Deep learning-based face detection and recognition on drones

• Published in: Journal of ambient intelligence and humanized computing 2024, Vol.15 (1), p.373-387
• Main Authors: Rostami, Mohsen, Farajollahi, Amirhamzeh, Parvin, Hashem
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Artificial neural networks; Classification; Clustering; Computational Intelligence; Deep learning; Drone aircraft; Drone vehicles; Drones; Engineering; Face recognition; Facial recognition technology; High altitude; Machine learning; Original Research; Pattern recognition; Performance evaluation; Remote monitoring; Robotics and Automation; Semantics; Surveillance; Unmanned aerial vehicles; User Interfaces and Human Computer Interaction
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-022-03897-8

Unmanned aerial vehicles as known as drones, are aircraft that can comfortably search locations which are excessively dangerous or difficult for humans and take data from bird's-eye view. Enabling unmanned aerial vehicles to detect and recognize humans on the ground is essential for various applications, such as remote monitoring, people search, and surveillance. The current face detection and recognition models are able to detect or recognize faces on unmanned aerial vehicles using various limits in height, angle and distance, mainly where drones take images from high altitude or long distance. In the present paper, we proposed a novel face detection and recognition model on drones for improving the performance of face recognition when query images are taken from high altitudes or long distances that do not show much facial information of the humans. Moreover, we aim to employ deep neural network to perform these tasks and reach an enhanced top performance. Experimental evaluation of the proposed framework compared to state-of-the-art models over the DroneFace dataset demonstrates that our method can attain competitive accuracy on both the recognition and detection protocols.