Iris as a Forensic 

Modality: 

The Path Forward

Arun Ross

Associate Professor

Michigan State University

rossarun@cse.msu.edu

http://www.cse.msu.edu/~rossarun
©Ross 2009

Pupillary Zone

Ciliary Zone

Collarette

Crypt

Contraction Furrows

Limbus boundary

Pupillary Boundary

Anatomy of the Iris

2
Automatic Iris Encoding

3
Ocular “Forensics”

• The eye and its immediate surroundings

• Consists of iris, sclera, eyelids, eyelashes, 

eyebrow, skin texture, etc.
Images from Smartphone

5
Color Iris
Variations in Iris Color

Brown Dark~Light 

Light/Brown Green 

Blue

Orig Image dimension: 1300 x 1040

Boyce et al, “Multispectral Iris Analysis: A Preliminary Study,” CVPR 2006
Dark-colored Iris

COLOR IMAGE NIR IMAGE

DARK IRIS LIGHT IRIS

8
 Visual Iris Matching: 

 Human interpretable features for iris matching

 Texture Analysis: 

 Gender and Ethnicity 

 Biological age

 Disease

 Post-mortem

 Degradation of iris after death 

 Image Forensics:

 Deducing sensor from image

 Deducing illumination source from image

Iris “Forensics”

9
 Human interpretable features for iris matching

 Utilizing anatomical features such as crypts, 

contraction furrows, collarette 

Manual Iris Matching

10
 Freckles

 Moles: Small pigmented clusters of uveal 

melanocytes

 Nevi: Dark pigmented lesions

 Iris Melanoma: Tumors causing distortion of pupil

 Blood Vessel spots

Using Macrofeatures

11
Macrofeatures: Example#1

12

nevus
Macrofeatures: Example#2

13

Pigmentation 

spots

Brushfield spots

Or 

Wolfflin nodules
Macrofeatures: Example#3

14

Sectoral 

Heterochromia
Matching Using Macro-features

• Each macro-feature is characterized by SIFT keypoints

• A hit-rate of 92.8% at rank 1 is observed for a database of 770 

color iris images where a subset of 380 images do not have 

any macro-features

15

Sam Sunder and Ross, “Iris Image Retrieval Based on Macro-features,” ICPR 2010
 Series of openings located on either side of the collarette 

that allow the stroma and deeper iris tissues to be 

bathed in aqueous humor 

 Thinning of the anterior stroma thereby exposing the 

heavily pigmented epithelium

Using Crypts

16

Crypts
 F. Shen, Ph.D., 2014: A Visually Interpretable Iris 

Recognition System with Crypt Features

 Extracts “crypts” and “blob-like” structures from 

normalized iris images 

Matching Using Crypts

17
 Tapia et al, “Gender Classification from Iris Images using 

Fusion of Uniform Local Binary Patterns”, ECCVW 2014

Gender from Iris 

18

• 750 males

• 750 females

• 80% training

• 20% testing

• Cross-validation
 The medical literature suggests that changes to 

the iris texture and structure are possible

 Two such changes:

 “Due to aging or trauma, atrophic areas may 

appear on the iris, resulting in a ‘moth-eaten’ 

texture”

 Iris melanomas: “The average age at 

diagnosis is 40-50 years; however, persons of 

any age can be affected”

 The impact of these specific changes on iris 

recognition is unknown

Biological Changes to the Iris

19
• Iris color changes with age in 10-15% Caucasians

• Pupil becomes myotic with age: excessive 

contraction of the pupil of the eye

• Under dim light, pupil of older people dilates less 

compared to pupil of younger people

• Rubeosis iridis is a medical condition of the iris in 

which new abnormal blood vessels are found on 

the surface of the iris

Changes in Pigmentation and Pupil

20
• Arcus senilis: An opaque arc or 

ring around the peripheral 

cornea - represents fatty or oily 

deposits in the cornea

• It is usually seen in elderly

people

• Arcus juvenilis is seen in people 

younger than 40 and often 

indicates high levels of 

cholesterol in the blood

Changes in Cornea

21

http://www.flickr.com/photos/ambistudies/4

147687397/#/
• Furrow degeneration or senile 

marginal degeneration

• Stromal fibrillar degeneration 

seen in rheumatoid arthritis

• Peripheral melting can occur

Changes in Furrows

22

http://www.flickr.com/photos/ambistudies/4147711777/in/p

hotostream/
 The pupillary margin became indistinguishable in certain eye 

images; which made identifying the boundary of the 

pupillary margin difficult

 In certain cases iris tissues were difficult to differentiate from 

adjacent scleral tissues, making limbal boundary indistinct 

 Iris images in the postmortem scenario were observed to 

develop corneal opacity

Post-Mortem Iris Recognition

23

Pupil fadeout Limbic boundary diffusion Corneal Opacity

Derakhshani et al, “Post Mortem Ocular Biometric Analysis,” CITeR Project, 2011 
Determining Sensors from Images

24

 Classification accuracy is ~90%

Kalka, Bartlow, Cukic, Ross, “Identifying Sensors from Iris Images,” Manuscript under preparation
Determining Data Source

25
Which Dataset is this Image From?

26

 Classification accuracy ranged from 70% to 82%

El Naggar, Ross, “Which Dataset is this Iris Image From?” (Manuscript Under Preparation)
 Iris forensics is an emerging area of research in 

academia

 Potential to exploit iris as a forensic modality:

 Iris matching in a court of law

 Resolving identity using “in-the-wild” images

 Deducing ancillary information from iris 

texture 

 Determining imaging source of iris data

 Genetics of human iris 

Summary

27
Iris as a Forensic 

Modality: 

The Path Forward

Arun Ross

Associate Professor

Michigan State University

rossarun@cse.msu.edu

http://www.cse.msu.edu/~rossarun