A Bio-metric-based authentication and verification system for students in MAKERERE
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Date
2019-05Author
Bamwireku, Prisca
Kamukama, Dariet
Nabimanya, Lynn
Nyonyintono, Pitwa Isaac
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Show full item recordAbstract
Makerere University employs the method of examination permits in order
to validate it’s students and ensure overall completion of tuition payment.
Although this method has kept the institution running for a long time now,
it has major drawbacks. Forgery of examination permits and impersonation
has long crept into our education system leaving a desperate need for a more
efficient authentication and verification system in its wake. Also, relying on
examination permits is absurd because paper is easily corruptible, damaged,
lost or even forgotten, and all of these instances gravely inconvenience both
students and administrators.
This research work designed and implemented a Bio-metric-based identity
verification System to be used as a verification system to allow students of
Makerere University into examination rooms. The system replaces the use
of examination permits which the University has been using previously with
use of individual fingerprints instead. On running their fingerprint through
a biometrics fingerprint reader, the student’s fingerprint is captured and the
system uses that it to implement a principle of Machine Learning, the Con-
volutional Neural Network to identify the student to whom that fingerprint
belongs from a database containing all student’s information. This network
was trained using a data set of fingerprints until it successfully “learned” to
identify a given fingerprint from a collection of other different fingerprints
and return it’s student number. On identifying the student number of that
fingerprint, their corresponding financial and registration status for the paper
they intend to sit is verified by the system, and if they are in the clear the
Arduino signals with a green light. If, however the student has not completed
their tuition payments, or did not register for the examination that is being
sat, it signals a red and white light respectively to alert the invigilators.
Software tools and technologies used in the development of this system in-
cluded Tensorflow for developing the Convolutional Neural Network to im-
plement Machine Learning in this project. We also used C++ to programhe LED lights on the arduino hardware to light differently according to a
different outcome while the databases were run using MySQL.
The system was tested to check for faults and errors. After training the neural
network with a sample data set, it was tested to identify the corresponding
student number of individual fingerprints and it successfully returned stu-
dent numbers with an accuracy of up to 98.0%. .
Achievements and limitations faced during the course of this project are at-
tached to this report, alongside with conclusions and recommended future
works.