Enhancing Facial Emotion Recognition with a Modified Deep Convolutional Neural Network

Authors

Samah Gamel, Hatem Khater

Abstract

Understanding and predicting human character traits play a crucial role in various domains ranging from psychology to human resources. With the advent of artificial intelligence (AI) and deep learning algorithms, researchers have explored the potential of analyzing facial images to predict human character traits accurately. In this paper, we present a comprehensive study of the application of AI techniques for human character recognition. We review the existing literature on facial image analysis, AI algorithms, and personality prediction. Furthermore, we propose a methodology that leverages deep learning and convolutional neural networks (CNNs) to extract meaningful features from facial images. Our experiments demonstrate the effectiveness of our approach in accurately predicting character traits and showcasing promising results using small-scale datasets. We discuss the implications of our findings in psychology, human resources, and personalized user experiences. Additionally, ethical considerations, such as privacy and bias, are addressed. This research contributes to the growing field of AI-driven character recognition, providing insights for further advancements and practical applications in understanding human behavior

Recommended Citation

Gamel, Hatem Khater, Samah (2023) "Enhancing Facial Emotion Recognition with a Modified Deep Convolutional Neural Network," Journal of Engineering Research: Vol. 7: Iss. 5, Article 47.
Available at: https://digitalcommons.aaru.edu.jo/erjeng/vol7/iss5/47