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Automatic Affect State Detection using Fiducial Points for Facial Expression Analysis

Samara, Anas, Galway, Leo, Bond, Raymond R and Wang, Hui (2016) Automatic Affect State Detection using Fiducial Points for Facial Expression Analysis. In: Irish Human Computer Interaction Conference, Cork. iHCI. 1 pp. [Conference contribution]

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URL: https://irishhci2016.wordpress.com/poster-abstracts/

Abstract

Current advancements in digital technology indicate that there is an opportunity to enhance computers with automated intelligence in order to understand human feelings and emotions that may be relevant to systems performance. Furthermore, one of the most important aspects of the Ubiquitous Computing paradigm is that machines should be characterised by autonomy and context awareness to facilitate more intelligent interaction. Therefore, there is an opportunity to enhance computer systems with automated intelligence in order to permit natural and reliable interaction similar to human-human interaction. Although various techniques have been proposed for automatically detecting a user’s affective state using facial expressions, this is still a research challenge in terms of achieving a consistently high level of classification accuracy. The current research probes the use of facial expressions as an input perception modality for computer systems. Facial expressions, which are deemed the most effective input channel in the domain of Affective Computing, are generated from the movements of facial muscles from different regions of the face; primarily the mouth, nose, eyes, eyebrows, and forehead. Subsequently, due to the correlation between facial expressions and human emotions, it is foreseen that automatic facial expression analysis will endow computer systems with the ability to recognise human affective states. The presented study considers the use of facial point distance vectors within the representation of facial expressions, along with investigations into a range of supervised machine learning techniques, for affective state classification. Results indicate a higher level of classification accuracy and robustness is achievable, in comparison to using standard Cartesian coordinates from the fiducial points.

Item Type:Conference contribution (Poster)
Keywords:Human computer interaction, HCI, facial expression analysis, affective computing, digital empathy, user interfaces
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute > Smart Environments
Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:36239
Deposited By: Dr Raymond Bond
Deposited On:16 Nov 2016 11:42
Last Modified:17 Oct 2017 16:26

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