Extended LBP based Facial Expression Recognition System for Adaptive AI Agent Behaviour

Kamlesh Mistry; Jyoti  Jasekar; Biju Issac; Li Zhang

Extended LBP based Facial Expression Recognition System for Adaptive AI Agent Behaviour

Kamlesh Mistry
, Jyoti Jasekar
, Biju Issac
, Li Zhang

Research output: Contribution to conference › Paper › peer-review

201 Downloads (Pure)

Abstract

Automatic facial expression recognition is widely
used for various applications such as health care, surveillance
and human-robot interaction. In this paper, we present a novel
system which employs automatic facial emotion recognition
technique for adaptive AI agent behaviour. The proposed
system is equipped with kirsch operator based local binary
patterns for feature extraction and diverse classifiers for
emotion recognition. First, we nominate a novel variant of the
local binary pattern (LBP) for feature extraction to deal with
illumination changes, scaling and rotation variations. The
features extracted are then used as input to the classifier for
recognizing seven emotions. The detected emotion is then used
to enhance the behaviour selection of the artificial intelligence
(AI) agents in a shooter game. The proposed system is
evaluated with multiple facial expression datasets and
outperformed other state-of-the-art models by a significant
margin.

Original language	English
Publication status	Accepted/In press - 15 Mar 2018
Event	International Joint Conference on Neural Networks (IJCNN) under IEEE World Congress on Computational Intelligence (WCCI 2018) - Rio de Janerio, Brazil Duration: 8 Jul 2018 → 13 Jul 2018 http://www.ecomp.poli.br/~wcci2018/

Conference

Conference	International Joint Conference on Neural Networks (IJCNN) under IEEE World Congress on Computational Intelligence (WCCI 2018)
Abbreviated title	IJCNN-WCCI-2018
Country/Territory	Brazil
City	Rio de Janerio
Period	8/07/18 → 13/07/18
Internet address	http://www.ecomp.poli.br/~wcci2018/

Bibliographical note

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Access to Document

Accepted manuscriptAccepted author manuscript, 579 KBLicence: CC BY-NC-ND

Cite this

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title = "Extended LBP based Facial Expression Recognition System for Adaptive AI Agent Behaviour",

abstract = "Automatic facial expression recognition is widely used for various applications such as health care, surveillance and human-robot interaction. In this paper, we present a novel system which employs automatic facial emotion recognition technique for adaptive AI agent behaviour. The proposed system is equipped with kirsch operator based local binary patterns for feature extraction and diverse classifiers for emotion recognition. First, we nominate a novel variant of the local binary pattern (LBP) for feature extraction to deal with illumination changes, scaling and rotation variations. The features extracted are then used as input to the classifier for recognizing seven emotions. The detected emotion is then used to enhance the behaviour selection of the artificial intelligence (AI) agents in a shooter game. The proposed system is evaluated with multiple facial expression datasets and outperformed other state-of-the-art models by a significant margin.",

author = "Kamlesh Mistry and Jyoti Jasekar and Biju Issac and Li Zhang",

note = "This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible; International Joint Conference on Neural Networks (IJCNN) under IEEE World Congress on Computational Intelligence (WCCI 2018), IJCNN-WCCI-2018 ; Conference date: 08-07-2018 Through 13-07-2018",

year = "2018",

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day = "15",

language = "English",

url = "http://www.ecomp.poli.br/\textasciitilde{}wcci2018/",

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Extended LBP based Facial Expression Recognition System for Adaptive AI Agent Behaviour. / Mistry, Kamlesh; Jasekar, Jyoti ; Issac, Biju et al.
2018. Paper presented at International Joint Conference on Neural Networks (IJCNN) under IEEE World Congress on Computational Intelligence (WCCI 2018), Rio de Janerio, Brazil.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Extended LBP based Facial Expression Recognition System for Adaptive AI Agent Behaviour

AU - Mistry, Kamlesh

AU - Jasekar, Jyoti

AU - Issac, Biju

AU - Zhang, Li

N1 - This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

PY - 2018/3/15

Y1 - 2018/3/15

N2 - Automatic facial expression recognition is widely used for various applications such as health care, surveillance and human-robot interaction. In this paper, we present a novel system which employs automatic facial emotion recognition technique for adaptive AI agent behaviour. The proposed system is equipped with kirsch operator based local binary patterns for feature extraction and diverse classifiers for emotion recognition. First, we nominate a novel variant of the local binary pattern (LBP) for feature extraction to deal with illumination changes, scaling and rotation variations. The features extracted are then used as input to the classifier for recognizing seven emotions. The detected emotion is then used to enhance the behaviour selection of the artificial intelligence (AI) agents in a shooter game. The proposed system is evaluated with multiple facial expression datasets and outperformed other state-of-the-art models by a significant margin.

AB - Automatic facial expression recognition is widely used for various applications such as health care, surveillance and human-robot interaction. In this paper, we present a novel system which employs automatic facial emotion recognition technique for adaptive AI agent behaviour. The proposed system is equipped with kirsch operator based local binary patterns for feature extraction and diverse classifiers for emotion recognition. First, we nominate a novel variant of the local binary pattern (LBP) for feature extraction to deal with illumination changes, scaling and rotation variations. The features extracted are then used as input to the classifier for recognizing seven emotions. The detected emotion is then used to enhance the behaviour selection of the artificial intelligence (AI) agents in a shooter game. The proposed system is evaluated with multiple facial expression datasets and outperformed other state-of-the-art models by a significant margin.

M3 - Paper

T2 - International Joint Conference on Neural Networks (IJCNN) under IEEE World Congress on Computational Intelligence (WCCI 2018)

Y2 - 8 July 2018 through 13 July 2018

ER -

Extended LBP based Facial Expression Recognition System for Adaptive AI Agent Behaviour

Abstract

Conference

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