Face Recognition Using Deep Learning

Abstract

Introduction

Affective computing is a multidisciplinary field focused on developing systems that recognize and interpret human emotions to improve human-machine interactions. Facial expressions are critical for conveying emotions nonverbally, and machine learning—especially deep learning techniques like Convolutional Neural Networks (CNNs)—has significantly advanced the analysis of these expressions.

Several large, publicly available facial emotion datasets support the training and evaluation of FER systems, including FER2013, CK+, JAFFE, AffectNet, EmotioNet, RAF-DB, and Oulu-CASIA. These datasets vary in size, diversity, annotation quality, and complexity, with some providing detailed facial action unit annotations.

Traditional facial recognition methods like PCA-based Eigenfaces and Fisherfaces faced challenges with lighting and expression variability. Deep learning models such as DeepFace and FaceNet revolutionized the field by achieving near-human accuracy and efficient face embeddings, though generalizing across diverse datasets remains a challenge.

Modern FER implementations rely heavily on CNNs, which automatically learn hierarchical features from images, enabling more accurate and subtle emotion recognition. Training these models requires large, diverse, and well-annotated datasets to ensure robustness and minimize biases across different demographics.

Conclusion

Inthisstudy,weexaminedtheapplicationof deep learning methods, particularly Convolutional Neural Networks (CNNs), along with the Haar Cascade algorithm for recognizingfacialemotions(FER).Through theintegrationofthesetechnologies,we demonstrated that emotion recognition systems can achieve high accuracy and efficiency, especially when combined with the robust face detection capabilities provided by Haar Cascade. The research highlights the growing potential of deep learning in enhancing FER systems and opensupavenuesforfutureadvancementsin the field.

References

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Copyright

Copyright © 2025 Vivek Kumar, Ritik Raj, Dr. Suveg Moudgil. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.