Digital Signal Processing Reference
In-Depth Information
6.2
Recent Experiments
At present, we are investigating two alternative approaches to this
problem: First technique is based on correlation filters, which have been very
used with encouraging results in multi-sensor biometric identification [2,11].
A similar approached using these two pressure readings, the acceleration
signal (vehicle speed), and other behavioral data including the steering wheel
information can be developed to better identify the drivers. Experiments are
being currently carried out for developing “meaningful and computationally
feasible” MACE filters for each driver. The findings will be reported later. It
is difficult to present any meaningful quantities as this stage but the promise
is much better that the earlier techniques studied above.
In the second technique, however, we are trying to incorporate Gaussian
Mixture Models (GMM) for modeling driver behavior. GMM based
techniques have resulted in promising results for speaker
identification/verification [3, 4, 18, 19]. In our preliminary experiments, we
have chosen a small subset of the 800 driver database (30 drivers with equal
gender split) and the average length of the driving data was approximately 20
minutes. The first half of the each data was used for modeling the driver and
the latter half has been employed for testing the system. We have
experimented with 1, 2, 4, 8 Gaussian mixtures and the sum of the log-
likelihood was used as the identification measure.
We have obtained a correct identification rate of 73.3 percent using the
both the static and dynamic information of accelerator and brake pedal
pressure.
7.
CONCLUSIONS
After a number of very interesting and yet-not-so-encouraging results from
several different approaches, this encouraging preliminary finding (first
success story!) is a very important milestone to achieve our goals of safer
driving, assisting drivers in road emergencies, and to be part of a multi-mode
biometric signature for driver identification. We are planning to present our
GMM approach details and the results in the near future.
