fingerprint recognition technique(ppt)

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Fingerprint RecognitionProject ID-1044

BySandeep Kumar Panda Roll# ECE200910024Sailendra Sagar Patra Roll# ECE200910023

Under the guidance of

Mrs. T. Mita Kumari

Sandeep Kumar Panda Roll # ECE200910024Sailendra Sagar Patra Roll # ECE200910023

B.Tech Project Presentation-2013

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Outline…….• Objective• What is Fingerprint?• What is Fingerprint Recognition?• Algorithms For Fingerprint Recognition.• Preprocessing Stages.• Minutia Extraction.• Minutia Match.• Result And Discussion• Conclusion

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Objective Of Our Project• The objective is to implement Fingerprint Recognition Algorithm by Using

Minutia Extraction and Minutia Matching.

• The objective is to implement fingerprint recognition algorithm. The Region of Interest (ROI) for each fingerprint image is extracted after enhancing its quality.

• That is used to extract the minutia, followed by minutiae extraction.

• Application :• Data Security

• Crime Investigation

• Security Lock



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What Is Fingerprint?• Skin on human fingertips contains

ridges and valleys which together forms distinctive patterns. These patterns are called FINGERPRINTS.

• However, shown by intensive research on fingerprint recognition, fingerprints are not distinguished by their ridges and furrows, but by features called Minutia, which are some abnormal points on the ridges.



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• Among the variety of minutia types reported in literatures, two are mostly significant and in heavy usage:

1.Ridge ending- the abrupt end of a ridge

2.Ridge bifurcation- a single ridge that divides into two ridges



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What Is Fingerprint Recognition?• Fingerprint recognition is the process

of comparing questioned and known fingerprint against another fingerprint to determine if the impressions are from the same finger or palm.

• It includes two sub-domains: one is fingerprint verification and the other

is fingerprint identification.



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Algorithms For Fingerprint Recognition



Load Image

Histogram Equalization

Enhancement Using FFT

Binarization

Ridge Ending

ROI

Thinning

Minutia Marking

Align And Match Template

Save Template

Image Acquisition

Preprocessing Stages

Minutia Extraction

Minutia Match

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Pre Processing Stages….• Histogram Equalization:1. Histogram equalization is a technique of improving the global contrast of an image

by adjusting the intensity distribution on a histogram.



Original Histogram Histogram After Equalization

Histogram Equalization Image

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Pre Processing Stages….• Enhancement by Fourier transform:

The image enhancement by FFT is done by the following formula:

Where,

for x=0,1,2,…….,31 and y=0,1,2,…….,31.



kvuFvuFFyxg ,,, 1

1

0

1

0

2exp,,M

x

N

y N

vy

M

uxjyxfvuF

Enhanced FFT Image

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Pre Processing Stages….• Binarization:• Fingerprint Image Binarization is to transform the 8-bit Gray fingerprint

image to a 1-bit image with 0-value for ridges and 1-value for furrows.• After the operation, ridges in the fingerprint are highlighted with black

colour while furrows are white. A locally adaptive binarization method is performed to binarize the fingerprint image.

• Such a named method comes from the mechanism of transforming a pixel value to 1 if the value is larger than the mean intensity value of the current block (16x16) to which the pixel belongs



Binarized Image

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Pre Processing Stages….B.Tech Project Presentation-2013


• Block Direction Estimation• The direction for each block of the fingerprint image with WxW in size(W is 16

pixels by default)is estimated. • The direction for each block of the fingerprint image with WxW in size(W is 16

pixels by default)is estimated.• The gradient values along x-direction (gx) and y-direction (gy) for each pixel of

the block is calculated.• For each block, following formula is used to get the Least Square

approximation of the block direction.

22

2tan

yx

yx

gg

gg

Block Direction Image

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Preprocessing Stages……• ROI(Region Of Interest):• Two Morphological operations called ‘OPEN’ and ‘CLOSE’ are adopted.• The ‘OPEN’ operation can expand images and remove peaks introduced by

background noise.• The ‘CLOSE’ operation can shrink images and eliminate small cavities.



ROI Image

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Minutia Extraction……..• Ridge Thinning:• Ridge Thinning is to eliminate the redundant pixels of ridges

till the ridges are just one pixel wide.

• An iterative, parallel thinning algorithm is used for ridge thinning.



Thinned Image

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Minutia Extraction……..• Minutia Marking:• After the fingerprint ridge thinning, marking minutia points is relatively easy.

The concept of Crossing Number (CN) is widely used for extracting the Minutia.



0 1 0

0 1 0

1 0 1

0 0 0

0 1 0

0 0 1

Bifurcation Termination

Marked Image

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Minutia Matching……..• Minutia match algorithm determines whether the two minutiae sets are

from same finger or not.• It include two stages:-

– Alignment Stage– Match Stage

• Alignment stage:- Given two fingerprint images to be matched, any one minutia from each image is chosen, and the similarity of the two ridges associated with the two referenced minutia points is calculated

• Match stage: After obtaining two set of transformed minutia points, the elastic match algorithm is used to count the matched minutia pairs by assuming two minutia having nearly the same position and direction are identical.



Matched Image

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RESULT AND DISCUSSION• Histogram Equalization Image:



Original Image Image After Histogram Equalization

Algorithm

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RESULT AND DISCUSSION• Enhancement by Fourier transform:



Image After Histogram Equalization

Algorithm

Image After FFT Enhancement

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RESULT AND DISCUSSION• Binarization:



Algorithm

Image After FFT Enhancement Binarized Image

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RESULT AND EXPERIMENT• Block Direction Estimation:• :



Algorithm

Binarized Image Block Direction Estimation

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RESULT AND EXPERIMENTB.Tech Project Presentation-2013


Algorithm

Binarized Image Open Operation Close Operation ROI+Bound

•ROI(Region Of Interest):

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RESULT AND EXPERIMENT• Ridge Thinning:



Algorithm

ROI Image Thinned Image

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RESULT AND EXPERIMENT• Minutia Marking:



Algorithm

Thinned Image Minutia Marked Image

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RESULT AND DISCUSSION• Minutia Matching:• Here we had taken two different sets of fingerprints. 1.Two different angles of a same fingerprint 2. Fingerprints of two different finger.• Using Match Score we distinguish two fingerprints are same or not.



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RESULT AND DISCUSSIONB.Tech Project Presentation-2013


•The match score value between the two images is 0.67.• This value is greater or same as threshold value.•We conclude that these two fingerprints are of same person.

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RESULT AND DISCUSSIONB.Tech Project Presentation-2013


•The match score value between the two images is 0.37.• This value less than threshold value.•We conclude that these two fingerprints are of two different persons.

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Conclusion



• The above implementation was an effort to understand how Fingerprint Recognition is used as a form of biometric to recognize identities of human beings.

• It includes all the stages from enhancement to minutiae extraction of fingerprints.

• There are various standard techniques are used in the intermediate stages of processing.

• At last minutiae extraction and comparison happens.

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Reference



• Fingerprint database - FVC2002 (Fingerprint Verification Competition 2002)

• Rafael C .Gonzalez, Richard E Woods “Digital Image Processing”2nd edition, 2002.

• K. Jain, F. Patrick, A. Arun , “Handbook of Biometrics”, Springer Science Business Media, LLC, 1st edition, pp. 1-42, 2008.

• D. Maio, and D. Maltoni, “Direct gray-scale minutia detection in fingerprints”, IEEE Transactions Pattern Analysis and Machine Intelligence, vol. 19(1), pp. 27-40, 1997.

• D. Maltoni, D. Maio, and A. Jain, S. Prabhakar, “4.3: Minutiae-based Methods’(extract) from Handbook of Fingerprint Recognition”, Springer, New York, pp. 141-144, 2003.

• E. Hastings, “A Survey of Thinning Methodologies”, Pattern analysis and Machine Intelligence, IEEE Transactions, vol. 4, Issue 9, pp. 869-885, 1992.

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THANK YOU


fingerprint recognition technique(ppt)

Engineering

fingerprint image binarization

gray fingerprint image

tech project presentation

known fingerprint

ece200910023 binarized

image enhancement

ece200910023 outline

minutia matching