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RCNN Emotion Vision AI Powered Expression Analysis
Authors
1 2 3 4 6 Department of CSA, GIET University Gunupur, Odisha, India. 5 Madanapalle Institute of Technology and Science University, Andhra Pradesh, India.
Article Information
- Organised By
- Madanapalle Institute of Technology & Science (MITS)
- Conference Date
- 9 & 10 March 2026
- Pages
- 113-119
- Article Type
- Research Article
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References
1. K. F. Azizan Illiana, "Facial Emotion Recognition: A Brief Review," in International Conference on Sustainable Engineering, Technology and Management 2018 (ICSETM-2018), 2020.
2. Rath, L., Mishra, S. R., Dash, S., Pradhan, P. C., & Baboo, A. (2025). Predicting diabetic patients coronary artery calcium score, deep learning using retinal images. In Intelligent Computing Techniques and Applications (pp. 113-118). CRC Press.
3. Pattnayak, A. P., Mishra, S. R., Dash, S., & Baboo, A. (2025). Utilization of deep learning and machine learning models to approach high glucose and low glucose prediction with type 1 diabetes mellitus in adult patients. In Intelligent Computing Techniques and Applications (pp. 102-107). CRC Press.
4. Dora, N., Dash, S., Baboo, A., & Mishra, S. R. (2025, August). Efficient Nail Disease Diagnosis Using Deep Neural Networks for Predicting Abnormalities. In 2025 International Conference on Next Generation of Green Information and Emerging Technologies (GIET) (pp. 1-5). IEEE.
5. Mishra, S. R., Dash, S., & Rath, L. (2024, November). Effective Diabetes Mellitus Prediction Using a Hybrid Ensemble Machine Learning Model with Iot. In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1- 8). IEEE.
6. Khuntuli, B., Dash, S., Pradhan, P. C., & Mishra, S. R. Combating food insecurity through remote sensing and machine learning for enhanced crop yield prediction. In Intelligent Computing Techniques and Applications (pp. 135-140). CRC Press.
7. Sahu, P. K., Biswal, B. B., Mishra, S. R., Padhy, J., & Kumar, D. (2025, March). Demand-Based Secured Data Transmission in WSN. In International Conference on Next Generation Computing and Communication Applications (pp. 37-44). Cham: Springer Nature Switzerland.
8. R. Shyam, "Convolutional Neural Network and its Architectures.," Journal of Computer Technology & Applications, vol. 12, no. 2, pp. 6-14, 2021.
9. Mishra, S. R., Dash, S., Padhy, S., Kumar, N., & Dash, Y. (2024, September). Integrating Multi-Omics Data for Advanced Diabetes Prediction and Understanding. In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I) (Vol. 7, pp. 1447-1453). IEEE.
10. Dash, S., Mishra, S. R., & Baboo, A. (2025, January). Enhancing Diabetes Prediction using Hybrid Ensemble Approach. In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN) (pp. 1-6). IEEE.
11. R. Shyam, "Machine Learning and Its Dominant Paradigms," Journal of Advancements in Robotics, vol. 8, no. 2, pp. 1-10, 2021.
12. Dash, S., Mishra, S. R., & Baboo, A. (2025, January). Efficient Prediction of Diabetes Mellitus Through Hybrid Ensemble Machine Learning Model Using IoT. In 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET) (pp. 1- 6). IEEE.
13. Mishra, S. R., & Dash, S. (2024, December). Machine Learning Based Diabetes Prediction Using the PIMA Indian Dataset. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-6). IEEE.
14. M. A. H. Akhand, S. Roy, N. Siddique, M. A. S. Kamal, and T. Shimamura, “Facial emotion recognition using transfer learning in the deep CNN,” Electron., vol. 10, no. 9, 2021, doi: 10.3390/electronics10091036.
A. S. Qazi, M. S. Farooq, F. Rustam, M. G. Villar, C. L. Rodríguez, and I. Ashraf, “Occlusions and tilt in facial recognition systems,” Appl. Sci., vol. 12, no. 1, 2022.
B. Gautam and K. R. Seeja, “Facial emotion recognition using handcrafted features and CNN,” Procedia Comput. Sci., vol. 218, pp. 1295-1303, 2023, doi: 10.1016/j.procs.2023.01.108.
15. X. Tao et al., “Facial video-based non-contact emotion recognition: A multi-view features expression and fusion method,” Biomed. Signal Process. Control, vol. 96, 2024.
16. H. V. Manalu and A. P. Rifai, “Detection of human emotions through facial expressions using hybrid convolutional neural network- recurrent neural network algorithm,” Intell. Syst. Appl., vol. 21, p. 200339, 2024, doi: 10.1016/j.iswa.2024.200339
17. H. Jiang and E. Learned-Miller, “Face detection with the faster R-CNN,” in Proc. 2017 IEEE Winter Conf. Appl. Comput. Vision,pp. 1-6, 2017.
18. R. Shyam, "Automatic Face Recognition in Digital World," Advances in Computer Science and Information Technology (ACSIT), vol. 2, no. 1, pp. 64-70, 2015.
19. Mishra, S. R., Dash, S., Padhy, S., & Das, R. K. (2024, September). Diabetic Foot Ulcer Diagnosis Through Deep Learning Model. In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit) (pp. 1194-1199). IEEE.
20. Mishra, S. R., & Dash, S. (2024). Iterative Model Design for Diabetes Analysis Using FedOmics Causal Network and Federated Multi- Omics Variational Autoencoder. International Journal of Emerging Technologies and Innovative Research, 11(6).
21. S. R. N. S. M. A. H. Akhand, "Facial Emotion Recognition Using Transfer Learning in the Deep CNN," MDPI, vol. 10, no. 9, 2021.
22. Mishra, S. R., & Dash, S. (2024). Predictive Analysis On Diabetes Detection Using Pima Indian Diabetes Dataset. IJRAR-International Journal of Research and Analytical Reviews (IJRAR), 11(2).
23. Baboo, A., Mishra, S. R., & Dash, S. (2024, November). An Improvised Diabetes Prediction System Using Hybrid Ensemble Approach. In 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1-6). IEEE.
24. N. Mehendale, "Facial emotion recognition using convolutional neural networks (FERC)," SN Applied Sciences, vol. 2, no. 3, 2020.
25. Mishra, S. R., & Dash, S. (2026). AI ‐ Driven Remote Health Monitoring for Predicting Diabetes and Heart Diseases Using ULMCSO and PGND Models. Hyper ‐ Intelligent Networks: Exploring the Future of Connectivity for Society 5.0, 219-247.
26. Mishra, S. R., Dash, S., Padhy, S., & Samuel, P. (2026). Legal Aspects of Operating IoMT Applications in the Fog Computing. In Integrating Cloud, Fog, and Edge Computing in Healthcare: Federated Learning and Blockchain Approaches: Harnessing Distributed Technologies for Enhanced Healthcare Delivery (pp. 211- 224). Cham: Springer Nature Switzerland.
27. Baboo, A., Dash, S., Padhy, S., & Samuel, P. (2026). Testing Perspectives of Fog-Based IoMT Applications with Federated Learning. In Integrating Cloud, Fog, and Edge Computing in Healthcare: Federated Learning and Blockchain Approaches: Harnessing Distributed Technologies for Enhanced Healthcare Delivery (pp. 199-210). Cham: Springer Nature Switzerland..
28. N. R. S, "Emotion Recognition from Facial Expression using deep learning," International Journal of Engineering and Advanced Technology (IJEAT), vol. 8, no. 6S, 2019.
29. Baboo, A., Patro, S. P., & Dash, S. (2024, December). A Deep Learning Approach for Enhancing Cardiovascular Disease Prediction Using ECG Data. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-5). IEEE.
30. Dash, A. B., Dash, S., Padhy, S., Kumar, N., Pati, G. K., & Uthansingh, K. (2025). Leveraging inception-v3 CNN model for efficient image classification. In Intelligent Computing and Communication Techniques (pp. 341-348). CRC Press.
31. Dash, A. B., Dash, S., Padhy, S., Mishra, B., & Paikaray, B. K. (2025). Streamlining colorectal cancer diagnosis: leveraging MobileNet-V3 for efficient image classification. Int. J. Internet Manufacturing and Services, 11(4), 317.
32. Dash, S., Padhy, S., Kumar, N., & Nayyar, A. (2026). Medisecure: a hybrid approach for enhancing multimedia data protection in healthcare. Cluster Computing, 29(1), 75.
33. Dash, S., Padhy, S., Suman, P., Mal, S., Malviya, L., Suman, A., & Kishore, J. (2025). Privacy-preserving diabetes and heart disease prediction via federated learning and WCO. International Journal of Computational Intelligence Systems, 18(1), 217.
34. Kumar, K. D., Dash, S., Ganiya, R. K., & Pradhan, P. C. (2025). A genetic algorithm-optimized hybrid CNN-LSTM for robust EEG seizure detection and adversarial defense. In Intelligent Computing Techniques and Applications (pp. 96-101). CRC Press.
35. Khandakhani, S. W., Dash, S., Padhy, S., & Panda, R. (2024, December). Implementation of Customized Convolutional Neural Networks for Handwritten Marathi Character Recognition. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-6). IEEE.
2. Rath, L., Mishra, S. R., Dash, S., Pradhan, P. C., & Baboo, A. (2025). Predicting diabetic patients coronary artery calcium score, deep learning using retinal images. In Intelligent Computing Techniques and Applications (pp. 113-118). CRC Press.
3. Pattnayak, A. P., Mishra, S. R., Dash, S., & Baboo, A. (2025). Utilization of deep learning and machine learning models to approach high glucose and low glucose prediction with type 1 diabetes mellitus in adult patients. In Intelligent Computing Techniques and Applications (pp. 102-107). CRC Press.
4. Dora, N., Dash, S., Baboo, A., & Mishra, S. R. (2025, August). Efficient Nail Disease Diagnosis Using Deep Neural Networks for Predicting Abnormalities. In 2025 International Conference on Next Generation of Green Information and Emerging Technologies (GIET) (pp. 1-5). IEEE.
5. Mishra, S. R., Dash, S., & Rath, L. (2024, November). Effective Diabetes Mellitus Prediction Using a Hybrid Ensemble Machine Learning Model with Iot. In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1- 8). IEEE.
6. Khuntuli, B., Dash, S., Pradhan, P. C., & Mishra, S. R. Combating food insecurity through remote sensing and machine learning for enhanced crop yield prediction. In Intelligent Computing Techniques and Applications (pp. 135-140). CRC Press.
7. Sahu, P. K., Biswal, B. B., Mishra, S. R., Padhy, J., & Kumar, D. (2025, March). Demand-Based Secured Data Transmission in WSN. In International Conference on Next Generation Computing and Communication Applications (pp. 37-44). Cham: Springer Nature Switzerland.
8. R. Shyam, "Convolutional Neural Network and its Architectures.," Journal of Computer Technology & Applications, vol. 12, no. 2, pp. 6-14, 2021.
9. Mishra, S. R., Dash, S., Padhy, S., Kumar, N., & Dash, Y. (2024, September). Integrating Multi-Omics Data for Advanced Diabetes Prediction and Understanding. In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I) (Vol. 7, pp. 1447-1453). IEEE.
10. Dash, S., Mishra, S. R., & Baboo, A. (2025, January). Enhancing Diabetes Prediction using Hybrid Ensemble Approach. In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN) (pp. 1-6). IEEE.
11. R. Shyam, "Machine Learning and Its Dominant Paradigms," Journal of Advancements in Robotics, vol. 8, no. 2, pp. 1-10, 2021.
12. Dash, S., Mishra, S. R., & Baboo, A. (2025, January). Efficient Prediction of Diabetes Mellitus Through Hybrid Ensemble Machine Learning Model Using IoT. In 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET) (pp. 1- 6). IEEE.
13. Mishra, S. R., & Dash, S. (2024, December). Machine Learning Based Diabetes Prediction Using the PIMA Indian Dataset. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-6). IEEE.
14. M. A. H. Akhand, S. Roy, N. Siddique, M. A. S. Kamal, and T. Shimamura, “Facial emotion recognition using transfer learning in the deep CNN,” Electron., vol. 10, no. 9, 2021, doi: 10.3390/electronics10091036.
A. S. Qazi, M. S. Farooq, F. Rustam, M. G. Villar, C. L. Rodríguez, and I. Ashraf, “Occlusions and tilt in facial recognition systems,” Appl. Sci., vol. 12, no. 1, 2022.
B. Gautam and K. R. Seeja, “Facial emotion recognition using handcrafted features and CNN,” Procedia Comput. Sci., vol. 218, pp. 1295-1303, 2023, doi: 10.1016/j.procs.2023.01.108.
15. X. Tao et al., “Facial video-based non-contact emotion recognition: A multi-view features expression and fusion method,” Biomed. Signal Process. Control, vol. 96, 2024.
16. H. V. Manalu and A. P. Rifai, “Detection of human emotions through facial expressions using hybrid convolutional neural network- recurrent neural network algorithm,” Intell. Syst. Appl., vol. 21, p. 200339, 2024, doi: 10.1016/j.iswa.2024.200339
17. H. Jiang and E. Learned-Miller, “Face detection with the faster R-CNN,” in Proc. 2017 IEEE Winter Conf. Appl. Comput. Vision,pp. 1-6, 2017.
18. R. Shyam, "Automatic Face Recognition in Digital World," Advances in Computer Science and Information Technology (ACSIT), vol. 2, no. 1, pp. 64-70, 2015.
19. Mishra, S. R., Dash, S., Padhy, S., & Das, R. K. (2024, September). Diabetic Foot Ulcer Diagnosis Through Deep Learning Model. In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit) (pp. 1194-1199). IEEE.
20. Mishra, S. R., & Dash, S. (2024). Iterative Model Design for Diabetes Analysis Using FedOmics Causal Network and Federated Multi- Omics Variational Autoencoder. International Journal of Emerging Technologies and Innovative Research, 11(6).
21. S. R. N. S. M. A. H. Akhand, "Facial Emotion Recognition Using Transfer Learning in the Deep CNN," MDPI, vol. 10, no. 9, 2021.
22. Mishra, S. R., & Dash, S. (2024). Predictive Analysis On Diabetes Detection Using Pima Indian Diabetes Dataset. IJRAR-International Journal of Research and Analytical Reviews (IJRAR), 11(2).
23. Baboo, A., Mishra, S. R., & Dash, S. (2024, November). An Improvised Diabetes Prediction System Using Hybrid Ensemble Approach. In 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1-6). IEEE.
24. N. Mehendale, "Facial emotion recognition using convolutional neural networks (FERC)," SN Applied Sciences, vol. 2, no. 3, 2020.
25. Mishra, S. R., & Dash, S. (2026). AI ‐ Driven Remote Health Monitoring for Predicting Diabetes and Heart Diseases Using ULMCSO and PGND Models. Hyper ‐ Intelligent Networks: Exploring the Future of Connectivity for Society 5.0, 219-247.
26. Mishra, S. R., Dash, S., Padhy, S., & Samuel, P. (2026). Legal Aspects of Operating IoMT Applications in the Fog Computing. In Integrating Cloud, Fog, and Edge Computing in Healthcare: Federated Learning and Blockchain Approaches: Harnessing Distributed Technologies for Enhanced Healthcare Delivery (pp. 211- 224). Cham: Springer Nature Switzerland.
27. Baboo, A., Dash, S., Padhy, S., & Samuel, P. (2026). Testing Perspectives of Fog-Based IoMT Applications with Federated Learning. In Integrating Cloud, Fog, and Edge Computing in Healthcare: Federated Learning and Blockchain Approaches: Harnessing Distributed Technologies for Enhanced Healthcare Delivery (pp. 199-210). Cham: Springer Nature Switzerland..
28. N. R. S, "Emotion Recognition from Facial Expression using deep learning," International Journal of Engineering and Advanced Technology (IJEAT), vol. 8, no. 6S, 2019.
29. Baboo, A., Patro, S. P., & Dash, S. (2024, December). A Deep Learning Approach for Enhancing Cardiovascular Disease Prediction Using ECG Data. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-5). IEEE.
30. Dash, A. B., Dash, S., Padhy, S., Kumar, N., Pati, G. K., & Uthansingh, K. (2025). Leveraging inception-v3 CNN model for efficient image classification. In Intelligent Computing and Communication Techniques (pp. 341-348). CRC Press.
31. Dash, A. B., Dash, S., Padhy, S., Mishra, B., & Paikaray, B. K. (2025). Streamlining colorectal cancer diagnosis: leveraging MobileNet-V3 for efficient image classification. Int. J. Internet Manufacturing and Services, 11(4), 317.
32. Dash, S., Padhy, S., Kumar, N., & Nayyar, A. (2026). Medisecure: a hybrid approach for enhancing multimedia data protection in healthcare. Cluster Computing, 29(1), 75.
33. Dash, S., Padhy, S., Suman, P., Mal, S., Malviya, L., Suman, A., & Kishore, J. (2025). Privacy-preserving diabetes and heart disease prediction via federated learning and WCO. International Journal of Computational Intelligence Systems, 18(1), 217.
34. Kumar, K. D., Dash, S., Ganiya, R. K., & Pradhan, P. C. (2025). A genetic algorithm-optimized hybrid CNN-LSTM for robust EEG seizure detection and adversarial defense. In Intelligent Computing Techniques and Applications (pp. 96-101). CRC Press.
35. Khandakhani, S. W., Dash, S., Padhy, S., & Panda, R. (2024, December). Implementation of Customized Convolutional Neural Networks for Handwritten Marathi Character Recognition. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-6). IEEE.
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