DIRECT DETECTION OF PEOPLE WEARING GLASSES USING THE HAARCASCADE CLASSIFIER

ALFONS, BILLY CORNELIO (2022) DIRECT DETECTION OF PEOPLE WEARING GLASSES USING THE HAARCASCADE CLASSIFIER. Other thesis, Universitas Katholik Soegijapranata Semarang.

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Abstract

In this day and age, many people wear glasses to help their eyesight or to add style to make them look more attractive. In some places it is mandatory for someone not to wear glasses for certain reasons such as someone who goes to an ATM machine not to wear dark colored glasses so that his face can be seen clearly and is better known for security reasons. In this situation it is very important for some places to be able to detect a person wearing glasses using the camera directly for some reason in order to quickly recognize the person's face more clearly. The haarcascade classification algorithm is an algorithm that can detect faces and eyes directly and quickly using a camera connected to a computer. The haarcascade that I use is the frontalface haarcascade to detect faces and the eye haarcascade to detect the eyes, and the results of the detection if the face and eyes are detected, it is certain that someone is not wearing glasses and vice versa if only a face is detected, it is certain that someone is wearing glasses. OpenCV to insert live video and processed by the library that we use. The final result that we get in this project is an image that has been captured and has been run through a dataset, namely direct video input that has been processed using haarcascade frontalface and haarcascade eye and opencv. At the top of a person's face there will be a text that explains whether the person is wearing glasses or not, and can count the number of faces and eyes of a person recorded on the camera.

Item Type:	Thesis (Other)
Subjects:	000 Computer Science, Information and General Works > 005 Computer programming, programs & data
Divisions:	Faculty of Computer Science > Department of Informatics Engineering
Depositing User:	mr AM. Pudja Adjie Sudoso
Date Deposited:	23 Mar 2022 04:10
Last Modified:	23 Mar 2022 04:10
URI:	http://repository.unika.ac.id/id/eprint/28278

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