Acquisition

Modeling an Anonymous Identification 

System

Brian DeCann and Arun Ross

March 8th, 2012

Integrated Pattern Recognition and Biometrics (i-PRoBe) Lab

Lane Department of Computer Science and Electrical Engineering

West Virginia University, Morgantown, West Virginia
Motivation Acquisition

Suppose a biometric system encounters some number of individuals*.

*Face images sampled from from MBGC Dataset 

Encountered Individual

• Presume no prior information

about any of these individuals / 

identities.

• The actual identity of the 

individual observed is unknown

to the system (identification).

• The system is also not acquiring

information pertaining to the 

identity of encountered 

individuals. 
Motivation Acquisition

Suppose all we wish to know is has this person been encountered

previously by the system.

*Face images sampled from from MBGC Dataset 

Encountered Individual Has this person

been encountered?
Motivation Acquisition

Suppose all we wish to know is has this person been encountered

previously by the system.

*Face images sampled from from MBGC Dataset 

Encountered Individual Has this person

been encountered?

Yes, this person has been previously 

encountered.
Anonymous Identification Acquisition

• Anonymous identification system

− Variant of a classical biometric system.

• No explicit enrollment process.

• Biometric templates in the gallery are not labeled with the identity of 

individuals.

• Comparison process

− System observes the input (probe) biometric data and 

determines if a match exists in the gallery.

− Addresses the question: “Has this person been encountered 

before?”
Visual Comparison Acquisition

Traditional

Biometric System

Anonymous

Identification System
Visual Example: Match Outcome Acquisition

*Face images sampled from from MBGC Dataset 
Visual Example: Match Outcome Acquisition

*Face images sampled from from MBGC Dataset 
Visual Example: Non-match Outcome Acquisition

*Face images sampled from from MBGC Dataset 
Visual Example: Non-match Outcome Acquisition

*Face images sampled from from MBGC Dataset 
Potential Applications Acquisition

• De-duplication

− The de-duplication problem invokes searching through a 

database to solely determine if the probe matches to an entry in 

the gallery.

• i.e., not concerned with the identity of the match.

− De-duplication contrasts to classical identification, since gallery 

entries may not be accurately labeled.

− Problem gaining traction in the context of national scale ID 

programs*.

• Surveillance

− Allows for real-time updating of a gallery.

• Identity profiles can be newly created or updated following each encounter.

− Covert operation. 

• Subjects do not need to be enrolled in order to be later recognized.

*UIDAI, “Role of Biometric Technology in Aadhaar Enrollment” Government of India, Tech. Rep. January 2012.
Error Analysis Acquisition

• Traditional system

− Error is computed using a fixed set of gallery and probe images.

• Anonymous system

− Since the gallery expands, the error rate changes depending on 

the current gallery and future probes.

• How?

− In a traditional analysis, probe pk is absolutely associated with a 

specific set of gallery entries.

− In an anonymous identification system, the actual identity 

pertaining to probe pk may or may not have been previously 

encountered.

− Further, if the proper identity has been encountered, it may exist 

in multiple identity clusters due to decision error.
Types of Errors Acquisition

• False Dynamic Match (FDM)

− Occurs when a probe incorrectly matches to an identity cluster

that does not contain an entry belonging to the encountered 

individual.

• False Dynamic Non-Match (FDNM):

− Occurs when a probe, which should match to at least one 

existing identity cluster, does not match to any identity cluster.

• Thus, if the individual has not been seen by the system, a false 

dynamic non-match cannot occur.

• A false dynamic non-match does not occur when a probe correctly 

matches to an identity cluster consisting of the true identity, in 

addition to other identities.
False Dynamic Match: Visualization Acquisition
Acquisition False Dynamic Non-Match: Visualization
Error Prediction Acquisition

• Analytical Error Prediction

− Derive an analytical approach for estimation of FDMR 

(False dynamic match rate) and FDNMR (False 

dynamic non-match rate) given a set of test data. 

• Constraints

– Assume all probes have an equal probability of being 

observed.
Acquisition Error Prediction – False Dynamic Match

• False Dynamic Match

− Occurs when a probe is incorrectly matched to an identity cluster 

who entries do not contain the true identity of the probe.

• Events

− Event A: When pk is matched against G at encounter ek, there 

are no genuine scores generated and at least one impostor 

score is greater than γ.

• γ = decision threshold

− Event B: When pk is matched against G at encounter ek, both 

genuine and impostor scores are generated, and there is at least 

one impostor score that (a) exceeds γ and (b) is greater than all 

genuine scores.
FDM - Mathematical Representation Acquisition

*Inclusive to entities outside the gallery.
Acquisition Error Prediction – False Dynamic Non-match

• False Dynamic Non-match

− A probe does not match to a genuine gallery entry 

and any impostor probes that could procure a match 

have not been observed.

• Events

− Event C: When pk is matched against G at encounter 

ek, all genuine scores generated are below γ.

− Event D: When pk is matched against G at encounter 

ek, all impostor scores generated are below γ.
FDNM - Mathematical Representation Acquisition

*Inclusive to entities outside the gallery.
Expected Error Rates Acquisition

*Expected FDMR and FDNMR over K encounters
Experiments Acquisition

• Datasets

− WVU face dataset

• 5 frontal face images for 240 subjects.

• Similarity scores computed from VeriFace.

− WVU fingerprint dataset

• 5 fingerprint images corresponding to the R1, R2, L1 and L2 

fingers for 240 subjects.

• Similarity scores computed from VeriFinger.

− CASIA Iris Version 3 dataset

• Subset using 5 left iris images for 122 subjects.

• Similarity scores computed using an open source IrisCode 

algorithm.
Experiments Acquisition

• Datasets

− WVU face dataset

• 5 frontal face images for 240 subjects.

• Similarity scores computed from VeriFace.

− WVU fingerprint dataset

• 5 fingerprint images corresponding to the R1, R2, L1 and L2 

fingers for 240 subjects.

• Similarity scores computed from VeriFinger.

− CASIA Iris Version 3 dataset

• Subset using 5 left iris images for 122 subjects.

• Similarity scores computed using an open source IrisCode 

algorithm. Note: We are interested in the meaning of 

the numbers, rather than the value(s).
DET Curves for Match Score Sets Acquisition

DET curves for face, fingerprint, and iris scores.
Evaluating the Prediction Model Acquisition

• Model Prediction 

− Evaluate the ability of the model to accurately predict error rates 

given a set of test scores.

• Experimental setup

− Create a bootstrapped test set of 300 probes

• Each bootstrapped test set contains 5 genuine probes for 60 

identities.

• Allows for variation in the test data for evaluating the model 

performance.

• Aids in mitigating numerical errors from computing very large 

combinatorics.

– Compare theoretical and observed FDMR and FDNMR.
Evaluating the Prediction Model Acquisition

Face Scores Fingerprint (R1) Scores
Evaluating the Prediction Model Acquisition

Iris Scores
Summary Acquisition
Summary Acquisition
Acquisition

Thank You!

Acknowledgments

Office of Naval Research

WVU Night Biometrics Team
Literature Review Acquisition

• Anonymous Identification in Literature

− Dodis et al. first defined an anonymous identification system 

where users enroll in ad-hoc groups and prove membership [1].

− Bringer et al. adopted the term anonymous identification in the 

context of cancelable biometrics [2].

− Phrase has come to refer to template protection or quality 

assurances for privacy.

• Present Form

− Here, this definition is not concerned with template protection.

− Loosely resembles the work of Dodis et al. [1], as the matching

process does not necessarily deduce identity.

[1] Y. Dodis, A. Kiayias, A. Nicolosi, V. Shoup, “Anonymous Identification in ad-hoc groups,” Advances in Cryptology – Eurocrypt (2002)

[2] J. Bringer, H. Chabanne, B. Kindarji, “Anonymous identification with cancelable biometrics,” International Symposium on Image and Signal 

Processing and Analysis (2009).
Experiments Acquisition

• Performance as a function of permutation

− Demonstrate that the order in which probes are encountered 

affects the observed error.

• Experimental setup

− Define three types of permutations

• Random Draw

− Draw K probes at random without replacement.

• Increment Probes (IP)

− Probes corresponding to a single unique identity repeat every M encounters. (M = 

Number of distinct identities in test set)

• Increment Subjects (IS)

− Probes corresponding to a single unique identity repeat successively.

− Record observed error rates of each permutation type at many 

values of γ.

• Also note the FMR, FNMR, FPIR, and FNIR of each score set for all values 

of γ. (γ = decision threshold).
Algorithmic Representation Acquisition
Experiments Acquisition
Experiments Acquisition

False dynamic match and false dynamic non-match rates for face scores.
Experiments Acquisition

False dynamic match and false dynamic non-match rates for face scores.
Experiments Acquisition

False dynamic match and false dynamic non-match rates for iris scores.
Summary Acquisition

• Traditional vs. Anonymous

− Shape of performance curves similar.

− Intersection of FDMR and FDNMR approximately equal to stated 

EER

− Suggests performance is comparable to the state of art.

− FMR and FNMR are poor predictors of FDMR and FDNMR, 

respectively.

• Performance as function of encounter

− Figures demonstrate the probability of observing a false dynamic 

match can be significantly impacted by probe order.

• Evidenced in permutation scatter

• Permutation IS – Highest Error

• Permutation IP – Lowest Error

• Error Prediction

− Accurately predicted observed error rates within +/- 1.5%