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Iris detection using intensity and edge information. Pattern Recognition 36 (2003) 549-562. Speaker: Jing Ming Chiuan ( 井民全 ) Feb. 21 2005. Outline. Introduction Outline of the proposed algorithm Extraction of the face regions Extraction of valleys in the face region

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Iris detection using intensity and edge information

Iris detection using intensity and edge information

Pattern Recognition 36 (2003) 549-562

Speaker: Jing Ming Chiuan (井民全) Feb. 21 2005


Outline
Outline

  • Introduction

  • Outline of the proposed algorithm

    • Extraction of the face regions

    • Extraction of valleys in the face region

    • Detection of iris candidates

    • Selecting the irises of both eyes

  • Experimental results


Introduction
Introduction

  • The face recognition has many applications

    • Personal identification,

    • Criminal investigation,

    • Security work,

    • Login authentication.

  • Face recognition takes among facial features, such eyes, and mouth


Introduction1
Introduction

  • The eyes can be considered salient and relatively stable features

    • The other feature can be estimated using the eye positions

  • Eye Detection

    • Eye position detection

    • Eye contour detection

      • Deformable template


Introduction2

Detect only eye patterns that are

similar to sample eye image

Introduction

  • Eye position detection

    • Template matching

    • Eigenspace

    • Separability filter

Eigenspace training samples


Outline of the proposed algorithm
Outline of the proposed algorithm

Valley detection

Face region detection

Candidate location

Candidate radius detection

Iris selection


About the input image

That is head rotation

about the y-axis is

(-30,+30)

About the input image

  • The image is

    • an intensity image or a colour image

    • a head-shoulder image with plain background

    • The irises of both eyes appear in the image

example


Extraction of the face regions

M

N

V(x)

x

Extraction of the face regions

  • For intensity images

Step 1

Input Image

Step 2

Sobel Edge operator

Step 3

Calculate the left

and right bound

more


Iris detection using intensity and edge information

Left bound:

The smallest x value which

> V(X0)/3

Right bound:

The largest x value which

> V(X0)/3

Y min  the smallest y that

H(y) >=0.05(Xr-Xl)

V(x)

Xl

Xr

Y min

x

Y min+1.2(Xr-Xl)

X0= the largest V(x)

Y max

Face Region Extraction


Extraction of the face regions1
Extraction of the face regions

Reference from J. Yang, A. Waibel, Real-time face tracker, Proceedings of the

3th IEEE Workshop on Applications of Computer Vision, 1996

  • For color images

    • A skin-color model is used

    • the r-g color space

Step 1

collect the skin

region on the face images

Step 2

Build the

Gaussian distribution model


The gaussian distribution

The probability density function

The mean vector

The covariance matrix

The Gaussian Distribution

Referece: http://mathworld.wolfram.com/NormalDistribution.html

Non-skin

skin

Basic normal distribution


Extraction of the face regions for color image
Extraction of the face regions-- for color image

Step 1

Select pixels (x,y) whose color values

v=(r,g) satisfy g(v) > ε

Skin-color pixels

Step 2

Apply a closing and an opening to the

region of the skin-color pixels

5x5

9x9

Noise removing

Step 3

Find the connected component with

the largest area.

Step 4

the boundary of the area is defined as

the face region


Extraction of valleys in the face region

neighborhood maximum operator

Ref: http://www.astro.princeton.edu/~esirko/idl_html_help/D24.html#wp747869

If(V(x,y)>T)

V(x,y) valley pixel

Else

V(x,y)  non-valley pixel

-

Original

Grayscale closing

Extraction of valleys in the face region

Valley detection

Face region detection

binary image

Extract the valley image

V(x,y)=G(x,y) - I(x,y),

Gray scale closing


Iris detection using intensity and edge information

is the maximum of V(x,y)

=0.1

is the # of pixels in the region such that V(x,y)= i

is the # of pixels in the region

  • The threshold value was set to the largest value T satisfying

Collect 10% of valleies

h(T)

h(T+1)

h(T+n)

h(MAX)


Detection of iris candidates

Reference from C.H. Lin, J.L. Wu, Automatic facial feature extraction by genetic

Algorithm, IEEE Trans. Image Process, 1999

Select m pixels according to nonincreasing order of C(x,y)

(m=20)

Vc= 1 1 1 1 1

Vr(0)=2

Vr(1)=2

Vr(2)=1

Vr(3)=2

Detection of iris candidates

Computers the valley costs

Candidate location


Measure the separability

Perfect match case extraction by genetic

P2 smaller  B smaller  η smaller

P2larger B larger  ηlarger

example

Reference from K. Fukui, O. Yamaguchi, Facial feature point extraction method based

On combination of shape extraction and pattern match, Trans. IEICE Japan 1997

Measure the separability


Find the optimal radius

Perfect match case extraction by genetic

η1 η2ηtηt+1ηu

Find the optimal radius

  • Changing the size r in the range,

    we find the size r maximizingη(r)

For AR face database rl =10, ru=13

For Bern face database rl=5, ru=7


Selecting the irises of both eyes
Selecting the irises of both eyes extraction by genetic


The cost of an iris

B extraction by genetici亮度越低越好 => 小

越 balance 越好 => 小

The factor of separation balance

越 balance 越好 => 小

The cost of an iris

  • Let B iare the iris candidates

    • Compute the cost for B i

The Hough transform vote


The hough transform

2r extraction by genetic

2r

r

r

Largest vote (a,b)

The Hough Transform

Edge point

r

(Xi,Yi)

Edge point


Costs for pairs of iris candidates
Costs for pairs of iris candidates extraction by genetic

Bi

Bj

dij

The width of the face region

The pair of the eyes

The cost of a pair of iris candidates Bi and Bj

The cost of an iris

Cross-correlation value


Cross correlation value

The template for AR face database extraction by genetic

Cross-correlation value

Step 1 Matching the template

Affine Transform

Step 2 the correlation value

The eye templates for Bern face database

R(i,j)=

Step 3

if R(i,j) <0.1 then R(i,j)=0.1



Experimental results
Experimental Results extraction by genetic

  • Two databases are used

    • The face database of University of Bern

    • The AR face database


The face database of university of bern
The face database of University of Bern extraction by genetic

Size: 512 x 342 with 150 faces without spectacle


Iris detection using intensity and edge information

95.3% (avg) extraction by genetic

The proposed algorithm is not sensitive to the variation of the template

All 150 faces

120 faces without

The looking-down faces


Iris detection using intensity and edge information

False detection of the irises extraction by genetic


The execution time
The execution time extraction by genetic

PentiumIII 700Mhz


Iris detection using intensity and edge information

More valley pixels extraction by genetic


Iris detection using intensity and edge information

all correlation factor extraction by genetic

No correlation factor


Comparison
Comparison extraction by genetic

Excluding training set


The ar face database
The AR face database extraction by genetic

  • Color images with size 768x576 without spectacles

  • Natural illumination condition with different expressions

  • 63 face images are used

Success rate = 96.8%, false image=2


Remove the light spot

Histogram Equalization extraction by genetic

Remove the light spot

The light spot

Step 2

Replace by the smallest intensity value


Iris detection using intensity and edge information

Successful images extraction by genetic


Conclusion
Conclusion extraction by genetic

  • We proposed a new algorithm to extract the irises of both eyes

  • A simple method is used to remove the light spot of the eye