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Predicting and improving the recognition of emotions


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2018AHakim_PredictingAndImprovingRecognitionOfEmotions.pdf - Published Version
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The technological world is moving towards more effective and friendly human computer interaction. A key factor of these emerging requirements is the ability of future systems to recognise human emotions, since emotional information is an important part of human-human communication and is therefore expected to be essential in natural and intelligent human-computer interaction. Extensive research has been done on emotion recognition using facial expressions, but all of these methods rely mainly on the results of some classifier based on the apparent expressions. However, the results of classifier may be badly affected by the noise including occlusions, inappropriate lighting conditions, sudden movement of head and body, talking, and other possible problems. In this paper, we propose a system using exponential moving averages and Markov chain to improve the classifier results and somewhat predict the future emotions by taking into account the current as well as previous emotions.

Item Type: Journal article
Uncontrolled Keywords: principal component analysis, short time mood learner, markov chain, exponential moving average
Subjects: Q Science > Q Science (General)
Divisions: Schools > Centre for Business, Information Technology and Enterprise > School of Information Technology
Depositing User: Ayesha Hakim
Date Deposited: 12 Oct 2018 01:56
Last Modified: 21 Jul 2023 07:26

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