The week's pick
Random Articles
Reseach Article
Depression Scale Recognition Over Fusion of Visual and Vocal Expression using Artificial Intellectual Method
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 24 |
| Year of Publication: 2021 |
| Authors: D.A. Vidhate, Pallavi Kumatkar, Vaishali Zine, Vaishnavi Kalyankar, Rutuja Satpute, Shruti S. Pophale |
10.5120/ijca2021921607
|
D.A. Vidhate, Pallavi Kumatkar, Vaishali Zine, Vaishnavi Kalyankar, Rutuja Satpute, Shruti S. Pophale . Depression Scale Recognition Over Fusion of Visual and Vocal Expression using Artificial Intellectual Method. International Journal of Computer Applications. 183, 24 ( Sep 2021), 16-19. DOI=10.5120/ijca2021921607
Abstract
In Automatic depression assessment supported visual and vocal cues may be a rapidly growing research domain. This exhaustive review of existing approaches as reported in over sixty publications during the last ten years focuses on image processing and machine learning algorithms. Visual indication of depression, many proceed used for data gathering, and existing datasets are reviewed. The article describes techniques and algorithms for visual feature extraction, dimensionality reduction, decision methods for classification, and regression approaches, also as different fusion strategies. A quantitative meta-analysis of reported results, counting on performance metrics robust to chance, is included, identifying general trends and key pending issues to be treated in future studies of automatic depression assessment utilizing visual and vocal cues alone or together with cues. The proposed work also administered to predict Depression levels consistent with the current input of videos using deep learning also as NLP.
References
- Girard, Jeffrey M., Jeffrey F. Cohn, Mohammad H. Mahoor, Seyed mohammad Mavadati, and Dean P. Rosenwald “Social risk and depression: Evidence from manual and automatic facial expression analysis” 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG),, pp. 1- 8. IEEE, 2013.
- Deepak A Vidhate, Parag Kulkarni “Performance comparison of multiagent cooperative reinforcement learning algorithms for dynamic decision making in retail shop application”, International Journal of Computational Systems Engineering, Inderscience Publishers (IEL), Vol 5,Issue 3,pp 169-178, 2019.
- Alghowinem, Sharifa, Roland Goecke, Jeffrey F. Cohn, Michael Wagner, Gordon Parker, and Michael Breakspear. ”Cross-cultural detection of depression from nonverbal behavior” 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol. 1, pp. 1-8. IEEE, 2015.
- Deepak A Vidhate, Parag Kulkarni “A Framework for Dynamic Decision Making by Multi-agent Cooperative Fault Pair Algorithm (MCFPA) in Retail Shop Application”, Information and Communication Technology for Intelligent Systems, Springer, Singapore, pp 693-703, 2019.
- Pampouchidou, A., O. Simantiraki, C-M. Vazakopoulou, C. Chatzaki, M. Pediaditis, K. Marias et al. “Facial geometry and speech analysis for depression detection” 39th Annual International Conference on Engineering in Medicine and Biology Society (EMBC), pp. 1433-1436. IEEE, 2017.
- Deepak A Vidhate, Parag Kulkarni “A Novel Approach by Cooperative Multiagent Fault Pair Learning (CMFPL)”, Communications in Computer and Information Science, Springer, Singapore, Volume 905, pp 352-361, 2018.
- Harati, Sahar, Andrea Crowell, Helen Mayberg, and Shamim Nemati. “Discriminating clinical phases of recovery from major depressive disorder using the dynamics of facial expression” 38th Annual International Conference of Engineering in Medicine and Biology Society (EMBC), pp. 2254- 2257, IEEE, 2016.
- Deepak A Vidhate, Parag Kulkarni, “Exploring Cooperative Multi-agent Reinforcement Learning Algorithm (CMRLA) for Intelligent Traffic Signal Control”, Smart Trends in Information Technology and Computer Communications. SmartCom 2017, Volume 876, pp 71-81.
- Cohn, Jeffrey F., Tomas Simon Kruez, Iain Matthews, Ying Yang, Minh Hoai “Detecting depression from facial actions and vocal prosody” 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops. ACII 2009., pp. 1-7. IEEE, 2009.
- Deepak A Vidhate, Parag Kulkarni, “A Novel Approach for Dynamic Decision Making by Reinforcement Learning-Based Cooperation Methods (RLCM)”, International Conference on Intelligent Computing and Applications, Springer, Singapore, pp 401-411, 2018.
- Tasnim, Mashrura, Rifat Shahriyar, Nowshin Nahar, and Hossain Mahmud. “Intelligent depression detection and support system: Statistical analysis, psychological review and design implication” 18th International Conference on Health Networking, Applications and Services (Healthcom), pp.1-6 IEEE, 2016.
- Pampouchidou, Anastasia, Kostas Marias, Manolis Tsiknakis, P. Simos and Fabrice Meriaudeau “Designing a framework for assisting depression severity assessment from facial image analysis” International Conference on on Signal and Image Processing Applications (ICSIPA), pp.578-583, IEEE, 2015.
- Deepak A Vidhate, Parag Kulkarni, “Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA)”, International Journal of Intelligent Information Systems, Science Publishing Group, volume 6, issue 6,pp72-84,2017.
- Meng, Hongying, Di Huang, Heng Wang, Hongyu Yang, Mohammed AI-Shuraifi, and Yunhong Wang. “Depression recognition based on dynamic facial and vocal expression features using partial least square regression” 3rd ACM international workshop on Audio/visual emotion challenge, pp. 21-30, ACM, 2013.
- Deepak A Vidhate, Parag Kulkarni, “A novel approach to association rule mining using multilevel relationship algorithm for cooperative learning” 4th International Conference on Advanced Computing & Communication Technologies, pp 230-236, 2014.
Index Terms
Keywords