MoodScope: Navigating Emotions through Convolutional Neural Networks

Abstract

Facial expressions play a crucial role in human communication, presenting a longstanding challenge for emotion recognition. Leveraging advancements in technology, this paper explores the feasibility of detecting emotions in facial images. We introduce a Deep Learning model based on Convolutional Neural Networks (CNNs) to recognize facial emotions. The proposed method involves three key steps: Face Detection, Feature Extraction, and Emotion Classification, aiming to identify seven cardinal human emotions—Anger, Fear, Disgust, Joy, Neutrality, Sadness, and Surprise.

The use of Convolutional Neural Networks is pivotal in achieving high accuracy and performance in Facial Emotion Recognition. This technology holds wide applicability in various domains, including student predictive learning, forensics, and social media platforms. The anticipated impact of Facial Emotion Recognition extends to enhancing communication and understanding emotional nuances in diverse applications.