International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Image Based Emotion Recognition using Deep Neural Network |
|---|---|
| ABSTRACT | Image-based emotion recognition using deep neural networks is important for understanding human emotions and improving human-computer interaction in areas like healthcare, education, and security. This project uses a Convolutional Neural Network (CNN) to automatically extract facial features and accurately classify emotions such as happiness, sadness, and anger. The system reduces the limitations of traditional methods by eliminating manual feature extraction and improving performance under different conditions. Compared to earlier techniques, this approach provides higher accuracy, better adaptability, and supports real-time emotion detection, making it effective for real-world applications. |
| AUTHOR | P.GAYATHRI, K.PRASAD, CH.SAI KEERTHI, R.SAMBI REDDY, Y.R.K.PARAMAHAMSA U.G. Student, Department of ECE, SVIET Engineering College, Nandamuru, Pedana, Andhra Pradesh, India Assistant Professor, Department of ECE, SVIET Engineering College, Nandamuru, Pedana, Andhra Pradesh, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1403128 |
| pdf/128_Image Based Emotion Recognition using Deep Neural Network.pdf | |
| KEYWORDS | |
| References | 1) P. Ekman and W. V. Friesen, “Facial Action Coding System: A Technique for the Measurement of Facial Movement,” 1978. 2) Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016. 3) Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems, 2012. 4) Mollahosseini, A., Hasani, B., and Mahoor, M. H., “AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild,” IEEE Transactions on Affective Computing, 2017. 5) S. Li and W. Deng, “Deep Facial Expression Recognition: A Survey,” IEEE Transactions on Affective Computing, 2020. 6) F. Happy and R. Routray, “Automatic Facial Expression Recognition Using Features of Salient Facial Patches,” IEEE Transactions on Affective Computing, 2015. |
