In this study, the Meypaatiyal dataset forms a basis for the Tamil emotion classification system, capturing authentic emotional expressions from drama and theatre situations. Previous face emotion identification research has been based on shared emotions and has generally overlooked the subtlety and richness of cultural variation in the emotional spectrum, such as the eight primary emotions described in "Tolkappiyam." Other limitations of the traditional approaches include the failure of existing approaches to properly account for the diversity and complexity of such emotions as nagai, azhugai, ilivaral, marutkai, and many others. In this study, the limitations have been overcome and a robust Local Artificial Attention-based Convolutional Neural Network-Hybrid Seagull and Grasshopper Optimization Algorithms model is proposed for Tamil emotion classification. Initially, the Meypaatiyal dataset with 948 images of facial expressions and a preprocessing process of resizing using KNN-RNN and data is normalized using Z-score normalization for improved model performance. Techniques for augmenting data, such as flipping, scaling, translation, and rotation, are employed. While Local Binary Patterns and Artificial Neural Networks are applied for optimizing the extraction of features, Hybrid Seagull and Grasshopper Optimization Algorithms are used for feature selection. These extracted features classify focus-attention-based convolutional neural networks, focusing on areas and condensing it to obtain accurate predictions in emotions. Lastly, the performance metrics used in this study revolve around F-measure, NPV, FPR, FNR, accuracy, precision, sensitivity, and specificity, where the achieved accuracy is 99.23%.
Joseph et al. (Thu,) studied this question.