Face presentation attack detection based on siamese-LSTM and analysis of optic flow and facial landmarks
DOI:
https://doi.org/10.24054/rcta.v1i43.2888Keywords:
Biometrics, anti-spoofing, Siamese neural network, LSTM network, optical flow, facial landmark pointsAbstract
Facial biometrics authentication has become essential in verifying the identity of individuals in online transactions, as classic mechanisms like username and password authentication have proven unreliable due to users often choosing easily memorable passwords. However, advances in model manufacturing with materials such as latex, print quality improvements, and screen resolution enhancements have demanded that fraud detection systems quickly adapt to new conditions. This paper proposes to address the problem of detecting presentation attacks by extracting optical flow and facial landmarks and analyzing them through a Siamese-LSTM network. The proposed model was evaluated using three datasets: Rose-youtu, Replay-attack, and Replay-mobile, and two metrics: HTER and EER
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