High Performance L

High Performance L

Complexity DCT Complexity DCT

-bas

Iris Recognition Iris Recognition

Donald M Monro, Soumyadip Rakshit and 

Electronic and Electrical Engineering, University o

Bath BA2 7AY, United Kingdom

S.Rakshit@bath.ac.uk S.Rakshit@bath.ac.uk

Http://dmsun4.bath.ac.uk Http://dmsun4.bath.ac.uk

NIST FRGC ICE Workshop 23 March 2006
Outline Outline

• Data Collection• Iris Recognition System

• Feature Extraction & Weighting

• Classifier Design• Proposed Metric• Results

• Ongoing & Future Work
Bath Iris Image Databas

Bath Iris Image Databas

Currently 16,000 Images 

from 400 Subjects (800 Eyes)

Mid-2006 Target 32,000 Images 

from 800 Subjects (1600 Eyes)

http://www.bath.ac.uk/elec http://www.bath.ac.uk/elec

-eng/pages/sip

eng/pages/sip
Non Ideal Images Non Ideal Images

Blinking, Out-of-focus, Motion Blur, Out of 
Iris Recognition System Iris Recognition System

Image 

Acquisition Localization

Normalization

Eye Image

Decision Classifier

Enrolled

Database

Match 
Feature Extraction Feature Extraction

• Image divided in diagonal 8 x 12 patches [BMVA 04, ICIP 05]

Vertical Vertical 

Space

Angle

• 50% overlap in both directions• Windowed average over width • Windowed 1D DCT of length 12 over length• Adjacent DCTs differenced• Zero Crossings form Feature Vector

Average DCT
Weighting Weighting

1 2 

280300320340360380400420440460480

Normalized HD Sum

• Blue and Brown Iris Structures differ.

• Positional weightings effective withinethnic groups but ineffective across groups.

• DCT Coefficient weighting is effective in choosing the most discriminating bits and reducing the Feature Vector Size.

• Most effective sub-feature bits 1, 2, 3.• Final Feature Size = 2343 bits (300 bytes).
Masking Masking

• Artifacts in iris images lead to erroneous code form• Caused by specular reflections, hard contact lens, eeyelashes, etc.• Non-iris regions masked to 0 graylevel in normalize

• Masked regions omitted during image equalization 
Product Product-of-Sum Distance Cla

Sum Distance Cla

( 

( )

1

1

1

1 2

1

1 2

N

M ij 

j

N

i

ij ij

j

Feature Feature

Dist

K Mask Mask

=

=

=

⎛ ⎜ 

⊕

= ⎜ 

⎜ ⎜ ⎝ 

∑ ∏ ∑ 

• Parameter Opt

minimizing Equa

The Product of Sum of Hamming Distances (HD) bbits gives a metric with good separation of MatchiMatching classes.

• Matching and NDistances modedistribution curv• Theoretical EEmeasuring areas overlapped regio
Proposed Metric Proposed Metric

A widely used metric for system performance - separation Hamming Distance of Matching and Average of Non-Ma

Proposal - Compare the separation between Normalized Matching with Nearest Non-Matching Irises.
Test Datasets Test Datasets

Dataset Number of Classes Enrol Images per class Tes

pe

CASIA 308 3 

Bath 150 3 
0 0.05 0.10 0.15 

0

0.005

0.01

0.015

0.02

0.025

N

Probability

Matching Irises

Nearest NonMatch

Gamma FitWeibull Fit

Results Results

Receiver Operating Characteristic Curves EER = 2.

Method Feature Extraction (ms) Matching (ms) T

Daugman 422 31 

Tan 125 68 

Monro 45 31 
Ongoing & Future Wor

Ongoing & Future Wor

• More Iris More Iris Image Collection Image Collection

• Alternative Iri

ative Iri

• Iris Quality Metrics Iris Quality Metrics• Novel Localization methods Novel Localization methods

• Fast Searching and Matching Fast Searching and Matching 

• Rotation Invariance Rotation Invariance

• Iris Variation S

Iris Variation 

• Liveness Detec

Liveness Detec

• Spoofing Coun

Spoofing Coun

• Effect of Medic

Effect of Medi
Acknowledgements Acknowledgements

Professor Tieniu Tan Professor Tieniu Tan

National Laboratory of Pattern Recognition 

National Laboratory of Pattern Recognition 

Chinese Academy of Sciences In Chinese Academy of Sciences Institute of Autom

stitute of Autom

Industrial Sponsors: Smart Sensors Ltd. 

Industrial Sponsors: Smart Sensors Ltd. 

Donald M Monro, Soumyadip Rakshit and 

Electronic and Electri

cal Engineering, University o

Bath BA2 7AY, United Kingdom

S.Rakshit@bath.ac.uk S.Rakshit@bath.ac.uk

Http://dms Http://dmsun4.bath.ac un4.bath.ac.uk

NIST FRGC ICE Workshop 23 March 2006
Questions ? Questions ?