Automated compound facial emotion recognition using hybrid deep learning model and DCHBO

To create a system that effectively recognizes and categorizes human emotions from facial expressions using deep learning-based automated compound facial emotion detection strategy.

A novel hybrid model based on deep learning combines the optimized bi-directional long short-term memory (O-Bi-LSTM) and deep neural decision forests (DNDF). The O-Bi-LSTM and DNDF are trained using the extracted features and the weight function of the BI-LSTM is fine-tuned using the new Dingo customized Honey Badger optimization algorithm (DCHBO) to further enhance the detection accuracy of the proposed model for automatically differentiating compound face emotions.

O-Bi-LSTM is concatenated, and the final detected outcome of the proposed model has been evaluated over the existing models.

The superiority of the proposed model is demonstrated in detecting and classifying compound facial emotions accurately.