Multispectral Biometrics System Framework: Application to Presentation Attack Detection
By Leonidas Spinoulas, Mohamed E. Hussein, David Geissbühler, Joe Mathai, Oswin G. Almeida, Guillaume Clivaz, Sébastien Marcel, Wael AbdAlmageed. 2020
15 citations recorded by the source corpus.
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, which enabled us to successfully conduct a series of data collection events. We also present a comprehensive analysis on the captured data using a deep-learning classifier for presentation attack detection. Our analysis follows a data-centric approach attempting to highlight the strengths and weaknesses of each spectral band at distinguishing live from fake samples.
Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing
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Citations in the corpus
- Detection and Continual Learning of Novel Face Presentation Attacks (2021, methodology): “The PADISI-Face Dataset is collected using a sensor array designed and built by our team, shown in Figure 1 [10].”