![]() In this paper an innovative method for 1verification of signature using parametric features based on optimal threshold selection is proposed. These experiments have been conducted on twoįace image databases and their results will demonstrate theĮffectiveness of proposed quality-guided approach. gallery size should be optimised to meet the needs ofĮach target individual). Number of biometric templates depends mainly on the performance of each This approach is developed to selectĪdaptively a specific number of templates for each individual. This approach uses a quality-based clustering, followed by a specialĬriterion for the selection of an ultimate set of biometric templatesįrom the various clusters. This paper is devoted to elaborating on and discussingĪ new two stages approach for biometric templates selection and update. Storage requirements while improving the recognition accuracy of theīiometric system. Template(s) selection technique must aim to control the run time and Samples/templates and the choice of the most appropriate templates Template(s), labelled with the person's identity. Vectors and store them in the gallery in the form of biometric The same person and then extract their individual biometric feature Systems acquire multiple biometric samples, at the enrolment stage, for To deal with severe variation in recording conditions, most biometric These experiments have been conducted on five face image databases and their results will demonstrate the effectiveness of proposed quality guided approach. Our proposed technique is a two stage algorithm whereby in the first stage image samples are clustered in terms of their image quality profile vectors, rather than their biometric feature vectors, and in the second stage a per cluster template is selected from a small number of samples in each clusters to create an ultimate template sets. In this paper, a novel offline approach is proposed for systematic modelling of intra-class variability and typicality in biometric data by regularly selecting new templates from a set of available biometric images. The number and typicality of these templates are the most important factors that affect system performance than other factors. Many systems store multiple templates in order to account for such variations in the biometric data during enrolment stage. Such large fuzziness of biometric data can cause a big difference between an acquired and stored biometric data that will eventually lead to reduced performance. The high intra-class variability of acquired biometric data can be attributed to several factors such as quality of acquisition sensor (e.g. Experimental evaluation on real world data demonstrates the advantages of our approach. This approach allows the identification of different template groupings, taking into account the heart rate variability, and the removal of outliers due to noise artifacts. We study several methods to perform automatic template selection and account for variations observed in a person's biometric data. Our approach is based on clustering, grouping individual heartbeats based on their morphology. In this paper we propose a novel approach to ECG biometrics, with the purpose of reducing the computational complexity and increasing the robustness of the recognition process enabling the fusion of information across sessions. These new locations lead to ECG signals with lower signal to noise ratio and more prone to noise artifacts the heart rate variability is another of the major challenges of this biometric trait. Recent developments at the sensor level have shown the feasibility of performing signal acquisition at the fingers and hand palms, using one-lead sensor technology and dry electrodes. Electrocardiography (ECG) biometrics is emerging as a viable biometric trait.
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