Work

An Experimental Study of Facial Recognition Performance for Real-Time UAV Surveillance in Urban Environments

Year: 2024

Type: article

Abstract: In today’s era, facial recognition is common in every aspect of life whether in smartphones, security, attendance management, healthcare, and law enforcement. Modern facial recognition uses a state-of-the-art camera mounted on each device, but we need to integrate it at a certain height for security purposes. To solve this problem, this article proposes real-time facial recognition through drone imaging for urban safety and surveillance. In this article, the Unmanned Aerial Vehicle is equipped with sensors and is allowed to fly in urban locations and capture images to find the suspected one through the image processing technique Convolutional Neural Networks (CNNs). In this research, performances of three facial recognition systems, Local Binary Pattern Histogram (LBPH), FaceNet, and Face_Recognition systems have been presented for Frames Per Second, Accuracy, and real-time tracking. A comparative analysis of the proposed work is also presented to validate the work where a minimum confidence threshold of 80% is achieved.

Source: Pakistan Journal of Engineering Technology & Science

DOI: 10.22555/pjets.v12i2.1253

Authors: Muhammad Shafiq, Beenish Ayesha Akram, Samar Raza Talpur, Leezna Saleem

Institutions: Karachi Institute of Economics and Technology, University of Engineering and Technology Lahore, Sukkur IBA University, Government of Pakistan

Language: English

Topic: Face recognition and analysis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: diamond

OpenAlex ID: W4408633526

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