  Current mobile user authentication systems based on PIN codes, fingerprint,
and face recognition have several shortcomings. Such limitations have been
addressed in the literature by exploring the feasibility of passive
authentication on mobile devices through behavioral biometrics. In this line of
research, this work carries out a comparative analysis of unimodal and
multimodal behavioral biometric traits acquired while the subjects perform
different activities on the phone such as typing, scrolling, drawing a number,
and tapping on the screen, considering the touchscreen and the simultaneous
background sensor data (accelerometer, gravity sensor, gyroscope, linear
accelerometer, and magnetometer). Our experiments are performed over HuMIdb,
one of the largest and most comprehensive freely available mobile user
interaction databases to date. A separate Recurrent Neural Network (RNN) with
triplet loss is implemented for each single modality. Then, the weighted fusion
of the different modalities is carried out at score level. In our experiments,
the most discriminative background sensor is the magnetometer, whereas among
touch tasks the best results are achieved with keystroke in a fixed-text
scenario. In all cases, the fusion of modalities is very beneficial, leading to
Equal Error Rates (EER) ranging from 4% to 9% depending on the modality
combination in a 3-second interval.
