APPLICATION OF FACIAL RECOGNITION USING CONVOLUTIONAL NEURAL NETWORK MODEL IN DORMITORY MANAGEMENT

Pham Tien Quyen; Kim Quang Chieu; Nguyen Thi Ngoc; Le Thu Huong

Pham Tien Quyen Hung Yen University of Technology and Education
Kim Quang Chieu
Nguyen Thi Ngoc Hung Yen University of Technology and Education
Le Thu Huong Hung Yen University of Technology and Education

Keywords: Convolutional Neural Network (CNN), Face Recognition, Security Monitoring, On-campus Residential Management

Abstract

Nowadays, Convolutional Neural Networks (CNNs) are widely applied in various fields to help humans detect surface defects, product errors, and perform facial recognition. They are particularly utilized in security surveillance through cameras, such as identifying crimes in law enforcement, image recognition, and monitoring at airports. In this article, the author’s team utilizes a CNN model for security surveillance at the dormitory of the Hung Yen University of Technology and Education to enhance residential security. The results show that the machine learning model developed achieves a facial recognition performance of over 97%. Based on this research outcome, security monitoring at the student dormitory will be implemented through a camera system. This device will send images to a computer, identify and detect individuals not residing in the dormitory, and subsequently issue warnings while storing the images as data for further processing. The findings of this study will contribute to making security monitoring within the dormitory more rigorous, efficient, and convenient compared to traditional security surveillance methods.

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