1 research outputs found
Resist : Reconstruction of irises from templates
Iris recognition systems transform an iris image into a feature vector. The
seminal pipeline segments an image into iris and non-iris pixels, normalizes
this region into a fixed-dimension rectangle, and extracts features which are
stored and called a template (Daugman, 2009). This template is stored on a
system. A future reading of an iris can be transformed and compared against
template vectors to determine or verify the identity of an individual. As
templates are often stored together, they are a valuable target to an attacker.
We show how to invert templates across a variety of iris recognition systems.
Our inversion is based on a convolutional neural network architecture we call
RESIST (REconStructing IriSes from Templates). We apply RESIST to a traditional
Gabor filter pipeline, to a DenseNet (Huang et al., CVPR 2017) feature
extractor, and to a DenseNet architecture that works without normalization.
Both DenseNet feature extractors are based on the recent ThirdEye recognition
system (Ahmad and Fuller, BTAS 2019). When training and testing using the
ND-0405 dataset, reconstructed images demonstrate a rank-1 accuracy of 100%,
76%, and 96% respectively for the three pipelines. The core of our approach is
similar to an autoencoder. To obtain high accuracy this core is integrated into
an adversarial network (Goodfellow et al., NeurIPS, 2014