A Novel Deep Learning Architecture With Image Diffusion for Robust Face Presentation Attack Detection

Madini O. Alassafi, Muhammad Sohail Ibrahim, Imran Naseem, Rayed Alghamdi, Reem Alotaibi, Faris A. Kateb, Hadi Mohsen Oqaibi, Abdulrahman A. Alshdadi, Syed Adnan Yusuf

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Face presentation attack detection (PAD) is considered to be an essential and critical step in modern face recognition systems. Face PAD aims at exposing an imposter or an unauthorized person seeking to deceive the authentication system. Presentation attacks are typically made using a fake ID through a digital/printed photograph, video, paper mask, 3D mask, and make-up etc. In this research, we propose a novel face PAD solution using an interpolation-based image diffusion augmented by transfer learning of a MobileNet convolutional neural network. The proposed interpolation-based image diffusion method and face PAD approach, implemented in a single framework, shows promising results on various anti-spoofing databases. The experimental results illustrate that the proposed face PAD method shows superior performance compared to most of the state-of-the-art methods.

Original languageEnglish
Pages (from-to)59204-59216
Number of pages13
JournalIEEE Access
Volume11
DOIs
Publication statusPublished - 13 Jun 2023

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