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An artificial intelligence driven facial emotion recognition system using hybrid deep belief rain optimization

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Facial expression recognition is a process of identifying the different facial expressions of the individuals to categorize the mental health of the individual. This system is used in most of the fields but is vastly used in the medical field to identify the mental health issues. In this paper, a novel approach has been proposed to identify the facial expressions of the individuals and categorizing it into seven different emotions. Initially, the images collected from the dataset are subjected to pre-processing for de-noising. Then, the major geometric and appearance-based features are extracted from the images. The most relevant features are selected from the extracted feature set. Finally, based on the selected features, the classification is performed where the input images get labelled into seven different emotions. The classification is carried out with the use of the hybrid strategy called the Deep Belief Rain Optimization (DBRO) technique. The efficiency of the proposed model is proved through the simulations and it is identified to outperform the other existing approaches.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.


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Correspondence to Fakir Mashuque Alamgir.

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Authors Fakir Mashuque Alamgir, Md. Shafiul Alam declares that they have no conflict of interest.

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Alamgir, F.M., Alam, M.S. An artificial intelligence driven facial emotion recognition system using hybrid deep belief rain optimization. Multimed Tools Appl 82, 2437–2464 (2023).

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