- author = "James Alexander Hughes and Joseph Alexander Brown and Adil Mehmood Khan",
- title = "Smartphone Gait Fingerprinting Models via Genetic Programming",
- booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)",
- year = "2016",
- editor = "Yew-Soon Ong",
- pages = "408--415",
- address = "Vancouver",
- month = "24-29 " # jul,
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, Symbolic Regression, Mathematical Model, Human Walking Models, Gait",
- isbn13 = "978-1-5090-0623-6",
-
DOI = "
doi:10.1109/CEC.2016.7743823",
-
abstract = "The idea of using the gait of a walking person asa
biometric identification method has been seen in a
number of proposed authentication methods, yet previous
works focus on the addition of other authentication
methods along with the gait, or have required a
stationary sensor attached to the hip of the user. This
paper uses a Genetic Programming model in order to act
as an identifier of gait fingerprints from two users
sampled from the accelerometer in a commercially
available phone. With the phone freely placed within a
pocket, users moved without a fixed protocol at a
normal, nonuniform pace. This design of data collection
more closely matches the real world applications of
such a method.
The highly specialized Genetic Programming system with multiple modular enhancements was implemented to perform symbolic regression. The system was demonstrated to be robust to noise and was able to effectively model each dataset with high accuracy. It was also determined that a model could be generated for a subject's whole dataset from only a single step's worth of data. Top models were applied to other subject's data in order to evaluate the uniqueness of these mathematical models.",
- notes = "CEC2016 WCCI2016",
