  In this paper, we propose a deep learning approach for smartphone user
identification based on analyzing motion signals recorded by the accelerometer
and the gyroscope, during a single tap gesture performed by the user on the
screen. We transform the discrete 3-axis signals from the motion sensors into a
gray-scale image representation which is provided as input to a convolutional
neural network (CNN) that is pre-trained for multi-class user classification.
In the pre-training stage, we benefit from different users and multiple samples
per user. After pre-training, we use our CNN as feature extractor, generating
an embedding associated to each single tap on the screen. The resulting
embeddings are used to train a Support Vector Machines (SVM) model in a
few-shot user identification setting, i.e. requiring only 20 taps on the screen
during the registration phase. We compare our identification system based on
CNN features with two baseline systems, one that employs handcrafted features
and another that employs recurrent neural network (RNN) features. All systems
are based on the same classifier, namely SVM. To pre-train the CNN and the RNN
models for multi-class user classification, we use a different set of users
than the set used for few-shot user identification, ensuring a realistic
scenario. The empirical results demonstrate that our CNN model yields a top
accuracy of 89.75% in multi-class user classification and a top accuracy of
96.72% in few-shot user identification. In conclusion, we believe that our
system is ready for practical use, having a better generalization capacity than
both baselines.
