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Automatic Emotion Recognition through Facial Expression Analysis in Merged Images Based on an Artificial Neural Network
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2013 (English)In: 2013 12th Mexican International Conference on Artificial Intelligence (MICAI): Proceedings, IEEE Computer Society, 2013, 85-96 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper focuses on a system of recognizing human’s emotion from a detected human’s face. The analyzed information is conveyed by the regions of the eye and the mouth into a merged new image in various facial expressions pertaining to six universal basic facial emotions. The output information obtained could be fed as an input to a machine capable to interact with social skills, in the context of building socially intelligent systems. The methodology uses a classification technique of information into a new fused image which is composed of two blocks integrated by the area of the eyes and mouth, very sensitive areas to changes human’s expression and that are particularly relevant for the decoding of emotional expressions. Finally we use the merged image as an input to a feed-forward neural network trained by back-propagation. Such analysis of merged images makes it possible, obtain relevant information through the combination of proper data in the same image and reduce the training set time while preserved classification rate. It is shown by experimental results that the proposed algorithm can detect emotion with good accuracy.

Place, publisher, year, edition, pages
IEEE Computer Society, 2013. 85-96 p.
Keyword [en]
Artificial Neural Network, Merged Images, Facial Expression Recognition, Emotions, Detection of Emotional Information.
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-97708DOI: 10.1109/MICAI.2013.16ISBN: 978-1-4799-2605-3 (print)OAI: oai:DiVA.org:su-97708DiVA: diva2:679938
Conference
12th Mexican International Conference on Artificial Intelligence, November 24-30, 2013, Mexico City, Mexico
Available from: 2013-12-17 Created: 2013-12-17 Last updated: 2015-12-14Bibliographically approved
In thesis
1. Decisional-Emotional Support System for a Synthetic Agent: Influence of Emotions in Decision-Making Toward the Participation of Automata in Society
Open this publication in new window or tab >>Decisional-Emotional Support System for a Synthetic Agent: Influence of Emotions in Decision-Making Toward the Participation of Automata in Society
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Emotion influences our actions, and this means that emotion has subjective decision value. Emotions, properly interpreted and understood, of those affected by decisions provide feedback to actions and, as such, serve as a basis for decisions. Accordingly, "affective computing" represents a wide range of technological opportunities toward the implementation of emotions to improve human-computer interaction, which also includes insights across a range of contexts of computational sciences into how we can design computer systems to communicate and recognize the emotional states provided by humans. Today, emotional systems such as software-only agents and embodied robots seem to improve every day at managing large volumes of information, and they remain emotionally incapable to read our feelings and react according to them. From a computational viewpoint, technology has made significant steps in determining how an emotional behavior model could be built; such a model is intended to be used for the purpose of intelligent assistance and support to humans. Human emotions are engines that allow people to generate useful responses to the current situation, taking into account the emotional states of others. Recovering the emotional cues emanating from the natural behavior of humans such as facial expressions and bodily kinetics could help to develop systems that allow recognition, interpretation, processing, simulation, and basing decisions on human emotions. Currently, there is a need to create emotional systems able to develop an emotional bond with users, reacting emotionally to encountered situations with the ability to help, assisting users to make their daily life easier. Handling emotions and their influence on decisions can improve the human-machine communication with a wider vision. The present thesis strives to provide an emotional architecture applicable for an agent, based on a group of decision-making models influenced by external emotional information provided by humans, acquired through a group of classification techniques from machine learning algorithms. The system can form positive bonds with the people it encounters when proceeding according to their emotional behavior. The agent embodied in the emotional architecture will interact with a user, facilitating their adoption in application areas such as caregiving to provide emotional support to the elderly. The agent's architecture uses an adversarial structure based on an Adversarial Risk Analysis framework with a decision analytic flavor that includes models forecasting a human's behavior and their impact on the surrounding environment. The agent perceives its environment and the actions performed by an individual, which constitute the resources needed to execute the agent's decision during the interaction. The agent's decision that is carried out from the adversarial structure is also affected by the information of emotional states provided by a classifiers-ensemble system, giving rise to a "decision with emotional connotation" included in the group of affective decisions. The performance of different well-known classifiers was compared in order to select the best result and build the ensemble system, based on feature selection methods that were introduced to predict the emotion. These methods are based on facial expression, bodily gestures, and speech, with satisfactory accuracy long before the final system.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2015. 146 p.
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 15-019
Keyword
Affective Computing; Machine Learning; Adversarial Risk Analysis; Broaden and Build Theory; Facial Expression Recognition; Speech Emotion Recognition; Detection of Emotional Information; Emotional self-regulation
National Category
Human Computer Interaction
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-122084 (URN)978-91-7649-291-8 (ISBN)
Public defence
2015-12-14, room L70, NOD Building, Borgarfjordsgatan 12, Kista, 13:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 8: Accepted.

Available from: 2015-11-20 Created: 2015-10-23 Last updated: 2015-11-30Bibliographically approved

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Rázuri, Javier G.Sundgren, DavidRahmani, Rahim
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