Facial Emotion Recognition for Visually Impaired People using Transfer Learning

Abstract

Individuals with visual impairment often face chal- lenges in social interactions, specifically at recognizing emotional cues. The proposed framework tackles this issue head-on by de- vising a Facial Emotion Recognition(FER) system, by employing an advanced Transfer Learning approach within Convolutional Neural Networks (CNNs). By leveraging the dataset FER-2013 [13], the proposed system aims to transcend the limitationsof traditional emotion recognition methods. Transfer learningallows the model to benefit from pre-trained knowledge on vast datasets, making it more efficient and effective in capturing complex facial features associated with different emotions. This approach is designed to offer better accuracy and generalization capabilities than other conventional methods. During training, the system will be designed to comprehensively capture the intricacies of facial expressions, enabling it to not only identify individuals but also interpret subtle changes in their emotional states throughout conversations. An innovative audio output system will be integrated into the FER system to provide a smoothand accessible experience for visually impaired users, allowing for a better understanding of social dynamics. By emphasizing transfer learning, this framework is designed to be efficient and robust, potentially revolutionizing emotional understanding for visually impaired individuals and setting a new standard in the field by showcasing the superior performance achievable throughadvanced machine learning techniques. Ultimately, this research aims to bridge the social gap for the visually impaired by fosteringinclusivity, independence, and safety in their daily life.