Securing Smartphone Handwritten Pin Codes with Recurrent Neural Networks

This paper investigates the use of recurrent neural networks to secure PIN code based authentication on smartphones, in a scenario where the user is invited to draw digits on the touchscreen. From the sequence of successive positions of the users finger on the touchscreen, a bidirectional recurrent neural network computes a discriminative embedding in terms of writer traits, carrying the contextual information of the written digit. This allows to reject impostors who would have knowledge of the PIN code. The neural network is trained to recognize both users and digits of a training dataset. Evaluations are run on two datasets of 43 and 33 users, respectively, absent from the training dataset. Results show that when enrolling the users on 4 examples of each digit, the Equal Error Rate reaches 4.9% for a 4-digit PIN code. Including digit value prediction during training is key to achieve good performances.