Emotion Recognition From Facial Expressions and Its Control Using Fuzzy Logic

Chakraborty, Aruna; Konar, Amit; Chakraborty, Uday K.; Chatterjee, Arpitam

doi:10.1109/tsmca.2009.2014645

Cited by 131 publications

(45 citation statements)

References 30 publications

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“…In 2015, Leo Pauly and Deepa Sankar presented a new system of recommendation based on facial recognition and sensation detection. Chakraborty et al [10] used fuzzy relational approach to recognize human emotions from facial expressions. Three different fuzzy sets are used: HIGH, LOW, MODERATE using only three facial features eye opening, mouth opening and the length of eyebrow constriction and recognized six basic emotions with the accuracy of 89.11% for adult males, 92.4% adult females and 96.28% for 8-12 years children.…”

Section: Methodsmentioning

confidence: 99%

Facial emotion recognition techniques: a review

Saraswat¹,

Pandey²,

Butola³

2017

Int J Recent Sci Res

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Section: Methodsmentioning

confidence: 99%

Facial emotion recognition techniques: a review

Saraswat¹,

Pandey²,

Butola³

2017

Int J Recent Sci Res

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“…So, emotional state of an intelligent agent turns to a positive state if triggered by a positive stimulus and to a negative state if triggered by a negative one [22]. In the scenario of this paper the distance between the agent and its enemy (known as Enemy Distance) and the distance between the agent and its goal (known as Goal Distance) are stimuli.…”

Section: Emotion Modelmentioning

confidence: 99%

A Computational Behavior Model for Life-Like Intelligent Agents

Alidoust

Rouhani

2015

Topics in Intelligent Engineering and Informatics

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Abstract. In this paper a novel computational behavior model is proposed which has a simple structure and also includes some of the major affecting parameters to the decision making process such as the agent's emotions, personality, intelligence level and physical situation. The effect of these parameters has been studied and the model has been simulated in a goal-achieving scenario for four agents with different characteristics. Simulation results show that the behavior of these intelligent agents are natural and believable and suggest that this model can be used as the decision making and behavior control unit of future life-like intelligent agents.

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“…He recognizes emotions of user's emotional state that could be robust to facial expression variations among different users. Chakraborty et al [10] uses fuzzy relational approach to recognize human emotions from facial expressions. They uses three different fuzzy sets : HIGH, LOW, MODERATE using only three facial features eye opening, mouth opening and the length of eyebrow constriction and recognized six basic emotions with the accuracy of 89.11% for adult males, 92.4% adult females and 96.28% for 8-12 years children.…”

Section: Related Workmentioning

confidence: 99%