RAMAKRISHNA LANKA ADVISING PROFESSOR: DR. K. · PDF file · 2016-05-07Examples:...
Transcript of RAMAKRISHNA LANKA ADVISING PROFESSOR: DR. K. · PDF file · 2016-05-07Examples:...
RAMAKRISHNA LANKA
MSEE, UTA
ADVISING PROFESSOR: DR. K. R. RAO
Personal identity management
Exponential growth of population and
migration of people is a challenge to
person management.
Risk of identity theft.
Crucial societal applications are based on
personal identity.
Personal attributes define the unique
identity.
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Personal identity management 4
Figure 2: Biometrics to validate individuals [1]Figure 1: Traditional schemes to validate individuals [1]
Aishwarya Rai
Need for biometrics Traditional person recognition relies on
surrogate representations of identity.
Knowledge based person recognition
mechanisms like PINs and passwords are
not reliable.
Biometric recognition, or simply biometrics,
offers a natural and more reliable solution.
Biometric identifiers are inherent.
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Biometric Systems
The term biometrics is derived from the
Greek words bio (life) and metric (to
measure).
Biometrics is the measurable biological
and behavioral trait unique to a person.
Examples: fingerprints, face, iris, voice, gait,
or the Deoxyribonucleic acid (DNA).
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Biometric Systems There are 2 phases to a biometric system:
Enrollment
Recognition
The biometric system consists of 4 basis
components:
Sensor
Feature extractor
Database
Matcher
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Biometric Systems 8
Figure 3: Block diagram of a biometric system [1]
The Fingerprint biometric 9
Finger-scan technology is a widely
deployed biometric technology.
The fingerprint biometric is highly accurate
and versatile.
Low-cost and small-size of fingerprint
acquisition devices.
Figure 4: Smooth skin [1] Figure 5: Friction ridge skin on the
fingertips [1]
The Fingerprint biometric
How it works10
Fingerprint recognition is feature-based.
For optimal feature extraction, the image
is enhanced and thinned to one pixel
wide.
Figure 6: Grayscale
fingerprint image [1]Figure 7: Thinned
fingerprint image [1]
Figure 8: Ridge ending and
bifurcation [1]
The Fingerprint biometric
How it works11
A minutiae set is an abstract representation
of the ridge skeleton.
The minutiae set is then matched with a
template to compute the match score.
Figure 9: Minutiae matching process [1]
Figure 10: A genuine
pair with maximum
matched minutiae [1]
Figure 11: An imposter
pair with very few
matched minutiae [1]
The Fingerprint biometric
Need for image enhancement12
Lack of robustness against image quality
degradation.
Several factors determine the quality of a
fingerprint image.
In an ideal fingerprint image, ridges and
valleys alternate and flow in a locally
constant direction.
Poor quality images result in spurious and
missed features.
The Fingerprint biometric
Need for image enhancement
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Figure 12: Examples of low quality fingerprint images: (a) dry finger, (b) wet
finger, and (c) finger with many creases [1].
Fingerprint image enhancement 14
The image enhancement is carried out in
either spatial or frequency domain.
Spatial domain.
g(x,y)=T[f(x,y)]
Frequency domain .
g(x,y)= ^(-1) [H(u,v)F(u,v)]
Figure 13: Fingerprint image enhancement process [14]
Tuned Gabor filtering 15
The state of the art fingerprint enhancement
technique is employed by L. Hong et al. [16].
Principle: Convolution of the image with
Gabor filter.
The filter is tuned to the local ridge
orientation and ridge frequency.
Tuned Gabor filtering 16
Figure 14: Flowchart of the L. Hong et al. fingerprint enhancement algorithm [13]
Tuned Gabor filtering -
Normalization17
Normalization is used to standardize the
intensity values in an image.
Normalization does not change the ridge
structures in a fingerprint.
Figure 15: The result of normalization. (a) Input image. (b) Normalized image [13]
Tuned Gabor filtering -
Orientation image estimation18
The orientation image is an intrinsic property of the
fingerprint images.
Figure 16: Orientation estimation at pixel (x,y) [13]
Divide the image into blocks of size w x w.
Compute gradients Vx(i,j) and Vy(i,j) at each pixel (i,j).
Estimate the local orientation of each block centered at
pixel (i,j)
Tuned Gabor filtering
Frequency image estimation19
Local ridge frequency is another intrinsic property of a
fingerprint image.
The gray levels along ridges and valleys are modeled
as a sinusoidal wave along the local ridge orientation.
The image is divided into blocks of size wxw.
The x-signature X[0],X[1],,X[l-1] of the ridges and
valleys within the window is computed.
The frequency of ridges and valleys can be estimated
from the x-signature.
Tuned Gabor filtering
Frequency image estimation20
X k =1
=0
1
G u, v , k = 0,1, . , l 1
= +
2 , +
2 ,
= +
2 , +
2 ,
Figure 17: Oriented window method to estimate the ridge frequency [13]
Tuned Gabor filtering
Filtering21
The estimated frequency and orientation in a fingerprint image provide useful information which helps in removing undesired noise.
A bandpass filter that is tuned to the estimation can efficiently remove noise and preserve the true ridge and valley structures.
Gabor filters have both frequency-selective and orientation-selective properties.
Tuned Gabor filtering
Filtering22
The general form of a Gabor filter is:
, : , = 1
2
2
2 +
2
2 cos 2
The enhanced image E is obtained by:
, = =
2
2 =
2
2 , : , , , ,
Figure 18: Original scanned fingerprint [13] Figure 19: Enhanced fingerprint image [13]
Tuned Gabor
filtering
Related work done 23 Ridge structures that are affected by unusual
input contexts can be very complicated.
Not all unrecoverable regions can be
recovered, as it is difficult to accurately
estimate filter parameters in bad sections.
A two-stage scheme to enhance the low-
quality fingerprint image in both the spatial
domain and the frequency domain was
proposed by J.Yang et al [9].
Two-stage enhancement 24
Figure 20: Flowchart of the two-stage enhancement algorithm [9]
Two-stage enhancement
Spatial Ridge Compensation25
The first stage performs ridge compensation along
the ridges in the spatial domain.
This stage increases the ridge contrast.
This stage consists of three steps:
Local normalization
Local orientation estimation
Local ridge-compensation filtering
Two-stage enhancement
Spatial Ridge Compensation26
The Local normalization and orientation estimation is
similar to the Tuned Gabor filter algorithm [16].
With the orientation of each sub-image estimated, a
oriented rectangular window h x w is created.
The ridge compensated image is:
, =(=(1)/2
(1)/2=(1)/2(1)/2
(,))
(((1)+))
= + cos , + sin ,
= sin , + cos ,
Figure 21: Window along the local
ridge orientation [9]
Two-stage enhancement
Spatial Ridge Compensation27
Figure 22: Original
fingerprint image [9].
Figure 23: normalized
image[9].
Figure 24: First-stage
enhanced image[9]
Two-stage enhancement
Frequency Bandpass Filter28
The result of the first spatial filter increases the ridge contrast.
The frequency bandpass filters used are orientation and frequency selective.
The Filter used is the Gabor Filter as in the state of the art approach [16].
Figure 25: Original fingerprint image [9].
Figure 26: First stage enhanced image [9].
Figure 27: Final enhanced image [9].
Image pyramids 29
The task Image pyramids or multi-
resolution processing is to decompose
images into multiple information scales.
The Laplacian Pyramid Decomposition
and Reconstruction (LPD and LPR) are
used.
The Laplacian pyramid is relevant in this
study as all the relevant information is
concentrated within a few frequency
bands.
Image pyramids 30
Image pyramid coding is to low-pass filter
the original image go to obtain image g1,
which is a reduced version in a way that
both resolution and sample density are
decreased. In a similar way form g2 as a
reduced version of g1, and so on.
The sequence g0,g1,,gn is called the
Gaussian pyramid.
The decomposition method in image
pyramids is known as reduce function
and reconstruction is known as expand.
Image pyramids 31
Take an image g0 with C x R dimensions
and an equivalent weighing function h,
then the reduced image gl = REDUCE(gl-1)
, =
=2
2
=2
2
, 1 2 + , 2 +
The