![]() Diefenderfer,"Fingerprint Recognition", NavalPostgraduate SchoolMonterey, California. Rohit Singh (Y6400), Utkarsh Shah (Y6510), Vinay Gupta (Y6534), "Fingerprint Recognition", Department of Computer Science & engineering Indian Institute of technology, Kanpur.Lokhande, Rotation –Invariant Fingerprint Identification System International Journal of Electronics Communication and Computer Technology (IJECCT) Volume 2 Issue 4 (July 2012). The purpose of this research paper was to implement fingerprint recognition algorithm using minutiae matching with the help of an image processing with programming tool MATLAB. And the fingerprint matching is accomplished by comparing the point's data of two fingerprint impressions. The prototype of algorithm performs two operations: first, to calculate available points in image and second, to find out location of those points. This research paper establishes correspondence between two fingerprints based on ridge ending and bifurcation points. All the biometric techniques have received the most attention for person identification. These substructure pairs are basically, ridge ending and bifurcation points. Minutiae-based techniques are work on substructure pair. One of the important, fingerprint matching is minutiae-based. _color(DispImg, (rr, cc), (0, 0, 255)) ĭef extract_minutiae_features(img, showResult=False):įeature_extractor = FingerprintFeatureExtractor()įeaturesTerm, FeaturesBif = feature_extractor.The advent of Digital Fingerprint processing system motivates to review new concepts of fingerprint matching algorithm. TermLabel = (self.minutiaeTerm, connectivity=2) ĭispImg = np.zeros((rows, cols, 3), np.uint8) Hence the window selected is 1Īngle = self._computeAngle(block, 'Bifurcation')įeaturesBif.append(MinutiaeFeature(row, col, angle, 'Bifurcation'))įeaturesTerm, FeaturesBif = self._performFeatureExtraction()īifLabel = (self.minutiaeBif, connectivity=2) WindowSize = 1 # -> For Bifurcation, the block size must be 3x3. Self.minutiaeBif = (self.minutiaeBif, connectivity=2) ![]() (row, col) = np.int16(np.round(i))īlock = self._skelĪngle = self._computeAngle(block, 'Termination')įeaturesTerm.append(MinutiaeFeature(row, col, angle, 'Termination')) If(dist For Termination, the block size must can be 3x3, or 5x5. Self.minutiaeTerm = np.uint8(self._mask) * self.minutiaeTermĭef _removeSpuriousMinutiae(self, minutiaeList, img, thresh): Self._mask = erosion(self._mask, square(5)) # Structuing element for mask erosion = square(5) Self._mask = convex_hull_image(self._mask > 0) Self.minutiaeBif = np.zeros(self._skel.shape) īlock = self._skel Self.minutiaeTerm = np.zeros(self._skel.shape) If ((i = 0 or i = blkRows - 1 or j = 0 or j = blkCols - 1) and block != 0):Īngle.append(grees(math.atan2(i - CenterY, j - CenterX)))Įlif (minutiaeType.lower() = 'bifurcation'): Self._skel = (img)ĭef _computeAngle(self, block, minutiaeType):ĬenterX, CenterY = (blkRows - 1) / 2, (blkCols - 1) / 2 This is what is inside the fingerprint-feature-extractor library: import cv2įrom skimage.morphology import convex_hull_image, erosionĭef _init_(self, locX, locY, Orientation, Type):Ĭlass FingerprintFeatureExtractor(object): I tried reading the classes in the library and I figure I could get those values (features locations, orientations, and type is explicitly stated in the library), but i do not know how to extract those values. Minutiaes bifurcations (marked with 1 in the last column) and terminations (marked with 0 in the last column) values extracted from fingerprint What I needed is values that looks like this where first and second column indicates xy coordinates, third column indicates orientations, and fourth column indicate type: I tried using print() command to see what's inside FeaturesBifurcations and I can't understand what the output means. I used this code that's included in the github link to get features bifurcations and terminations: import fingerprint_feature_extractorįeaturesTerminations, FeaturesBifurcations = fingerprint_feature_extractor.extract_minutiae_features(img, showResult=True, spuriousMinutiaeThresh=10) The problem is I need to extract terminations and bifurcations value from the library. I recently tried the new fingerprint feature extractor library by Utkarsh-Deshmukh ( ) and it works like wonder.
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