The week's pick
Random Articles
Reseach Article
Human Emotion Classification using Facial Expressions and CNN Models
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 14 |
| Year of Publication: 2025 |
| Authors: Alishana Thorat, Kanishka Panpatil, Selvavani Mathavan, Sneha Kushwaha, Savita Sangam |
10.5120/ijca2025925115
|
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 ( Jun 2025), 22-26. DOI=10.5120/ijca2025925115
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
- F Ghaffar, 'Facial Emotions Recognition using Convolutional Neural Net' arXiv preprint arXiv:2001.01456, Jan. 2020. [Online].
- A. V Savchenko, 'Facial expression and attributes recognition based on multi-task learning of lightweight neural networks' arXiv preprint arXiv: 2103.17107, Mar.2021. [Online]
- A Saroop, S. K. S Gupta, S. K. S Gupta, 'Facial Emotion Recognition: A multi-task approach using deep learning' arXiv preprint arXiv: 2110.15028, Oct 2021. [Online].
- A. Chaudhari, A. Agrawal and S Joshi ‘ViTFER: Facial Emotion Recognition with Vision Transformers’, Journal of Imaging 5 (4) (2022) 80. [Online]
- S Kaur and N Kulkarni, ‘FERFM: An Enhanced Facial Emotion Recognition System Using Fine Tuned MobileNetV2 Architecture’, IETE Journal of Research 10 (2023) 415-418. [Online]
- R Zhang, ‘Facial emotion detection based on improved VGG-16’, Applied and Computational Engineering 2 (3) (2023) 45-50. [Online].
- A R Angeline, A N Alice, ‘Multimodal Human Facial Emotion Recognition Using DenseNet-161 and Image Feature Stabilization Algorithm’ Traitement du Signal 39 (6) (2022)
- S Nathani, ‘A Comparative Study of Transfer Learning for Emotion Recognition using CNN and Modified VGG16 Models’ arXiv preprint
- 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.
- Doe, J., & Smith, J. (2023, March). Deep emotion recognition: A comprehensive review of current approaches and future directions. Journal
- Davis, E., & Johnson, M. (2024, June). Real-time facial emotion recognition using lightweight CNNs. Proceedings of the IEEE.
Index Terms
Keywords