Prediction of Human Introspection in Facial Expressions through Emotional Intelligence and Machine Learning Techniques

Abstract

Human introspection in facial expression is the process through which people analyze and analyze their own emotions and feelings through the observation and interpretation of their facial expressions. The basic concept behind this idea is that people can learn about their thoughts and feelings by observing the expressions on their faces, which can act as a window into their emotional state. This study analyses the methods by which machine learning for facial expression interpretation is influenced by emotional intelligence. Its goal is to increase our comprehension of how emotional intelligence contributes to precise and thorough facial expression analysis. Our proposed work investigates the efficacy of a Convolutional ReLU Bidirectional Long-Term Neural Network (CRBLNN) for facial expression detection, utilizing an image dataset sourced from Kaggle. Building upon the foundation of machine learning and emotional intelligence, our approach employs a comprehensive multi-step strategy, integrating pre-processing, feature extraction, and feature selection techniques. We present a new approach to facial expression recognition that considers emotional intelligence. Initially, we apply histogram equalization as a pre-processing step to improve the overall quality and contrast of the images. Next, we extract features using Local Binary Pattern (LBP) to identify important patterns and textures in the facial expressions. Finally, we select the most informative features out of the enriched feature set using Select K-best feature selection. Our proposed CRBLNN architecture combines bidirectional Long-Short Term Memory (LSTM) units and convolutional layers with Rectified Linear Unit (ReLU) activation. The model can learn both temporal and spatial dependencies from the facial expression data due to this strong framework that the design offers. Beyond the technical aspects, we also explore the domain of emotional intelligence, recognizing its significance in the interpretation of facial expressions. The model's performance is thoroughly examined through the use of common metrics like F1-score, accuracy, precision, and recall. The results demonstrate how well the model can recognize and interpret facial expressions, highlighting the potential for machine learning and emotional intelligence to work together to improve facial expression recognition systems.