Unsupervised Learning for Face Anti-Spoofing Models
Typ
Examensarbete för masterexamen
Program
Publicerad
2022
Författare
Molander, Gustav
Nilsson, Jens
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Distinguishing images of bonafide (genuine) faces from Presentation Attacks (PA)
or spoofs attracts increasing attention in industry due to the wide application of reliable
automatic authentication. The anomaly detection problem in high dimensional
data such as images can be addressed as a supervised or unsupervised learning problem,
but when spoof data is sparse and not all spoof domains can be represented
in training the unsupervised model can generalize knowledge to unseen domains
to a higher extent. The unsupervised anomaly detection paradigm is essentially a
one-class learning problem to distinguish the distribution of bonafide images from
everything else.
In this thesis we explore the mathematical framework and implementation of encoderdecoder-
based deep learning models to learn the distribution of real images and classify
spoofs. This thesis is preliminary work on the end goal of designing a lightweight
unsupervised anti-spoofing model to run in Smart Eye automotive software. The
researched models reproduce input images by utilizing a latent space embedding
fitted to the distribution of bonafide images. The latent space along with the reconstructed
image is used to classify spoofs.
The results of the experiments show promise but are not yet at the level feasible for
implementation in production devices. The models have been evaluated in terms of
the spoof precision and recall as well as the embedding of the spoofs in the latent
space. Further, some models use Gaussian Mixture Models (GMM) of the latent
space to determine the spoof affiliation, though these results are inconclusive.
Beskrivning
Ämne/nyckelord
variational inference, deep learning, face anti-spoofing, variational autoencoder, Smart Eye, CNN, latent variable models