Multispectral Biometrics System Framework: Application to Presentation Attack Detection

• URL: http://arxiv.org/abs/2006.07489v1
• Date: Fri, 12 Jun 2020 22:09:35 GMT
• Title: Multispectral Biometrics System Framework: Application to Presentation Attack Detection
• Authors: Leonidas Spinoulas, Mohamed Hussein, David Geissb\"uhler, Joe Mathai, Oswin G.Almeida, Guillaume Clivaz, S\'ebastien Marcel, and Wael AbdAlmageed
• Abstract summary: We present a framework for building a biometrics system capable of capturing multispectral data from a series of sensors synchronized with active illumination sources. The presented design is the first to employ such a diverse set of electromagnetic spectrum bands, ranging from visible to long-wave-infrared wavelengths.
• Score: 10.246136918682057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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