In a preregistered, cross-sectional study we investigated whether olfactory loss is a reliable predictor of COVID-19 using a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified univariate and multivariate predictors of COVID-19 status and post-COVID-19 olfactory recovery. Both C19+ and C19- groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both univariate and multivariate models (ROC AUC=0.72). Additional variables provide negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms (e.g., fever). Olfactory recovery within 40 days of respiratory symptom onset was reported for ~50% of participants and was best predicted by time since respiratory symptom onset. We find that quantified smell loss is the best predictor of COVID-19 amongst those with symptoms of respiratory illness. To aid clinicians and contact tracers in identifying individuals with a high likelihood of having COVID-19, we propose a novel 0-10 scale to screen for recent olfactory loss, the ODoR-19. We find that numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4<OR<10). Once independently validated, this tool could be deployed when viral lab tests are impractical or unavailable.
Background: COVID-19 has heterogeneous manifestations, though one of the most common symptoms is a sudden loss of smell (anosmia or hyposmia). We investigated whether olfactory loss is a reliable predictor of COVID-19. Methods: This preregistered, cross-sectional study used a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified singular and cumulative predictors of COVID-19 status and post-COVID-19 olfactory recovery. Results: Both C19+ and C19- groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both single and cumulative feature models (ROC AUC=0.72), with additional features providing no significant model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms, such as fever or cough. Olfactory recovery within 40 days was reported for ~50% of participants and was best predicted by time since illness onset. Conclusions: As smell loss is the best predictor of COVID-19, we developed the ODoR-19 tool, a 0-10 scale to screen for recent olfactory loss. Numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (10<OR<4), especially when viral lab tests are impractical or unavailable.
Multi-biometric system stores multiple templates for the same user corresponding to the different biometric sources. Infallible security should be provided to the stored biometric templates as biometric is not revocable. In this work, multi-modal biometric template security for palmprint and fingerprint is proposed which is based on the fuzzy vault generation. At first, the preprocessing steps are applied and subsequently, the features are extracted and combined. For recognition, we match the feature vectors of images. The multi-modal biometric template along with the input key are used to generate the fuzzy vault. In the decoding process, the template is given as input and is combined with the stored fuzzy vault to generate the corresponding final key. The experimentation is carried out using CASIA database for palmprint and FVC 2004 database for fingerprint. The evaluation metrics have FMR and FNMR value parameters. Journa l o f B io m etrics & B io s ta tistics
Biometric system is vulnerable to a variety of attacks aimed at undermining the integrity of the authentication process. More importantly template security is of vital importance in the biometric systems because unlike passwords, stolen biometric templates cannot be revoked. In this paper we describe the various threats that can be encountered by a biometric system. We specifically focus on attacks designed to elicit information about the original biometric data of an individual from the stored template. A few algorithms presented in the literature are discussed in this regard. We also examine techniques that can be used to deter or detect these attacks. Furthermore, we provide experimental results pertaining to a biometric system combining biometrics with cryptography, that converts dorsal hand vein templates into novel cryptographic structure called fuzzy vault. Initially, the pre-processing steps are applied to dorsal hand vein images for enhancement, smoothing and compression. Subsequently, thinning and binary encoding techniques are employed and then feature extracted. Then the biometric template and the input key are used to generate the fuzzy vault. For decoding, biometric template from dorsal hand vein image is constructed and it is combined with the stored fuzzy vault to generate the final key. The experimentation was conducted using dorsal hand vein databases and the FNMR and FMR values are calculated with and without noise.Citation: Brindha VE (2012) Biometric Template Security using Dorsal Hand Vein Fuzzy Vault. J Biomet Biostat 3:145.
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