RSFS: A soft biometrics-based relative support features set for person verification

Bilal Hassan, E. Izquierdo
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引用次数: 3

Abstract

Generally, biometrics is gaining increased attention due to its application for secure and efficient verification – more specifically at border crossing points. Usually, there are many different types of biometrics associated with human body i.e., intrusive like finger prints etc. and non-intrusive, termed as soft biometrics. In order to make the concept of Smart Borders a reality, the non-intrusive soft biometrics are the baseline technology. One of biggest challenge in soft biometrics based verification is to find a highly related set of features from different modalities of human body – as there is large number such soft biometrics associated with human body. In fact, this is extremely useful to select only those soft biometrics which are supportive to each other and relevant to the problem domain. In our work, we thoroughly investigated one of the largest collection of soft biometrics and developed a multiple non-linear regression based framework for the selection of highly supportive and relevant soft biometrics. We used one of the largest dataset e.g., PETA and its annotation for the evaluation of our proposed model. The accuracy is reported in form of MAE and error distribution graphs for two global soft biometrics i.e., gender and age prediction.
RSFS:用于人员验证的基于软生物识别的相对支持特性集
一般来说,生物识别技术由于其在安全和有效核查方面的应用而受到越来越多的关注,特别是在过境点。通常,有许多不同类型的生物识别技术与人体有关,即侵入性的,如指纹等和非侵入性的,称为软生物识别技术。为了使智能边界的概念成为现实,非侵入性软生物识别技术是基础技术。在基于软生物识别技术的验证中,最大的挑战之一是如何从不同的人体形态中找到一组高度相关的特征,因为与人体相关的软生物识别特征非常多。事实上,只选择那些相互支持且与问题领域相关的软生物特征是非常有用的。在我们的工作中,我们深入研究了最大的软生物特征集合之一,并开发了一个基于多元非线性回归的框架,用于选择高度支持和相关的软生物特征。我们使用了最大的数据集之一,例如PETA及其注释来评估我们提出的模型。准确性以MAE和误差分布图的形式报告了两种全球软生物特征,即性别和年龄预测。
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