Real Time Emotion Recognition and Classification for Diverse Suggestions using Deep Learning

Abstract

At the captivating nexus of technology and human emotions, we harness cutting-edge software to discern individuals' feelings through their facial expressions. This unique capability empowers us to provide customized recommendations for various forms of entertainment and enrichment, such as movies, music, books, and meditation practices. For instance, if a user appears sad, we can offer upbeat music to brighten their mood. Our ultimate objective is to enhance technology comprehension of emotions, enabling it to suggest content that resonates with people's emotional states. Through this integration of facial expressions and diversified suggestions, here aim is to cultivate a more user-friendly and supportive digital environment, fostering feelings of happiness and calmness. Exploring the captivating domain of facial emotion recognition and recommendation systems is the central emphasis of this endeavor. The primary ambition is to construct a framework capable of deciphering user emotions from facial cues. By using deep learning techniques such as the CNN algorithm which can be applied to facial images to process emotion recognition feature vectors with the categorization of individualized content. It attempts to form a balanced blend of advanced tech and boost user engagement, nurturing emotional connections in the digital domain.