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    [CS 297 Proposal]

    [Deliverable 1]

    [Depth Wise Convolutional Model-PDF]

    [Deliverable 2]

    [My Experience with Unity-PDF]

    [Deliverable 3]

    [Deliverable 4]

    [CS297 Final Report-PDF]

    [CS 298 Proposal]

    [CS298 Final Report-PDF]

    [CS298 Slides-PDF]

CS297 Proposal

Visual and Lingual Emotional Recognition using Deep Learning Techniques

Akshay Kajale

Advisor: Dr. Chris Pollett


In this era of technology and Artificial Intelligence, there is growing demand for human computer interaction. Humans express their emotions in differwnt ways (facial, speaking etc). The aim of this project is to detect the emotions of a person using facial expression and linguistic features by implementing computer vision, and natural language processing techniques. We will develop a hybrid neural network model to identify the emotion of a person based on the facial expreesion and pitch of the voice while speaking.

Week 1: Sept 23 - Sept 29Finalize topics, discuss deliverables and draft proposal
Week 2: Sept 29 - October 6Finalize the Dataset and continue working on Deliverable 1
Week 3: October 6 - October 13Deliverable 1 Due(Neural Network)
Week 4: October 13 - October 20Learn Unity to create dataset
Week 5: October 20 - October 27Decide the labelled dataset for training model and continue working on Deliverable 2
Week 6: October 27 - November 4Deliverable 2 Due(Video Dataset)
Week 7: November 4 - November 11Mobile Application Development. Continue to Work on Deliverable 3
Week 8: November 11 - November 18Deliverable 3 Due(Mobile Application)
Week 9: November 17 - November 24Deploy the model on the mobile application for fine tuning the parameters
Week 10: November 24 - December 2Deliverable 4 Due(Deployment of Model)
Week 11: Dec 2 - Dec 9Start CS 297 Report
Week 12: Dec 10 - Dec 14Review the Report and Deliverable 5 Due(CS297 Report)


The full project will be done when CS298 is completed. The following will be done by the end of CS297:

1. Implement a neural network to detect emotion using facial recognition algorithm with good accuracy.

2. Generate a video dataset which contains a person having different facial expressions.

3. Develop a mobile application which can access both cameras at the same time using split screen.

4. Deploy the model on Mobile Application for initial testing.

5. CS297 Report Due.


[1]L. Zhang, Y. Yang, W. Li, S. Dang and M. Zhu, "Research of Facial Expression Recognition Based on Deep Learning," 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, 2018, pp. 1-4, doi: 10.1109/ICSESS.2018.8663777.

[2]Mao Xu, Wei Cheng, Qian Zhao, Li Ma and Fang Xu, "Facial expression recognition based on transfer learning from deep convolutional networks," 2015 11th International Conference on Natural Computation (ICNC), Zhangjiajie, 2015, pp. 702-708, doi: 10.1109/ICNC.2015.7378076.

[3]A. Fathallah, L. Abdi and A. Douik, "Facial Expression Recognition via Deep Learning," 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), Hammamet, 2017, pp. 745-750, doi: 10.1109/AICCSA.2017.124.