  Here, we present IDNet, a user authentication framework from
smartphone-acquired motion signals. Its goal is to recognize a target user from
their way of walking, using the accelerometer and gyroscope (inertial) signals
provided by a commercial smartphone worn in the front pocket of the user's
trousers. IDNet features several innovations including: i) a robust and
smartphone-orientation-independent walking cycle extraction block, ii) a novel
feature extractor based on convolutional neural networks, iii) a one-class
support vector machine to classify walking cycles, and the coherent integration
of these into iv) a multi-stage authentication technique. IDNet is the first
system that exploits a deep learning approach as universal feature extractors
for gait recognition, and that combines classification results from subsequent
walking cycles into a multi-stage decision making framework. Experimental
results show the superiority of our approach against state-of-the-art
techniques, leading to misclassification rates (either false negatives or
positives) smaller than 0.15% with fewer than five walking cycles. Design
choices are discussed and motivated throughout, assessing their impact on the
user authentication performance.
