position, and from the difference in their angle) by complex filtering of the smoothed orientation field.Then use the local structure for point-to-point matching.

    Input image Template image

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    Minutiae matching with relative pre-alignment

    Pre-alignment based on the minutiae marked with circles and the associated ridges

    Matching results, where paired minutiae are connected by green lines

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    Triangular matching

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    Ridge count

  • DT methodWe first compute the Delaunay triangulation of minutiae sets Q and P. Second, we use triangle edge as comparing index. To compare two edges, Length, 1 , 2 , Ridgecount values are used, all of which invariant of the translation and rotation.

  • Matching parameters

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    Correlation based matching

    Non-linear distortion makes fingerprint impressions significantly different in terms of global structure; two global fingerprint patterns cannot be reliably correlatedDue to the cyclic nature of fingerprint patterns, if two corresponding portions of the same fingerprint are slightly misaligned, the correlation value falls sharplyA direct application of 2D correlation is computationally very expensive

  • Example of correlation-based matchingFrom: Correlation-Based Fingerprint Matching withOrientation Field AlignmentAlmudena Lindoso, Luis Entrena, Judith Liu-Jimenez, and Enrique San Millan

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    Ridge feature-based matching

    Most frequently used features for fingerprint matching:Orientation imageSingular points (loop and delta)Ridge line flowGab