Divergence Shepherd Feature Optimization-Based Stochastic-Tuned Deep Multilayer Perceptron for Emotional Footprint Identification

Emotional Footprint Identification refers to the process of recognizing or understanding the emotional impact that a person, experience, or interaction leaves on others. Emotion Recognition plays an important role in human–computer interaction for identifying emotions such as fear, sadness, anger, happiness, and surprise on the human face during the conversation. However, accurate emotional footprint identification plays a crucial role due to the dynamic changes. Conventional deep learning techniques integrate advanced technologies for emotional footprint identification, but challenges in accurately detecting emotions in minimal time. To address these challenges, a novel Divergence Shepherd Feature Optimization-based Stochastic-Tuned Deep Multilayer Perceptron (DSFO-STDMP) is proposed. The proposed DSFO-STDMP model consists of three distinct processes namely data acquisition, feature selection or reduction, and classification. First, the data acquisition phase collects a number of conversation data samples from a dataset to train the model. These conversation samples are given to the Sokal–Sneath Divergence shuffling shepherd optimization to select more important features and remove the others. This optimization process accurately performs the feature reduction process to minimize the emotional footprint identification time. Once the features are selected, classification is carried out using the Rosenthal correlative stochastic-tuned deep multilayer perceptron classifier, which analyzes the correlation score between data samples. Based on this analysis, the system successfully classifies different emotions footprints during the conversations. In the fine-tuning phase, the stochastic gradient method is applied to adjust the weights between layers of deep learning architecture for minimizing errors and improving the model’s accuracy. Experimental evaluations are conducted using various performance metrics, including accuracy, precision, recall, F1 score, and emotional footprint identification time. The quantitative results reveal enhancement in the 95% accuracy, 93% precision, 97% recall and 97% F1 score. Additionally, the DSFO-STDMP minimized the in training time by 35% when compared to traditional techniques.