A Machine Learning Emotion Detection Platform to Support Affective Well Being

Michael Healy, Ryan Donovan, Paul Walsh, Huiru Zheng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)

Abstract

This paper describes a new emotional detection system based on a video feed in real-time. It demonstrates how a bespoke machine learning support vector machine (SVM) can be utilized to provide quick and reliable classification. Features used in the study are 68-point facial landmarks. In a lab setting, the application has been trained to detect six different emotions by monitoring changes in facial expressions. Its utility as a basis for evaluating the emotional condition of people in situations using video and machine learning is discussed.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Bioinformatics and Biomedicine(BIBM 2018)
Place of PublicationMadrid, Spain
PublisherIEEE
Pages2694-2700
ISBN (Electronic)978-1-5386-5488-0
ISBN (Print)978-1-5386-5489-7
DOIs
Publication statusPublished - 3 Dec 2018

Fingerprint

Dive into the research topics of 'A Machine Learning Emotion Detection Platform to Support Affective Well Being'. Together they form a unique fingerprint.

Cite this