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FOCAL POINT LOCALIZATIONALGORITHM FORFINGERPRINT REGISTRATION
BY
NEMISHA KHOSA
85008
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CONTENETS
INTRODUCTION
PREVIOUS ALGORITHMS
FOCAL POINT LOCALIZATION
ALGORITHM
CONCLUSION
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INTRODUCTION
Biometrics is the Science and
Technology of Measuring and
Analyzing Biological data.
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It is an Automated Method of recognizing a person based on aPhysiological orBehavioral characteristics
The Features measured are, Face
finger print
hand geometry
hand writing
Voice
Iris blood vessels in the retina
coloration in the cornea of the eye
DNA from tissue samples
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FINGERPRINT REGISTRATION fingerprints are the traces of an impression from the
friction ridges of any part of a human hand
fingerprint registration are required to perform real-
time large-scale automatic fingerprint identification
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PREVIOUS ALGORITHMS Pattern- Based
Poincare by Kawagoe and Tojo
Complex symmetrical filters, by Nilsson andBigun.
Projection- Based
hierarchical analysis of the orientation
coherence by Jing etc
local axial symmetry fields by Liu etc
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FOCAL POINT LOCALIZATIONALGORITHM The proposed algorithm composes of 4 processes:
Pre-processing
Crossing-points localization
Initial block localization
Focal point localization
Focal point Quality and Assessment
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PRE-PROCESSING Composes of fingerprint partitioning and directional
field estimation
Original fingerprint partitioned into 1616 blocks
Bazen and Gerezs approach
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CROSSING POINTS LOCALIZATION The crossing point is defined as an intersection of two
straight lines which is perpendicular to two
orientation fields
The following equation is used to get the straight line:
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Assume that two straight-line equations, l1and l2 ,
are
The crossing point that we can get from the two line(
l1, l2)
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from (4) and (5), m2m1 cannot be zero i.e. the two
lines cannot be parallel.
If some line is parallel or almost parallel to the
other, the crossing point will be located very far
away or outside a fingerprint image.
only the crossing points in this effective area will
be employed in calculating the focal point.
Crossing
point
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INITIAL BLOCK LOCALIZATION The block which contains a maximum number of
crossing points is the initial block.
The centre of this block is a starting point for the
next process.
Initial
block
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FOCAL POINT LOCALIZATION ALGORITHM
START
Set centre of initial block to a centroid
Find Effective Area by the Centroid
Find the New Centroid
Focal Point =Centroid
Measure Centroid Shift
END
Convergence
?
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THE FOCAL POINT LOCALIZATIONALGORITHM Step 1:Set iteration time to zero (i = 1), and set the
centre of the initial block to be a centroid (xct
(0),yct(0)).
Step 2:Select a top-half circle area. The top-half
circle area can be defined with radiusR (blocks) asshown in Fig
Step 2 The top-half circle
area with initial block as the
centre
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Step 3:Generate the crossing points by using only
orientation fields in this top-half circle area, and find
the new centroid with the help of the equation:
where (xp(j),yp(j)) is the jth crossing point generated
by two lines which are perpendicular to two
orientation fields in thetop-half circle area.
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Step 4: Find the (i)th shifted distance, or (i), between the
oldcentroid point (xct(i1),yct(i1)) and the new centroid
point(xct(i),yct(i)), given by
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Step 5 : Check convergence or divergence condition ofthis centroid.
1. related to focal point convergence
if (distance) (i) (threshold)T
then iteration stops and this centroid is defined as the focal point.
2. related to focal point divergence
if (cumulative shifted distance) (i) (threshold) T
then iteration stops and the focal point is diverged
3. Else the new centroid (xct(i)
,yct(i)
) is replaced by the previousone,(xct(i1),yct(i1)), and repeat the step 2 through step 5 again
until the iteration ends.
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FOCAL POINT QUALITY ANDASSESSMENT The number of crossing points, which contributed to
the stability of the detected focal point, can be used as
focal point quality parameter
The one, which obtained the highest number of the
crossing points, should be selected as the final focal
point.
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ERROR MEASURMENT results in higher accumulation error than the other
techniques
Manually select 3 minutiae points
linearly project the detected focal point of afingerprint into another fingerprint with less error as
possible.
measure a distance error (DE) between 2 detected
focal points from two different impressions of the
same finger.
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CONCLUSION The focal point is shown to be a very stable point for
fingerprint registration.
Moreover, the algorithm also consumed only 1/7 executiontime compared to the previous scheme (88.6 millisecond).
Future research also exploited the focal point applications in
fingerprint classification and fingerprint recognition.
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REFERENCE
Vutipong Areekul and Natthawat Boonchaiseree , Fast Focal Point
Localization Algorithm for Fingerprint Registration , IEEE, Kasetsart
University Thailand, Page no 2089-2094
Fingerprint Recognition : Image Processing and Computer Vision By Vinay
Gupta & Rohit Singh
Wikipedia
ACM Digital Library
Papers by Vutipong Areekul, Kittiwat Suppasriwasuseth, Suksan Jirachawang
A.M. Bazen and S.H. Gerez, Systematic methods for the computation of the
directional fields and singular points of fingerprints, IEEE Trans. Pattern
Anal. Machine Intell., vol.24, pp. 905-919, July 2002
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