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Preliminary Assessment of Discrimination of Twins in Photographs based on Facial Blemishes Nisha Srinivas 1, Matthew Pruitt 1, Gaurav Aggarwal 1, Patrick.

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Presentation on theme: "Preliminary Assessment of Discrimination of Twins in Photographs based on Facial Blemishes Nisha Srinivas 1, Matthew Pruitt 1, Gaurav Aggarwal 1, Patrick."— Presentation transcript:

1 Preliminary Assessment of Discrimination of Twins in Photographs based on Facial Blemishes Nisha Srinivas 1, Matthew Pruitt 1, Gaurav Aggarwal 1, Patrick Flynn 1, Richard Vorder Bruegge 2 1 University of Notre Dame 2 Frederal Bureau of Investigation, Digital Evidence Lab

2 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?

3 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 SkinScar (Round)PockmarkPimple

4 Proposed System Overview Manual Annotation Feature Extraction Geometric Normalization Point Cloud Matching Biometric Verification Performance Evaluation

5 Manual Annotation Display Module Annotation Module Tool Module

6 Facial Blemishes Identified by Observer 1

7 Facial Blemishes Identified by Observer 2

8 Facial Blemishes Identified by Observer 3

9 Facial Blemishes Identified by Observers Total Number of Facial Blemishes Annotated by each Observer Observer 1: 3785 Observer 2: 2311 Observer 3: 5100

10 Facial Blemishes Matching N NodesM Nodes Moles

11 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.

12 Matching Contd. Match Similarity metric=Number of matches/Max(N,M)

13 Data Twin face images were collected at the Twins Days Festival in Twinsburg, Ohio in August 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

14 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

15 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}

16 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

17 Individual Performance Evaluation- Observer 3

18 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:

19 Individual Performance Evaluation- Observer 3

20 Comparison: All vs All and Twins vs Twins

21 Inter-Observer Performance Degradation in Performance when comparing facial marks annotated by different observers

22 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

23 Thank You This research was supported by – NIJ/OJP award 2009-DN-BX-K231 – FBI through TSWG/ARMY RDECOM contract W91CRB-08-C-0093


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