Method of performing fingerprint matching

摘要

The method of performing fingerprint matching includes a matching algorithm that combines affine moment invariants (AMIs) and translation, rotation and scaling invariants (TRS) based on moments analysis without requiring minutiae detection. Preprocessing normalizes the fingerprint image using the Federal Bureau of Investigation's wavelet scalar quantification (WSQ) compression standard definition. Analysis of the orientation field reliably and accurately determines the reference point. The area within a predetermined range around the detected reference point is used as a region of interest (ROI) for feature extraction. A directional filter bank transform (DFB) obtains directional components of the image. An invariant moment analysis on sub-bands of the filtered images extracts features while limiting the effects of noise and non-linear distortions. Absolute distance is used to match the fingerprints.

主权项

1. A computer-implemented method of performing fingerprint matching, comprising the steps of:
acquiring a first fingerprint image; locating a reference core point within the first fingerprint image in order to initiate feature extraction of the first fingerprint image; establishing a region of interest (ROI) around the reference core point; decomposing the region of interest into eight separate directional sub-band outputs; calculating translation, rotation, and scaling (TRS) independent moment invariants and affine moment invariants from each of the eight separate directional sub-band outputs by:
calculating at least one geometric moment invariant Mpq, said geometric moment invariant calculation being defined by the relation Mpq=∫−∞∞∫−∞∞xpyqf(x,y)dxdy for p, q=0, 1, 2, . . . , n, where n represents a number of stored template features, x and y are Cartesian coordinates, and f(x,y) is a continuous function;adapting the geometric moment invariant calculation to grayscale images having pixel intensities I(x,y), the geometric moment invariant calculation adaptation being characterized by the relation MIj=ΣxΣyxIyjI(x,y) for I,j=0, 1, 2, . . . , n;calculating at least one complex moment invariant cpq, the complex moment invariant calculation being defined by the relation cpq=∫−∞∞∫−∞∞(x+iy)p(x−iy)qf(x,y)dxdy, where i is the complex number, the at least one complex moment invariant being based on a polynomial basis kpqcharacterized by the relation kpq(x,y)=(x+iy)p(x−iy)q; andcalculating at least one orthogonal moment vpq, the orthogonal moment invariant calculation being defined by the relation vpq=npnq∫∫Ωpp(x)pq(y)f(x,y)dxdy, wherein npand nq are normalized factors and Ω is an area of orthogonality, the grayscale image being scaled such that its support is contained in Ω; combining features calculated from each of the separate directional sub-band outputs into a first feature vector Vf characterized by the relation Vf={Iθ1, Iθ2, . . . , Iθk}, where θε{0, 1, 2, 3, 4, 5, 6, 7}, corresponding to the eight separate directional sub-band outputs, k ε{0, 1, 2, . . . , 56}, corresponding to the independent moment invariants from each of the eight separate directional sub-band outputs, and Iθkis a feature sub-block of the θ-th directional sub-band and the k-th independent moment invariant; calculating an absolute distance between the first feature vector and a second vector Vf2including features of a second fingerprint having similarly obtained moment invariants, the second vector being characterized by the relation Vf2={b1, b2, . . . , bn}, where each of the {b1, b2, . . . , bn}represent one of the n stored template features; and displaying match results based on the first and second fingerprint absolute distance calculation step.