Earprint touchscreen sensoring comparison between
hand-crafted features and transfer learning for smartphone authentication Jose-Luis Cabra1+, Carlos Parra2, and Luis Trujillo2
1Fundacion Universitaria
Compensar, Avenida (Calle)
32 No. 17-30. Bogota, 111311, Colombia 2Pontificia Universidad Javeriana,
Ak. 7 #40 - 62. Bogota, 110231,
Colombia Abstract 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 ef[1]ficient 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 au[1]thentication 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 discrim[1]ination
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. Keywords: Smartphone
Authentication, Earprint, Capacitive images, Machine Learning, Touchscreen +: Corresponding author: Jose-Luis Cabra Journal of Internet Services and
Information Security (JISIS), 12(3): 16-29, August 2022 |