Research Article
Human Emotion Classification Using Facial Expressions and CNN Models
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 187 - Issue 14 |
| Published: June 2025 |
| Authors: Alishana Thorat, Kanishka Panpatil, Selvavani Mathavan, Sneha Kushwaha, Savita Sangam |
10.5120/ijca2025925115
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Alishana Thorat, Kanishka Panpatil, Selvavani Mathavan, Sneha Kushwaha, Savita Sangam . Human Emotion Classification Using Facial Expressions and CNN Models. International Journal of Computer Applications. 187, 14 (June 2025), 22-26. DOI=10.5120/ijca2025925115
@article{ 10.5120/ijca2025925115,
author = { Alishana Thorat,Kanishka Panpatil,Selvavani Mathavan,Sneha Kushwaha,Savita Sangam },
title = { Human Emotion Classification Using Facial Expressions and CNN Models },
journal = { International Journal of Computer Applications },
year = { 2025 },
volume = { 187 },
number = { 14 },
pages = { 22-26 },
doi = { 10.5120/ijca2025925115 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2025
%A Alishana Thorat
%A Kanishka Panpatil
%A Selvavani Mathavan
%A Sneha Kushwaha
%A Savita Sangam
%T Human Emotion Classification Using Facial Expressions and CNN Models%T
%J International Journal of Computer Applications
%V 187
%N 14
%P 22-26
%R 10.5120/ijca2025925115
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This project aims to teach machines how to recognize human emotions by analysing facial expressions. Using deep learning and the pre-trained VGG16 model, our system identifies six key emotions: happiness, sadness, anger, fear, surprise, and disgust. This system applies transfer learning, data augmentation, and class balancing to improve accuracy and performance. The result is a reliable emotion detection model that can support real-world applications like mental health monitoring, smart assistants, and interactive learning tools.
References
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- El Boudouris, Y., & Bohi, A. (2025, January). EmoNeXt: An adapted ConvNeXt for facial emotion recognition. arXiv preprint arXiv:2501.08199. [Online]. Available: arXiv: 2501.08199.
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