As our online and physical systems become increasingly complex, the authentication of individuals have become integral in securing sensitive personal and proprietary information.
MagicPaper maintains the integrity of critical systems by incorporating touchless gestures with unique capacitance levels of individuals. The capacitance thresholds are calibrated into the system along with the encrypted, gesture password to provide two layers of security.
The major challenge we faced included interfacing the Arduino output with software. Due to the sensitive nature of the capacitive sensors, the calibration and signal processing required additional filters and smoothing functions to ensure clean data.
Our team is pleased with our work in experimenting with concepts of capacitance in achieving biometric authentication. However, we were most proud of leveraging each others' skills and strengths and also in the high level of open communication and collaboration we had in tackling the technological and interpersonal challenges we faced.
On the technological side, our team developed an understanding of electromagnetism, low-level hardware programming, and Node.js. In addition, we learned how to approach an open-ended problem and identifying the required functions to develop a minimum functioning product as proof of concept.
In the future, MagicPaper will focus on increasing resolution of capacitative measurements allowing higher levels of accuracy and more sophisticated encryption algorithms. The packaging of this technology with two-factor authentication, security locks or existing devices will also be explored.