Abstract
Motivated by India’s nationwide biometric program for social inclusion, we analyze verification (i.e., one-to-one matching) in the case where we possess similarity scores for 10 fingerprints and two irises between a resident’s biometric images at enrollment and his biometric images during his first verification. At subsequent verifications, we allow individualized strategies based on these 12 scores: we acquire a subset of the 12 images, get new scores for this subset that quantify the similarity to the corresponding enrollment images, and use the likelihood ratio (i.e., the likelihood of observing these scores if the resident is genuine divided by the corresponding likelihood if the resident is an imposter) to decide whether a resident is genuine or an imposter. We also consider two-stage policies, where additional images are acquired in a second stage if the first-stage results are inconclusive. Using performance data from India’s program, we develop a new probabilistic model for the joint distribution of the 12 similarity scores and find near-optimal individualized strategies that minimize the false reject rate (FRR) subject to constraints on the false accept rate (FAR) and mean verification delay for each resident. Our individualized policies achieve the same FRR as a policy that acquires (and optimally fuses) 12 biometrics for each resident, which represents a five (four, respectively) log reduction in FRR relative to fingerprint (iris, respectively) policies previously proposed for India’s biometric program. The mean delay is sec for our proposed policy, compared to 30 sec for a policy that acquires one fingerprint and 107 sec for a policy that acquires all 12 biometrics. This policy acquires iris scans from 32–41% of residents (depending on the FAR) and acquires an average of 1.3 fingerprints per resident.
Highlights
In India, one of the biggest barriers for poor people to access government services is the inability to prove one’s identity [1]
We develop a probabilistic model for each resident’s 12 genuine similarity scores obtained during the Best Finger Detection (BFD) and Best Iris Detection (BID) processes, and each resident’s similarity scores during subsequent verifications
In the single-stage finger policy, the false reject rate (FRR), which is measured on a log scale in Fig. 2 due to the wide range of outcomes, falls by 1.5–1.7 logs when the delay is increased from 30 to 40 sec, where the reduction decreases with smaller false accept rate (FAR) values
Summary
In India, one of the biggest barriers for poor people to access government services is the inability to prove one’s identity [1]. To improve social inclusion [2], the government of India has undertaken the largest biometric program in human history, called the Unique Identification Authority of India (UIDAI), with the aim of creating a unique biometric identity for each of its 1.2 B residents [1]; other countries, such as Indonesia, are developing similar programs [3]. This program requires two main biometric matching activities. UIDAI predicts that it will perform up to 106 verifications/hr after the system is operational, and that most of these verifications will be online, i.e., performed while the resident waits
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