Poster Abstract: A Novel and Efficient Approach to Evaluate Biometric Features for User Identification

Namrata Kayastha, Kewei Sha
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引用次数: 2

Abstract

Classifications based on biometric features are widely used in modern healthcare applications, including user identification, authentication, and tracking. The complexity and accuracy of classification algorithms largely depend on the size and the quality of the feature set used to build classifiers. Feature evaluation and selection are critical steps to decide a small set of high-quality features to build accurate and efficient classifiers. This paper proposes a novel and efficient approach to evaluate and select biometric features for user identification applications based on activity sensor data collected from the user's wrists. For each feature, we first generate an NRMSD matrix, each entry of which represents the similarity level of any two users. Then, we define a heuristic, the Farness value to evaluate the quality of the feature based on the NRMSD matrix of the feature. Finally, we select a set of high-quality features whose Farness value is higher than 0.10.
摘要:一种新的、有效的用于用户识别的生物特征评估方法
基于生物特征的分类广泛应用于现代医疗保健应用,包括用户识别、身份验证和跟踪。分类算法的复杂性和准确性很大程度上取决于用于构建分类器的特征集的大小和质量。特征评估和选择是确定一小部分高质量特征以构建准确高效分类器的关键步骤。本文提出了一种基于从用户手腕收集的活动传感器数据来评估和选择用户识别应用的生物特征的新颖有效方法。对于每个特征,我们首先生成一个NRMSD矩阵,其中的每个条目表示任意两个用户的相似度。然后,基于特征的NRMSD矩阵,我们定义了一个启发式的Farness值来评估特征的质量。最后,我们选择一组Farness值大于0.10的高质量特征。
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