An Evaluation of Score Level Fusion Approaches for Fingerprint and Finger-vein Biometrics

Biometric systems have to address many requirements, such as large population coverage, demographic diversity, varied deployment environment, as well as practical aspects like performance and spoofing attacks. Traditional unimodal biometric systems do not fully meet the aforementioned requirements making them vulnerable and susceptible to different types of attacks. In response to that, modern biometric systems combine multiple biometric modalities at different fusion levels. The fused score is decisive to classify an unknown user as a genuine or impostor. In this paper, we evaluate combinations of score normalization and fusion techniques using two modalities (fingerprint and finger-vein) with the goal of identifying which one achieves better improvement rate over traditional unimodal biometric systems. The individual scores obtained from finger-veins and fingerprints are combined at score level using three score normalization techniques (min-max, z-score, hyperbolic tangent) and four score fusion approaches (minimum score, maximum score, simple sum, user weighting). The experimental results proved that the combination of hyperbolic tangent score normalization technique with the simple sum fusion approach achieve the best improvement rate of 99.98%.Comment: 10 pages, 5 figures, 3 tables, conference, NISK 201

Multimodal Biometric Authentication System: Challenges and Solutions

Biometric technologies are automated methods for measuring and analyzing biological data extracting a feature set from acquired data and comparing this set against to the templates set in the database Unimodal biometric system have variety of problems such as noisy data spool attacks etc Multimodal biometrics refers the combination of two or more biometric modalities in a single identification Most biometric verification systems are done based on knowledge base and token based identification these are prone to fraud Biometric authentication employs unique combinations ofmeasurable physical characteristics- fingerprint facial features iris of the eye voice print and so on- that cannot be readily imitated or forged by others This paper discuss the various scenarios that are possible in multi model biometric system the level of fusion that are plausible and the integration strategic that can be adopted to consolidate information Fusion methods include processing biometric madalitics sequential until an acceptable match is obtaine

Towards Data-Driven Autonomics in Data Centers

Continued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using predictive computational and executable models obtained through data-science tools, and at that point, the intervention of humans will be limited to setting high-level goals and policies rather than performing low-level operations. Data-driven autonomics, where management and control are based on holistic predictive models that are built and updated using generated data, opens one possible path towards limiting the role of operators in data centers. In this paper, we present a data-science study of a public Google dataset collected in a 12K-node cluster with the goal of building and evaluating a predictive model for node failures. We use BigQuery, the big data SQL platform from the Google Cloud suite, to process massive amounts of data and generate a rich feature set characterizing machine state over time. We describe how an ensemble classifier can be built out of many Random Forest classifiers each trained on these features, to predict if machines will fail in a future 24-hour window. Our evaluation reveals that if we limit false positive rates to 5%, we can achieve true positive rates between 27% and 88% with precision varying between 50% and 72%. We discuss the practicality of including our predictive model as the central component of a data-driven autonomic manager and operating it on-line with live data streams (rather than off-line on data logs). All of the scripts used for BigQuery and classification analyses are publicly available from the authors' website.Comment: 12 pages, 6 figure

Towards Operator-less Data Centers Through Data-Driven, Predictive, Proactive Autonomics

Continued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using predictive computational and executable models obtained through data-science tools, and at that point, the intervention of humans will be limited to setting high-level goals and policies rather than performing low-level operations. Data-driven autonomics, where management and control are based on holistic predictive models that are built and updated using live data, opens one possible path towards limiting the role of operators in data centers. In this paper, we present a data-science study of a public Google dataset collected in a 12K-node cluster with the goal of building and evaluating predictive models for node failures. Our results support the practicality of a data-driven approach by showing the effectiveness of predictive models based on data found in typical data center logs. We use BigQuery, the big data SQL platform from the Google Cloud suite, to process massive amounts of data and generate a rich feature set characterizing node state over time. We describe how an ensemble classifier can be built out of many Random Forest classifiers each trained on these features, to predict if nodes will fail in a future 24-hour window. Our evaluation reveals that if we limit false positive rates to 5%, we can achieve true positive rates between 27% and 88% with precision varying between 50% and 72%.This level of performance allows us to recover large fraction of jobs' executions (by redirecting them to other nodes when a failure of the present node is predicted) that would otherwise have been wasted due to failures. [...

Prediction accuracy measurements for ensemble classifier

Multiple classifier combination (or ensemble method) has been shown to be very helpful in improving the performance of classification over single classifier approach. The diversity among base classifiers (or ensemble members) is important when constructing a classifier ensemble.Although there have been several measures of diversity, but there is no reliable measure that can predict the ensemble accuracy. The base classifiers accuracy will increase when the diversity decreases and this is known as the accuracy-diversity dilemma.This paper presents a new method to measure diversity in classifier ensembles.Furthermore another parameter which based on this diversity measure is defined.It is hope that the new parameter will be able to predict the ensemble accuracy.Based on experimental results on classification of 84 samples of fruit images using nearest mean classifier ensembles, it has been shown that there is a positive linear relationship between the new parameter and the ensemble accuracy.This parameter is expected to assist in constructing diverse and accurate ensemble

Accuracy of classifier combining based on majority voting

In this paper, we formulate the accuracy of classifier combining which is based on majority voting, there are only two parameter involved, one is the average accuracy of individual classifiers, the other we call it Lapsed Accuracy (LA) is related with the efficiency of classifier combining, and we discuss the theoretical bounds of majority voting via the formula