  We utilize the dynamics involved in the imaging of a fingerprint on a
touch-based fingerprint reader, such as perspiration, changes in skin color
(blanching), and skin distortion, to differentiate real fingers from spoof
(fake) fingers. Specifically, we utilize a deep learning-based architecture
(CNN-LSTM) trained end-to-end using sequences of minutiae-centered local
patches extracted from ten color frames captured on a COTS fingerprint reader.
A time-distributed CNN (MobileNet-v1) extracts spatial features from each local
patch, while a bi-directional LSTM layer learns the temporal relationship
between the patches in the sequence. Experimental results on a database of
26,650 live frames from 685 subjects (1,333 unique fingers), and 32,910 spoof
frames of 7 spoof materials (with 14 variants) shows the superiority of the
proposed approach in both known-material and cross-material (generalization)
scenarios. For instance, the proposed approach improves the state-of-the-art
cross-material performance from TDR of 81.65% to 86.20% @ FDR = 0.2%.
