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Preliminary Assessment of Discrimination of Twins in
Photographs based on Facial Blemishes
Nisha Srinivas1, Matthew Pruitt1, Gaurav Aggarwal1, Patrick Flynn1, Richard Vorder Bruegge2
1University of Notre Dame2Frederal Bureau of Investigation, Digital Evidence Lab
Problem Statement
• Investigate the usefulness of facial blemishes to distinguish between identical twins– Moles, Freckles, Scars, etc
• Determine– Whether facial blemishes and locations can be
used to distinguish between identical twins– Whether the distributions of facial blemishes are
“more similar” for identical twins than unrelated persons?
Facial Blemishes
• Types of facial blemishes – Mole– Freckle– Freckle Group– Pimple– Darkened Patch– Lightened Patch– Splotchiness– Birthmark– Raised Skin– Pockmark– Scars
• Linear• Round
Mole Freckle and Freckle Group
Lightened Patch
Darkened Patch
Raised Skin Scar (Round) Pockmark Pimple
Proposed System Overview
Manual Annotation
Feature Extraction
Geometric Normalization
Point Cloud Matching
Biometric Verification
Performance Evaluation
Manual Annotation
Display Module
Annotation Module
Tool Module
Facial Blemishes Identified
by Observer 1
Facial Blemishes Identified
by Observer 2
Facial Blemishes Identified
by Observer 3
Facial Blemishes Identified by Observers
Moles
Freck
les
Freck
le Gro
up
Pimple
Birthmark
Darkened Patch
Lighte
ned Patch
Splotchiness
Raised Skin
Pockmark
Scar R
ound
Scar L
inear0
500
1000
1500
2000
2500
Observer 1Observer 2Observer 3
Types of Facial Blemishes
Coun
t
Total Number of Facial Blemishes Annotated by each ObserverObserver 1: 3785Observer 2: 2311Observer 3: 5100
Facial Blemishes Matching
N Nodes M Nodes
Moles
Matching Contd.
• The Edges in the bipartite graph correspond to potential matches• Each potential match has a cost associated with it which is a function of
the euclidean distance between the centroids of the blemishes being compared.
Matching Contd.
Match
Match
Similarity metric=Number of matches/Max(N,M)
Data
• Twin face images were collected at the Twins Days Festival in Twinsburg, Ohio in August 2009.
• High Resolution Images: 4310 rows x 2868 columns• Dataset Attributes
– Frontal (yaw=0), Indoor, No Glasses, Neutral Expression
• Number of Images: 295– Number of Subjects: 152– Number of Twins Pairs: 76
• Terminology– Target set: “gallery” of persons to be recognized– Query set: a set of images of unidentified persons to be
matched against the target set
Experimental Setup
• Perform two different experiments– Individual Observer Analysis
• Query set and Target set are annotated by same observer
– Inter-Observer Analysis• Query set is annotated by one observer and the Target
set is annotated by another observer– Observer 1 vs Observer 2– Observer 2 vs Observer 3– Observer 3 vs Observer 1
Subset of Facial blemishes
• FM={moles, freckles, freckle group, pimple, birthmark, darkened patch, lightened patch, splotchiness, raised skin, pockmark, scar round, scar linear}
• FM1=FM-{pimple}• FM2={moles, freckles}• FM3={moles, freckles, pimple}
Twins vs Twins Setup: Query Set Target Set
Subject 1, Twin A
Subject 2, Twin B
Subject 3, Twin A
Subject 4, Twin B
Match Comparison
Non-Match Comparison
Individual Performance Evaluation- Observer 3
Match Comparison
Non-Match Comparison
Query Set Target Set
Subject 1, Twin A
Subject 2, Twin B
Subject 3, Twin A
Subject 4, Twin B
Subject 5, Twin A
All vs All Setup:
Individual Performance Evaluation- Observer 3
Comparison: All vs All and Twins vs Twins
Inter-Observer Performance
Degradation in Performance when comparing facial marks annotated by different observers
Conclusion
• There appears a correlation between the distribution of facial blemishes across twins.
• The number of facial blemishes across twins appears to be similar.
• Facial blemishes can be used as a potential biometric signature.
• Consistent annotation is a challenging process – It is difficult to achieve consistency
Thank You
This research was supported by– NIJ/OJP award 2009-DN-BX-K231– FBI through TSWG/ARMY RDECOM contract
W91CRB-08-C-0093