Gender Influence on Emotional Recognition using Facial Expressions and Voice

Emotions are a complex and powerful force that can have a profound impact on our thoughts, behaviors, and interactions with others. They are essential for our social functioning and play a critical role in our understanding of the world around us. This study introduces two emotion recognition methods, combining facial expressions and voice tone via deep neural networks (DNNs). Predicting emotions includes happiness, sadness, neutral, anger, disgust and fear. The main goal of the study was to investigate the effect of gender on facial expressions and voice tone in emotion recognition. One approach involves training separate models using genderspecific datasets for males and females. The second approach entails training models disregarding gender-based differentiation. This comparative analysis enables a comprehensive evaluation of gender's role in emotion recognition. The research utilizes two distinct architectural approaches, namely Convolutional Neural Networks (CNNs) and ResNet, to perform a gender comparison using different model techniques. The results of the study show that gender has a significant impact on both facial expressions and tone of voice highlighting the importance of considering gender as a factor in the development of emotion recognition systems for improved accuracy and performance.