Modified Earthworm Optimization With Deep Learning Assisted Emotion Recognition for Human Computer Interface

doi:10.1109/ACCESS.2023.3264260

Modified Earthworm Optimization With Deep Learning Assisted Emotion Recognition for Human Computer Interface

2023-01-01

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DOI：10.1109/ACCESS.2023.3264260

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Among the most prominent field in the human-computer interface (HCI) is emotion recognition using facial expressions. Posed variations, facial accessories, and non-uniform illuminations are some of the difficulties in the emotion recognition field. Emotion detection with the help of traditional methods has the shortcoming of mutual optimization of feature extraction and classification. Computer vision (CV) technology improves HCI by visualizing the natural world in a digital platform like the human brain. In CV technique, advances in machine learning and artificial intelligence result in further enhancements and changes, which ensures an improved and more stable visualization. This study develops a new Modified Earthworm Optimization with Deep Learning Assisted Emotion Recognition (MEWODL-ER) for HCI applications. The presented MEWODL-ER technique intends to categorize different kinds of emotions that exist in the HCI applications. To do so, the presented MEWODL-ER technique employs the GoogleNet model to extract feature vectors and the hyperparameter tuning process is performed via the MEWO algorithm. The design of automated hyperparameter adjustment using the MEWO algorithm helps in attaining an improved emotion recognition process. Finally, the quantum autoencoder (QAE) model is implemented for the identification and classification of emotions related to the HCI applications. To exhibit the enhanced recognition results of the MEWODL-ER approach, a wide-ranging simulation analysis is performed. The experimental values indicated that the MEWODL-ER technique accomplishes promising performance over other models with maximum accuracy of 98.91%.

Keywords:

Feature extraction

Human computer interaction

Emotion recognition

Brain modeling

Convolutional neural networks

Computational modeling

Deep learning

Human-computer interaction

artificial intelligence

deep learning

emotion recognition

earthworm optimization algorithm

Modified Earthworm Optimization With Deep Learning Assisted Emotion Recognition for Human Computer Interface

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