Earprint touchscreen sensoring comparison between hand-crafted features and transfer learning for smartphone authentication

The smartphone’s lock screen is at a threshold between usability and comfort. For example, some smartphone users prefer not to use the sliding or acceptance call button, but a more secure and efficient way of picking up the phone instead. Others prefer the smoothest interaction possible with their devices for getting quick access to smartphone services. In this paper, from a smartphone authentication point of view, we propose using the touchscreen as an ear shape detector. This approach helps verify the right user for incoming calls, supporting user privacy, as well as avoiding any action approval through a button. In a one-against-all authentication scheme, looking for the best discrimination model, genuine and impostor data are evaluated with two different authentication engines: (i.) Transfer Learning (ii.) Different classifiers are fed by fused hand-crafted features like LBP, HoG, and LIOP. Previous to both authentication approaches execution, the ear shape is extracted by an own heuristic architecture to remove skin-related noises and highlight the region of interest. The classifier results of this paper confirm that Earprint guarantees user verification, reaching an accuracy of 97.7.