Emotion based mental retardation recognition framework (EMRRF) using HPSO-ANN technique

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Linking the human emotion with the reduced brain skill of that particular person becomes the social competent concept. Various research methods has been conducted to predict the mental retardation of people which concludes that the human emotions can be used to predict them successfully. It is due to uncontrollable emotional states of mentally retarded people comparatively than the normal mental age people. The mentally retarded people cannot control their facial emotions which is more difficult to decode. Finding stable emotions of people can be used to predict the solutions of requirements. There are no research work has been available to accurately predict the emotional behaviour of humans. The main goal of this research work is to introduce the system to predict the varying emotional state of people accurately. This is attained by introducing the new framework namely Emotion based Mental Retardation Recognition Framework (EMRRF) which can recognize the different kind of emotions. In this work, input videos are preprocessed first to differentiate the required object from the noisy pixels and background portions. After preprocessing, feature extraction is performed to predict the emotions where the extracted features are color, texture and shape features. The extracted features are learned by applying the Hybridized Particle Swarm Optimization and Artificial Neural Network (HPSO-ANN) to ensure the accurate prediction of required object emotional state present in video. The overall experimentation of the research work is done in the matlab simulation environment from which it is proved that the proposed research method leads to better result than the existing research works.

     

     


  • Keywords


    Background Subtraction; Emotion Recognition; Feature Extraction; Mental Retardation; Video Processing.

  • References


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Article ID: 20358
 
DOI: 10.14419/ijet.v7i4.20358




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