Current - Issue
Thesis
Facial Emotion Recognition from Video Streams Using Deep Learning Techniques
Rubina S Pathan1
Dr.Aslam J Karjagi2
1 PG Scholar, Department of Computer Science and Engineering, Secab Institute of Engineering and Technology, Vijayapura, Karnataka, India. 2 Associate.Professor, Department of Computer Science and Engineering, Secab Institute of Engineering and Technology, Vijayapura, Karnataka, India.
Published Online: May-August 2026
Pages: 514-522
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502057
References
1) I. J. Goodfellow et al., “Challenges in representation learning: A report on three machine learning contests,” Neural Networks, vol. 64, pp.
59–63, 2013.
2) P. Ekman and W. V. Friesen, Facial Action Coding System. Palo Alto, CA: Consulting Psychologists Press, 1978.
3) R. W. Picard, Affective Computing. MIT Press, 2000.
4) Y. Li et al., “Occlusion-aware facial expression recognition using CNN with attention mechanism,” IEEE Trans. Image Processing, vol. 28,
no. 5, pp. 2439–2450, 2018.
5) K. Zhang et al., “Joint face detection and alignment using multitask cascaded convolutional networks,” IEEE Signal Processing Letters, vol.
23, no. 10, pp. 1499–1503, 2016.
6) K. He et al., “Deep residual learning for image recognition,” in Proc. IEEE CVPR, pp. 770–778, 2016.
7) P. Lucey et al., “The extended Cohn–Kanade dataset (CK+),” in Proc. IEEE CVPR Workshops, pp. 94–101, 2010.
8) O. Arriaga et al., “Real-time convolutional neural networks for emotion and gender classification,” arXiv preprint arXiv:1710.07557,2017.
9) C. Pramerdorfer and M. Kampel, “Facial expression recognition using convolutional neural networks,” arXiv preprint arXiv:1612.02903,
2016.
10) A. Mollahosseini et al., “AffectNet: A database for facial expression,” IEEE Trans. Affective Computing, 2017.
11) L. Pham et al., “Residual masking network,” in Proc. ICPR, 2021.
12) F. Xue et al., “Relation-aware facial expression recognition,” in Proc. ICCV, 2021.
13) Z. Zhang et al., “Interpersonal relation prediction,” IJCV, 2020.
14) K. Wang et al., “Suppressing uncertainties,” in Proc. CVPR, 2021.
15) T. Bui et al., “FER with noisy annotations,” IEEE Trans. Affective Computing, 2022.
16) F. Schroff et al., “FaceNet,” in Proc. CVPR, 2015.
17) X. Li et al., “Few-shot FER,” in Proc. AAAI, 2022.
18) J. Deng et al., “ImageNet,” in Proc. CVPR, 2009.
19) L. Chen et al., “Softmax regression for FER,” IEEE Trans. Affective Computing, 2022.
20) R. Selvaraju et al., “Grad-CAM,” in Proc. ICCV, 2017.
21) S. Li et al., “Crowdsourcing FER,” in Proc. CVPR, 2017.
22) A. Mollahosseini et al., “AffectNet extended,” IEEE Trans. Affective Computing, 2019.
23) J. Russell, “Circumplex model of affect,” J. Personality, 1980.
24) L. Barrett et al., “Emotion perception,” Psychological Science, 2011.
25) A. Paszke et al., “PyTorch,” NeurIPS, 2019.
26) G. Bradski, “OpenCV,” Dr. Dobb’s Journal, 2000.
27) M. Grinberg, Flask Web Development. 2018.
28) D. Merkel, “Docker,” Linux Journal, 2014.
29) J. Grafsgaard et al., “Emotion recognition in education,” 2014.
30) McDuff et al., “AM-FED dataset,”2013.
59–63, 2013.
2) P. Ekman and W. V. Friesen, Facial Action Coding System. Palo Alto, CA: Consulting Psychologists Press, 1978.
3) R. W. Picard, Affective Computing. MIT Press, 2000.
4) Y. Li et al., “Occlusion-aware facial expression recognition using CNN with attention mechanism,” IEEE Trans. Image Processing, vol. 28,
no. 5, pp. 2439–2450, 2018.
5) K. Zhang et al., “Joint face detection and alignment using multitask cascaded convolutional networks,” IEEE Signal Processing Letters, vol.
23, no. 10, pp. 1499–1503, 2016.
6) K. He et al., “Deep residual learning for image recognition,” in Proc. IEEE CVPR, pp. 770–778, 2016.
7) P. Lucey et al., “The extended Cohn–Kanade dataset (CK+),” in Proc. IEEE CVPR Workshops, pp. 94–101, 2010.
8) O. Arriaga et al., “Real-time convolutional neural networks for emotion and gender classification,” arXiv preprint arXiv:1710.07557,2017.
9) C. Pramerdorfer and M. Kampel, “Facial expression recognition using convolutional neural networks,” arXiv preprint arXiv:1612.02903,
2016.
10) A. Mollahosseini et al., “AffectNet: A database for facial expression,” IEEE Trans. Affective Computing, 2017.
11) L. Pham et al., “Residual masking network,” in Proc. ICPR, 2021.
12) F. Xue et al., “Relation-aware facial expression recognition,” in Proc. ICCV, 2021.
13) Z. Zhang et al., “Interpersonal relation prediction,” IJCV, 2020.
14) K. Wang et al., “Suppressing uncertainties,” in Proc. CVPR, 2021.
15) T. Bui et al., “FER with noisy annotations,” IEEE Trans. Affective Computing, 2022.
16) F. Schroff et al., “FaceNet,” in Proc. CVPR, 2015.
17) X. Li et al., “Few-shot FER,” in Proc. AAAI, 2022.
18) J. Deng et al., “ImageNet,” in Proc. CVPR, 2009.
19) L. Chen et al., “Softmax regression for FER,” IEEE Trans. Affective Computing, 2022.
20) R. Selvaraju et al., “Grad-CAM,” in Proc. ICCV, 2017.
21) S. Li et al., “Crowdsourcing FER,” in Proc. CVPR, 2017.
22) A. Mollahosseini et al., “AffectNet extended,” IEEE Trans. Affective Computing, 2019.
23) J. Russell, “Circumplex model of affect,” J. Personality, 1980.
24) L. Barrett et al., “Emotion perception,” Psychological Science, 2011.
25) A. Paszke et al., “PyTorch,” NeurIPS, 2019.
26) G. Bradski, “OpenCV,” Dr. Dobb’s Journal, 2000.
27) M. Grinberg, Flask Web Development. 2018.
28) D. Merkel, “Docker,” Linux Journal, 2014.
29) J. Grafsgaard et al., “Emotion recognition in education,” 2014.
30) McDuff et al., “AM-FED dataset,”2013.
Related Articles
2026
Artificial Intelligence in Learning and Teaching
2026
Admin Assist: An AI – Driven Configuration and Orchestration for Enterprise Application
2026
Enhancing Blood Group Identification using pigeon inspired optimization: An Innovative Approach
2026
Eco-Genius: Power Up Smart, Power Down Waste
2026
Crowd-Sourced Disaster Response and Rescue Assistant
2026
