AI Facial Recognition System
Ahmadli, Ogtay (2022)
2022
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202202042110
https://urn.fi/URN:NBN:fi:amk-202202042110
Tiivistelmä
Nowadays, facial recognition is one of the widely used categories of biometric security that distinguish itself by its security and speed from other categories such as fingerprint recognition and eye retina or iris recognition. This technology is mainly used in electronics devices, airport control, banking, health care, marketing, and advertising. This thesis project aimed to build a facial recognition system that could recognize people through the camera and unlock the door locks. Recognized results were sent to the database and could be analyzed by users after the successful login.
The project consists of building a facial recognition, electronics operation, and webpage design for the database. Firstly, machine learning and deep learning algorithms were used to recognize faces. In the second step, AI data is transmitted to the electronics components and sensors to make a smart lock system. Finally, the last step was to design a user interface that requires a login and displays the attendance list according to the database.
The prototype could successfully recognize human faces and activate the electronics components. It has fast performance and could log information about recognized humans in the Google database.
With further advancements, the prototype would implement more extensive algorithms to distinguish the pictures and real faces through a camera. These algorithms would make the prototype faster, secure, and suitable for commercial purposes.
The project consists of building a facial recognition, electronics operation, and webpage design for the database. Firstly, machine learning and deep learning algorithms were used to recognize faces. In the second step, AI data is transmitted to the electronics components and sensors to make a smart lock system. Finally, the last step was to design a user interface that requires a login and displays the attendance list according to the database.
The prototype could successfully recognize human faces and activate the electronics components. It has fast performance and could log information about recognized humans in the Google database.
With further advancements, the prototype would implement more extensive algorithms to distinguish the pictures and real faces through a camera. These algorithms would make the prototype faster, secure, and suitable for commercial purposes.