  Fingerprint presentation attack detection (FPAD) is becoming an increasingly
challenging problem due to the continuous advancement of attack techniques,
which generate `realistic-looking' fake fingerprint presentations. Recently,
laser speckle contrast imaging (LSCI) has been introduced as a new sensing
modality for FPAD. LSCI has the interesting characteristic of capturing the
blood flow under the skin surface. Toward studying the importance and
effectiveness of LSCI for FPAD, we conduct a comprehensive study using
different patch-based deep neural network architectures. Our studied
architectures include 2D and 3D convolutional networks as well as a recurrent
network using long short-term memory (LSTM) units. The study demonstrates that
strong FPAD performance can be achieved using LSCI. We evaluate the different
models over a new large dataset. The dataset consists of 3743 bona fide
samples, collected from 335 unique subjects, and 218 presentation attack
samples, including six different types of attacks. To examine the effect of
changing the training and testing sets, we conduct a 3-fold cross validation
evaluation. To examine the effect of the presence of an unseen attack, we apply
a leave-one-attack out strategy. The FPAD classification results of the
networks, which are separately optimized and tuned for the temporal and spatial
patch-sizes, indicate that the best performance is achieved by LSTM.
