Overview

Determining human emotions from photographs is a difficult but important challenge for social communication. Emotion detection using traditional approaches is typically inefficient and inaccurate. In this study, we investigate how convolutional neural networks (CNNs), a type of deep learning technique, may improve the ability to identify emotions from facial expressions. In order to increase CNN efficacy, we test several preprocessing methods and refine CNN designs to identify eight fundamental emotions. Our goal is to improve human emotion recognition and classification through deep learning, so that computers can react to human emotions and behaviors more precisely. The research dataset consists of roughly 32,290 photos with various expressions on their faces. Our approach includes preprocessing processes like feature extraction and noise reduction to improve image quality. To reliably classify facial expressions, we present an enhanced CNN (ECNN) technique that is in line with the Facial Action Coding System (FACS). We test our ECNN model empirically and compare its performance to that of conventional CNNs and support vector machines (SVMs). The results show that our ECNN methodology achieves higher accuracy rates in emotion categorization than previous methods. We show notable gains in computing efficiency and classification performance by utilizing deep learning techniques. Our research advances face expression recognition technology, which has ramifications for a number of fields including social robots, affective computing, and human-computer interaction.

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