|
|
Explainable Face Liveness Classification
Mičulek, Petr ; Beran, Vítězslav (oponent) ; Špaňhel, Jakub (vedoucí práce)
The goal of this thesis is to explore, develop, and evaluate explainable face presentation attack detection (PAD) systems. PAD systems act as security filters for face recognition, preventing spoofed faces from reaching the identification phase. These systems are a necessary component enabling the recent rise of biometric systems used in smartphones and security cameras. While neural networks are the standard method for this task, they are commonly a black-box method providing no explanation. To provide a better understanding of the detection process, input attribution methods are applied. Their suitability is studied and various variants are compared. Of the seven methods compared, GradCAM using test-time augmentation is evaluated as the best, achieving a deletion metric AUC of 0.658 and an insertion metric AUC of 0.908. Experiments with the explanations show their limited capability at helping understand the model, but provide hints at how the predictive accuracy of the PAD system can be verified, and possibly improved.
|
host ::
RSS.