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Ameliorate grasshopper optimization algorithm based long short term memory classification for face emotion recognition system

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Abstract

Face Emotion Recognition (FER) has become a vital need for human interaction with machines in the virtual world. In virtual classroom, the emotional states of the students are necessary for better learning and communication. In this work, Ameliorate Grasshopper Optimization Algorithm (AGOA) based Long Short Term Memory (LSTM) classification for Face emotion recognition system is presented. AGOA is proposed by enhancing the conventional Grasshopper Optimization Algorithm (GOA) with opposition-based learning, levy flight mechanism and Gaussian mutation for the selection of optimized features. Convolution Neural Network (CNN) based feature extraction is adopted and the LSTM unit classifies the basic human emotions such as fear, happy, disgust, anger, sad, surprise and normal. The exploratory work is done using the YALE face database and it resulted in 93.90% recognition accuracy. The results attained shows that the performance measures such as precision, recall, specificity, F-Measure, sensitivity and Area under Curve (AUC) are higher in AGOA-LSTM based system rather than the system without AGOA and it also resulted in reduced error rate.

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Authors and Affiliations

  1. Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, 500075, India

    Sumalakshmi CH

  2. Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India

    Vasuki P

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  1. Sumalakshmi CH
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  2. Vasuki P
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Correspondence to Sumalakshmi CH.

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CH, S., P, V. Ameliorate grasshopper optimization algorithm based long short term memory classification for face emotion recognition system. Multimed Tools Appl 83, 37961–37978 (2024). https://doi.org/10.1007/s11042-023-16837-1

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  • DOI: https://doi.org/10.1007/s11042-023-16837-1

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