Sound-Print: Generalised Face Presentation Attack Detection using Deep
  Representation of Sound Echoes
Raghavendra Ramachandra Jag Mohan Singh Sushma Venkatesh
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.