Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

Application of Linearization and Approximation

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...

Vector Functions and Motion: Problem Solving

Vector Functions and Motion: Problem Solving

Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

AI-driven 5G IoT e-nose for whiskey classification.

Applied intelligence (Dordrecht, Netherlands)·2025

Same author

5G AI-IoT System for Bird Species Monitoring and Song Classification.

Sensors (Basel, Switzerland)·2024

Same author

Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios.

Sensors (Basel, Switzerland)·2024

Same author

Infrastructure-Wide and Intent-Based Networking Dataset for 5G-and-beyond AI-Driven Autonomous Networks.

Sensors (Basel, Switzerland)·2024

Same author

AI-IoT Low-Cost Pollution-Monitoring Sensor Network to Assist Citizens with Respiratory Problems.

Sensors (Basel, Switzerland)·2023

Same author

Machine-Learning-Based Carbon Dioxide Concentration Prediction for Hybrid Vehicles.

Sensors (Basel, Switzerland)·2023

Search research articles

Related Experiment Video

Updated: Jul 1, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Dynamic-Distance-Based Thresholding for UAV-Based Face Verification Algorithms.

Julio Diez-Tomillo¹, Jose Maria Alcaraz-Calero¹, Qi Wang¹

¹School of Computing, Engineering and Physical Sciences (CEPS), University of the West of Scotland (UWS), Paisley PA1 2BE, UK.

Sensors (Basel, Switzerland)

|December 23, 2023

Summary

This study presents an adaptive face verification system for Unmanned Aerial Vehicles (UAVs). The new method improves accuracy by 15% in diverse public safety scenarios.

Keywords:

Euclidean distance cosine distance face verification siamese network thresholds

More Related Videos

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

Related Experiment Videos

Last Updated: Jul 1, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

Area of Science:

Computer Vision
Biometrics
Artificial Intelligence

Background:

Face verification is vital for security but struggles with varying image conditions like distance, angle, and lighting.
Resolution changes in diverse environments significantly reduce verification accuracy.

Purpose of the Study:

To develop an adaptive face verification solution for Unmanned Aerial Vehicle (UAV)-based public safety.
To address challenges posed by varying distances, angles, and lighting conditions in real-world scenarios.

Main Methods:

An innovative adaptive verification threshold algorithm was developed.
An optimized operation pipeline was designed to handle varying distances between UAVs and subjects.
The solution was implemented on a UAV platform for empirical testing.

Main Results:

The proposed adaptive face verification solution demonstrated improved accuracy.
Empirical comparisons showed a 15% increase in accuracy compared to state-of-the-art methods.
The system effectively accommodates varying distances and environmental conditions.

Conclusions:

The adaptive face verification system offers a robust solution for UAV-based public safety applications.
The developed algorithm and pipeline enhance face verification accuracy under challenging conditions.
This approach significantly advances the reliability of identity authentication in diverse environments.