research-article

Have We Met Before? Using Consumer-Grade Brain-Computer Interfaces to Detect Unaware Facial Recognition

Authors:

Christopher Bellman,

Miguel Vargas Martin,

Shane MacDonald,

Ramiro LiscanoAuthors Info & Claims

Computers in Entertainment (CIE), Volume 16, Issue 2

Article No.: 7, Pages 1 - 17

https://doi.org/10.1145/3180661

Published: 10 April 2018 Publication History

Abstract

Much research has been done on the brain’s reaction to seeing faces, but while much of the work has investigated the brain’s conscious reaction to faces, far less work has been done exploring the brain’s unaware reactions using consumer-grade devices. Built on previous work, we describe an experiment conducted using EEGs and consumer-grade Brain-Computer Interface (BCI) headsets to measure the brain’s unaware reaction to seeing faces of three pre-defined recognition classes: no recognition, unaware recognition, and aware recognition. We pre-select images to be shown in each class and display the images in a two-day experiment where participants implicitly learn images tagged as “unaware recognition” for use in the second day. It was found that, outperforming previous works, unaware facial recognitions could be detected with fairly high accuracies using a method that combines multiple sensors from a BCI device and utilizing out-of-the-box classification methods.

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Cited By

Alomari RVargas Martin MMacDonald SBellman C(2019)Using EEG to Predict and Analyze Password Memorability2019 IEEE International Conference on Cognitive Computing (ICCC)10.1109/ICCC.2019.00019(42-49)Online publication date: Jul-2019
https://doi.org/10.1109/ICCC.2019.00019
(2018)Guest Editorial Preface Deep Learning, Ubiquitous, and Toy ComputingComputers in Entertainment 10.1145/318066316:2(1-4)Online publication date: 10-Apr-2018
https://dl.acm.org/doi/10.1145/3180663
Alomari RVargas Martin M(2018)Classification of EEG Signals Using Neural Networks to Predict Password Memorability2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA.2018.00126(791-796)Online publication date: Dec-2018
https://doi.org/10.1109/ICMLA.2018.00126

Index Terms

Have We Met Before? Using Consumer-Grade Brain-Computer Interfaces to Detect Unaware Facial Recognition
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image Computers in Entertainment

Computers in Entertainment Volume 16, Issue 2

Special Issue: Deep Learning, Ubiquitous and Toy Computing

April 2018

152 pages

EISSN:1544-3574

DOI:10.1145/3181320

Issue’s Table of Contents

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 April 2018

Accepted: 01 June 2017

Revised: 01 May 2017

Received: 01 December 2016

Published in CIE Volume 16, Issue 2

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Cited By

Alomari RVargas Martin MMacDonald SBellman C(2019)Using EEG to Predict and Analyze Password Memorability2019 IEEE International Conference on Cognitive Computing (ICCC)10.1109/ICCC.2019.00019(42-49)Online publication date: Jul-2019
https://doi.org/10.1109/ICCC.2019.00019
(2018)Guest Editorial Preface Deep Learning, Ubiquitous, and Toy ComputingComputers in Entertainment 10.1145/318066316:2(1-4)Online publication date: 10-Apr-2018
https://dl.acm.org/doi/10.1145/3180663
Alomari RVargas Martin M(2018)Classification of EEG Signals Using Neural Networks to Predict Password Memorability2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA.2018.00126(791-796)Online publication date: Dec-2018
https://doi.org/10.1109/ICMLA.2018.00126

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