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Published byRodger Barnett Modified over 8 years ago
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Fingerprint Image Enhancement 程广权
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Introduction Problems – Image contrast – Adverse physical factors Minimize the undesired effects Some intermediate steps
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Fingerprint Image Digitalization and Density Resolution: – 500 dpi ( dots per inch ) – 8bits depth ( i.e. 256 gray level ) Some feathers – Pores – Valleys – Incipient Ridges – Ridges
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Recognition of Fingerprint Ridge
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Enhancement result
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Intermediate Steps in Fingerprint Image Enhancement Contrast enhancement or normalization Pore and incipient ridge removal Ridge orientation estimation Frequency estimation Foreground segmentation Ridge enhancement filtering
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Contrast enhancement
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Pore and incipient Ridge Removal Pore and incipient ridge are obstacles in frequency estimation
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Pore and incipient Ridge Removal Methods : – Remove the minutiae originated by pores – Pores are enclosed by darker pixels(easy to confuse with some type of ridges) These two factors have not yet been fully explored, and plays an important role.
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Ridge Orientation Estimation Estimation Methods: – Slits – Two-dimensional gradient – Two-dimensional Fourier transform Ridge orientation smoothing Suggestion: use global pattern type and prior knowledge of ridge flow
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Frequency Estimation Frequency: the number of ridges per unit length Frequency can be rather unstable, hard to estimate.(unlike orientation) Methods: – Two-dimensional Fourier transform – Peak intervals orthogonal to the ridge orientation
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Frequency Estimation
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Foreground Segmentation Distinguish the fingerprint ridge region from the background Methods: – Base on the confidence of the orientation – Base on the gray-level analysis(not fit for low quality images)
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Ridge Enhancement Filtering Methods: – Use filtering mask with fixed sizeor predetermined variable frequency – Two-dimensional Fourier transform(Gabor and wavelet filtering) Mimutia – Stable – Unstable
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Ridge Enhancement Filtering
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Strength of the enhancement – Law enforcement, for accuracy, not strong – Non-law enforcement, automatic, strong enhancement is OK Strong enhancement is beneficial to extract stable minutiae even from poor quality images
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Ridge Enhancement Filtering
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After the enhancement Fingerprint binarization Fingerprint skeletonization
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thanks Q&A
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