An Iris Recognition Algor

Phase

-Based Image M

An Iris Recognition Algor

Phase

-Based Image M

FRGC and ICE Workshop 

22-23 March 2006, Arlington 

Kazuyuki MIYAZAWA

1, Koichi ITO

1, Taka

Koji KOBAYASHI2 and Hiroshi NAK

1 Tohoku University, Japan 

2 Yamatake Corporation, Japan 
What Is Phase-Based Image Ma

 Using phase components in 2D Discre

Transforms (DFTs) of given images. 

Amplitude 

Phase 2D DFT 

Image 

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Biometrics with Phase-Based Image 

 Application to image registration 

C. D. Kuglin et al., “The phase correlation image alignment method,” Proc. Int. Conf. Cybernetics and Society, 1975. 

 Application to biometrics (fingerprint) 

H. Nakajima et al., “Pattern collation apparatus based on spatial frequency characteristics,” US Patent, 1995. 

 Commercial fingerprint verification unit (1998)  Other successful applications (biometrics) 

 Iris recognition  3D face recognition  Palmprint recognition 

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Outline 

 Background  Preprocessing stage  Matching stage  Experimental results of ICE 2005  Conclusion and future plans 

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Preprocessing Stage 

Eyelid 

Iris 

Pupil Sclera 

Size of an iris vary depending on environment 

Eyelid 

Iris norm

Iris loc

Origina

Normaliz

Contrast e

 Remove the parts that are not necess

recognition. 

 Normalize iris region to reduce the inf

environmental factors. 

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Iris Localization 

 Based on Dr. Daugman’s approach 

1.Calculate 

integral aellipse. 

2.Compute 

derivative direction with increlengths o

 Detect the inner boundary by finding t

which maximizes the derivative. 

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Iris Normalization 

256 pixe

Normalized 

Original image 

 Use only the lower half of the iris regio Eyelid is not necessary for recognition Using the same method for detecting 

boundary, detect the eyelid boundary

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Contrast Enhancement 

Normalized image Enhanced 

 In some databases, the normalized iri

has low contrast. 

 Improve the contrast by using local hi

equalization. 

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Matching Stage 

Normalized image 1 Normali

Is the score close to 

threshold? 

Matching score 

Yes 

Effective region extraction

Phase-based image matching 

Displacement alignment 

Matching score calculation Matching score calculation 

with scale correction 

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Phase-Based Image Matchin

 Phase-Only Correlation (POC) functio

(30,20) ),( 21 nnr

Image 1 Image 2 

POC function 

n

2

POC function has a sharp peak like a delta funLocation of the correlation peak: TranslationaHeight of the correlation peak: Similarity of i

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Basic Computation Flow for PO

Spatial Domain Frequency 

n

2

k

2

n

1

k1 Input images 

f

(

n1,n

2

) 2D DFT 

F

(

k ,k

)

=

A

(

k 

1 2

F 

g

(

n ,n

)

G

(

k ,k

)

=

A

(

k 

1 2 1 2

G 

Amplit

POC function Cross-phase sp

F

(

k 

r

(

n1,n

2

)

R

(

k1,k

2

) = 

F

(

k

2D IDFT 

j{θ

F 

=

e

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Degradation of Accuracy 

M

PO

n

2

2D DFT 

2D IDFT 

n

1

n

2

k1

k2

Spatial domain Frequency domain 

 2D DFT of a normalized iris image ha

unreliable phase components in high domain. 

 The height of the correlation peak is r

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Band-Limited POC (BLPOC

Spatial domain Frequency domain 

M

BLP

Effective frequency band 

(wider in horizontal direction) 

n

2

2D DFT 

2D IDFT 

n

1

n

2

k1

k2

 Adaptively change the size of 2D IDFT BLPOC function shows much higher d

capability than the original POC functi

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Matching Stage 

Normalized image 1 Normalize

Is the score close to 

threshold? 

Matching score 

Yes 

Effective region extraction 

Displacement alignment 

Matching score calculation Matching score calculation 

with scale correction 

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Effective Region Extraction 

w1

(

) 21,max ww

Reflections of light 

w2

(

) 21,max ww

 Extract the effective regions of the same siz

two images. 

 Align the displacement between the extracte

using the peak location of POC function. 

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Matching Stage 

Normalized image 1 Normalize

Is the score close to 

threshold? 

Matching score 

Yes 

Effective region extraction 

Displacement alignment 

Matching score calculation Matching score calculation 

with scale correction 

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Matching Score Calculation 

Calculate the matching score as the maximum value of the BLPOC function within the small window. 

BLPOC function 

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Error in Iris Localization 

Without error With erro

 Error in iris localization causes slight scaling 

direction of the normalized iris image. 

 In the case of genuine matching, this reduce

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Matching Stage 

Normalized image 1 Normalize

Is the score close to 

threshold? 

Matching score 

Yes 

Effective region extraction 

Displacement alignment 

Matching score calculation Matching score calculation 

with scale correction 

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Scale Correction 

97% 95% 

Shrinking 

100% 

Normalized image1 

Expansion 

103% 105% 

Normalized image2 

 If the matching score is close to the thresho

a set of scaled images and calculate matchithe generated images. 

 Select their maximum score as the final mat

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Possible Problem in Matchin

 A problem may occur when the most of the 

iris image is covered by eyelid. 

 Extracted region becomes too small to perfo

matching. 

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Multiple Sub-Regions Extrac

Co

95 

 Extract multiple effective sub-regions from e

image by changing the width parameter. 

 Matching score is calculated by taking an av

matching scores for the sub-regions. 

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ICE Experiment (Fully Autom

 Experiment 1 (irises from right eyes) 

 1425 iris images (124 unique eyes)  Evaluation of the genuine matching score

• All the possible combinations: 12,214 

 Evaluation of the impostor matching scor

• All the possible combinations: 1,002,3

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Results 

Verification Rate 

 ROC curve 

Proposed algorithm 

Baseline alg

Baseline 

EER = 0.0891 Verification Rate (@FAR=0.001

Proposed 

EER = 0.0264 Verification Rate (@FAR=0.001

False Accept Rate 

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Unsuccessful Matching (genuin

 Miss-localization (Non-orthogonal) 

239479 

240005 

Max = 0

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Unsuccessful Matching (genuin

 Defocused 

240010 

240587 

Max = 0

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2D Fourier Phase Code (2D F

Quantized phase spectrum of normaliz

Implementation Issues 

 Proposed algorithm assumes that the 

image directly in the system. 

 Increase in the size of iris data  Low security of actual iris recognition sys

 Reduce the size of iris data.  Prevent the visibility of individual iris im

2D Fourier Phase Code (2D F

Quantized phase spectrum of normaliz

Normalized image Phase 

2D DFT Quant

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Database 

(2D FPCs) Average amplitude 

Amp. Phase 

Iris image synthesis

(2D IDFT) 

Input ima

Preproces

Is the sco

close to 

Pseudo iris image threshold

Yes 

Effective region 

Displacement a

Matching score cMatching score c

with scale cor

Matching s

Iris Recognition Algorithm Using 

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Phase Quantization and Size R

4bit 3bit 2bit 

π

π

π 5 3

π

2

π

2

2

3

8

π

π

π

4

4

4

4

−

π

−

−

π

4

π

−

5

3

π

43

8

π

3

7

π

π

8

8

π

0

π

0

π

0

7

π

−

8

8

3

π

−

4

4

−

π

π

−

π

π

π

8 8 −

−

−

2

2

2

 Data size of an iris image 

256 x 128 = 32 Kbyte 

 Data size of 2D FPC 

 with 4-bit quantization: 8 Kbyte  with 3-bit quantization: 6 Kbyte  with 2-bit quantization: 4 Kbyte  with 1-bit quantization: 2 Kbyte 

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Conclusion 

 Experimental evaluation demonstrate

use of phase components of iris imagpossible to achieve highly accurate irirecognition with a simple matching alg

Challenges for ICE 2006  Non-orthogonal iris image matching  Defocused iris image matching 

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