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.

3

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.

5

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).

6

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

7

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

13

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