论文标题
多光谱生物识别系统框架：应用于演示攻击检测
Multispectral Biometrics System Framework: Application to Presentation Attack Detection
论文作者
论文摘要
在这项工作中，我们提出了一个通用框架，用于构建能够从一系列与主动照明源同步的传感器中捕获多光谱数据的生物识别系统。该框架统一了针对不同生物识别方式的系统设计，其面部，手指和虹膜数据的实现将详细描述。据我们所知，提出的设计是第一个采用如此多样化的电磁频谱频段，从可见到长波 - 内红外波长不等，并且能够在几秒钟内获取大量数据。在进行了一系列数据收集之后，我们使用深度学习分类器进行演示攻击检测对捕获的数据进行了全面分析。我们的研究遵循一种以数据为中心的方法，试图突出显示现场与假样品时每个光谱频段的优势和劣势。
In this work, we present a general framework for building a biometrics system capable of capturing multispectral data from a series of sensors synchronized with active illumination sources. The framework unifies the system design for different biometric modalities and its realization on face, finger and iris data is described in detail. To the best of our knowledge, the presented design is the first to employ such a diverse set of electromagnetic spectrum bands, ranging from visible to long-wave-infrared wavelengths, and is capable of acquiring large volumes of data in seconds. Having performed a series of data collections, we run a comprehensive analysis on the captured data using a deep-learning classifier for presentation attack detection. Our study follows a data-centric approach attempting to highlight the strengths and weaknesses of each spectral band at distinguishing live from fake samples.
