2 research outputs found
End-to-End Fingerprints Liveness Detection using Convolutional Networks with Gram module
This paper proposes an end-to-end CNN(Convolutional Neural Networks) model
that uses gram modules with parameters that are approximately 1.2MB in size to
detect fake fingerprints. The proposed method assumes that texture is the most
appropriate characteristic in fake fingerprint detection, and implements the
gram module to extract textures from the CNN. The proposed CNN structure uses
the fire module as the base model and uses the gram module for texture
extraction. Tensors that passed the fire module will be joined with gram
modules to create a gram matrix with the same spatial size. After 3 gram
matrices extracted from different layers are combined with the channel axis, it
becomes the basis for categorizing fake fingerprints. The experiment results
had an average detection error of 2.61% from the LivDet 2011, 2013, 2015 data,
proving that an end-to-end CNN structure with few parameters that is able to be
used in fake fingerprint detection can be designed.Comment: 15 pages, 7 figure
Multi-Modal Fingerprint Presentation Attack Detection: Evaluation On A New Dataset
Fingerprint presentation attack detection is becoming an increasingly
challenging problem due to the continuous advancement of attack preparation
techniques, which generate realistic-looking fake fingerprint presentations. In
this work, rather than relying on legacy fingerprint images, which are widely
used in the community, we study the usefulness of multiple recently introduced
sensing modalities. Our study covers front-illumination imaging using
short-wave-infrared, near-infrared, and laser illumination; and
back-illumination imaging using near-infrared light. Toward studying the
effectiveness of each of these unconventional sensing modalities and their
fusion for liveness detection, we conducted a comprehensive analysis using a
fully convolutional deep neural network framework. Our evaluation compares
different combination of the new sensing modalities to legacy data from one of
our collections as well as the public LivDet2015 dataset, showing the
superiority of the new sensing modalities in most cases. It also covers the
cases of known and unknown attacks and the cases of intra-dataset and
inter-dataset evaluations. Our results indicate that the power of our approach
stems from the nature of the captured data rather than the employed
classification framework, which justifies the extra cost for hardware-based (or
hybrid) solutions. We plan to publicly release one of our dataset collections