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Fingerprint presentation attack detection (FPAD) is becoming an increasinglychallenging 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 sensingmodality for FPAD. LSCI has the interesting characteristic of capturing theblood flow under the skin surface. Toward studying the importance andeffectiveness of LSCI for FPAD, we conduct a comprehensive study usingdifferent patch-based deep neural network architectures. Our studiedarchitectures include 2D and 3D convolutional networks as well as a recurrentnetwork using long short-term memory (LSTM) units. The study demonstrates thatstrong FPAD performance can be achieved using LSCI. We evaluate the differentmodels over a new large dataset. The dataset consists of 3743 bona fidesamples, collected from 335 unique subjects, and 218 presentation attacksamples, including six different types of attacks. To examine the effect ofchanging the training and testing sets, we conduct a 3-fold cross validationevaluation. To examine the effect of the presence of an unseen attack, we applya leave-one-attack out strategy. The FPAD classification results of thenetworks, which are separately optimized and tuned for the temporal and spatialpatch-sizes, indicate that the best performance is achieved by LSTM.

On the Effectiveness of Laser Speckle Contrast Imaging and Deep Neural Networks for Detecting Known and Unknown Fingerprint Presentation Attacks