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Analyzing personalized policies for online biometric verification.

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 [Formula: see text] 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.

SUBMITTER: Sadhwani A

PROVIDER: S-EPMC4006790 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Analyzing personalized policies for online biometric verification.

Sadhwani Apaar A Yang Yan Y Wein Lawrence M LM

PloS one 20140501 5

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 similarit ...[more]

PMID: 24787752

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Project description:Due to the health and economic costs of childhood obesity, coupled with studies suggesting the benefits of comprehensive (dietary, physical activity and behavioral counseling) intervention, the United States Preventive Services Task Force recently recommended childhood screening and intervention for obesity beginning at age six. Using a longitudinal data set consisting of the body mass index of 3164 children up to age 18 and another longitudinal data set containing the body mass index at ages 18 and 40 and the presence or absence of disease (hypertension and diabetes) at age 40 for 747 people, we formulate and numerically solve - separately for boys and girls - a dynamic programming problem for the optimal biennial (i.e., at ages 2, 4, …, 16) obesity screening thresholds. Unlike most screening problem formulations, we take a societal viewpoint, where the state of the system at each age is the population-wide probability density function of the body mass index. Compared to the biennial version of the task force's recommendation, the screening thresholds derived from the dynamic program achieve a relative reduction in disease prevalence of 3% at the same screening (and treatment) cost, or - due to the flatness of the disease vs. screening tradeoff curve - achieves the same disease prevalence at a 28% relative reduction in cost. Compared to the task force's policy, which uses the 95th percentile of body mass index (from cross-sectional growth charts tabulated by the Centers for Disease Control and Prevention) as the screening threshold for each age, the dynamic programming policy treats mostly 16 year olds (including many who are not obese) and very few males under 14 years old. While our results suggest that adult hypertension and diabetes are minimized by focusing childhood obesity screening and treatment on older adolescents, the shortcomings in the available data and the narrowness of the medical outcomes considered prevent us from making a recommendation about childhood obesity screening policies.

Dataset Information

Analyzing personalized policies for online biometric verification.

Publications

Analyzing personalized policies for online biometric verification.

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