Paper Title
Facial Emotion Detection Model

Abstract
In the real world of sentiment analysis, the profound influence of emotions on details filtering, viewpoint evolution, and conclusion making is well-established. Despite recent advancements in Facial Expression Recognition (FER), building dependable and powerful FER systems remains a formidable challenge, largely attributed to the inherent diversity of people's altering faces and image fluctuations. Until now, existing research has predominantly recommended either singlenetwork models or group models. While ensemble models exhibit higher accuracy, they come with the baggage of multiple models, datasets, and occasional data tweaking, elevating computational complexity. In contrast to the prevailing focus on accuracy enhancement, this study takes a novel approach by applying the proposed model to real-world scenarios where individuals may exhibit mixed emotions, rendering single-label sentiment classification noisy and inadequate. In response to this complex scenario, we conducted an exhaustive evaluation of 20-25 imitation and procedures. This paper introduces a groundbreaking freestanding CNN model, seamlessly integrated into an instantaneous exceptional system for feeling recognition. This system encompasses various functionalities, including face observation, feeling categorization, and the generation of a few lists of expected labels from a webcam feed in a single streamlined process. Through the utilization of transfer learning, the recommended model attains an impressive perfection rate of 78.62%, surpassing all separate models like VGG16, VGG19, EfficientNetB7, and other recommended models, solely on the notoriously rigorous and rowdy FER2013 database, without any reliance on supplementary databases. This innovative approach not only advances the field of emotion recognition but also holds promise for practical applications, such as human-computer interaction and user experience enhancement in real-world contexts where emotions are complex and multifaceted. Keywords: Facial Expression Recognition (FER), Sentiment Analysis, Deep Learning, CNN.