Sound-Print: Generalised Face Presentation Attack Detection using Deep Representation of Sound Echoes

TL;DR

This paper introduces a novel acoustic echo-based method for face presentation attack detection on smartphones, leveraging reflection profiles of transmitted sound signals to distinguish genuine faces from various attack types, including unknown PAs.

Contribution

The paper proposes a new sound echo-based PAD technique using a wide pulse transmission to improve SNR and accurately model reflection profiles, enabling detection of unknown presentation attacks.

Findings

Robust detection of various PAs including unknown types.

High accuracy demonstrated on the new Acoustic Sound Echo Dataset.

Effective noise removal enhances reflection profile analysis.

Abstract

Facial biometrics are widely deployed in smartphone-based applications because of their usability and increased verification accuracy in unconstrained scenarios. The evolving applications of smartphone-based facial recognition have also increased Presentation Attacks (PAs), where an attacker can present a Presentation Attack Instrument (PAI) to maliciously gain access to the application. Because the materials used to generate PAI are not deterministic, the detection of unknown presentation attacks is challenging. In this paper, we present an acoustic echo-based face Presentation Attack Detection (PAD) on a smartphone in which the PAs are detected based on the reflection profiles of the transmitted signal. We propose a novel transmission signal based on the wide pulse that allows us to model the background noise before transmitting the signal and increase the Signal-to-Noise Ratio (SNR). The received signal reflections were processed to remove background noise and accurately represent reflection characteristics. The reflection profiles of the bona fide and PAs are different owing to the different reflection characteristics of the human skin and artefact materials. Extensive experiments are presented using the newly collected Acoustic Sound Echo Dataset (ASED) with 4807 samples captured from bona fide and four different types of PAIs, including print (two types), display, and silicone face-mask attacks. The obtained results indicate the robustness of the proposed method for detecting unknown face presentation attacks.