Friday , April 19 2019

Anti-Spoofing Techniques in Face Recognition, an Ensemble Based Approach

Răzvan-Daniel ALBU1, Cornelia Emilia GORDAN1, Ioan DZIȚAC2,3*
University of Oradea, Faculty of Electrical Engineering and Information Technology,
Department of Electronics and Telecommunications, 1 Universității, 410087, Oradea, Romania
ralbu@uoradea.ro
Aurel Vlaicu University of Arad, Faculty of Exact Sciences,
Department of Mathematics and Informatics, 2 Elena Drăgoi Street, 310330 Arad, Romania
ioan.dzitac@uav.ro (*Corresponding author)
Agora University of Oradea, Faculty of Economics, Department of Economics,
8 Piața Tineretului, 410526 Oradea, Romania
idzitac@univagora.ro

ABSTRACT: In this article we describe the implementation of a reliable and innovative ensemble-based technique that can prevent face spoofing attacks. The presented software is part of a technology developed in partnership with IsItYou, an Israeli company, that attempts to replace passwords with a face-based authentication system. Since the main problem of biometric systems is represented by the spoof attacks, IsItYou came with a solution to this, developing a unique technology that can identify spoof attacks, and authenticate only authorized humans. Inspired from deep learning techniques where ensemble-based solutions improve machine learning results by uniting several models, a software ensemble that combines multiple anti-spoofing methods, covering a larger range of spoof attacks and increasing overall security was developed. The article also shows the performances results and implementation details. The experimental results signpost our solution can provide first-rate results compared to the state-of-the-art approaches.

KEYWORDS: Face anti-spoofing, Face recognition, Beware, Biometric security, Ensemble system.

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CITE THIS PAPER AS:
Răzvan-Daniel ALBU, Cornelia Emilia GORDAN, Ioan DZIȚAC, Anti-Spoofing Techniques in Face Recognition, an Ensemble Based Approach, Studies in Informatics and Control, ISSN 1220-1766, vol. 28(1), pp. 111-118, 2019. https://doi.org/10.24846/v28i1y201912