A Machine Learning Emotion Detection Platform to Support Affective Well Being

Michael Healy; Ryan  Donovan; Paul Walsh; Huiru Zheng

doi:10.1109/BIBM.2018.8621562

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 proceeding › Conference contribution › peer-review

27 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 language	English
Title of host publication	2018 IEEE International Conference on Bioinformatics and Biomedicine(BIBM 2018)
Place of Publication	Madrid, Spain
Publisher	IEEE
Pages	2694-2700
ISBN (Electronic)	978-1-5386-5488-0
ISBN (Print)	978-1-5386-5489-7
DOIs	https://doi.org/10.1109/BIBM.2018.8621562
Publication status	Published (in print/issue) - 3 Dec 2018

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10.1109/BIBM.2018.8621562

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title = "A Machine Learning Emotion Detection Platform to Support Affective Well Being",

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.",

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A Machine Learning Emotion Detection Platform to Support Affective Well Being

Abstract

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