Facising Deep Leal Emotion Detection with Age and Gender Recognition Uarning

DOI

10.2991/978-94-6463-866-0_73How to use a DOI?

Keywords

Facial Emotion Recognition; Deep Learning; Convolutional Neural Networks; Age Prediction; Gender Recognition; Real-Time Emotion Detection

Abstract

Facial emotion detection has emerged as a critical area in human-computer interaction, healthcare, and security applications. Traditional methods often relied on handcrafted features and single-label classification, which lacked depth and flexibility. This paper proposes a deep learning-based model utilizing Convolutional Neural Networks (CNNs) for facial emotion detection along with age and gender prediction. Our approach addresses mixed emotion recognition by providing a percentage-based emotional distribution, enhancing the understanding of complex facial expressions. Real-time performance is achieved through optimized model architecture and data augmentation techniques. The system demonstrates superior accuracy, offering significant advancements for real-world implementations.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - C. A. Ashwath Amudhan
AU  - G. Ajay
AU  - S. Ramachanthar
AU  - M. Indumathy
PY  - 2025
DA  - 2025/10/31
TI  - Facising Deep Leal Emotion Detection with Age and Gender Recognition Uarning
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 901
EP  - 912
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_73
DO  - 10.2991/978-94-6463-866-0_73
ID  - Amudhan2025
ER  -