Doctoral Consortium
The IEEE International Conference on Automatic Face and Gesture Recognition (FG 2023) Doctoral Consortium aims to provide a forum for doctoral students to discuss their research and career objectives with experienced researchers of international repute. One of the unique benefits of the consortium is the networking environment to establish new contacts and collaborations with other researchers. It will also serve to spread awareness amongst the students about career options in both academia and industry.
The FG 2023 Doctoral Consortium will be held during the conference, starting with an informal introduction by senior researchers followed by doctoral students. Following this, student participants will present their research and receive feedback from the invited committee. The students will also be paired with a mentor from the committee on the conference platform and can interact with their mentor during the conference directly on specific topics of interest.
Doctoral students working on research problems related to the FG community are invited to apply for the Doctoral Consortium. Successful applicants are expected to actively participate in the consortium and the conference. A best doctoral consortium application award will be given to the top applicant.
Best Doctoral Consortium Application Award
Saandeep Lakshminarayan, ``Towards Explainable Affective Computing using Efficient and Tractable Representation Learning’’
Accepted applications
| Title | Author |
|---|---|
| Causal Fairness for Affect Recognition | Jiaee Cheong (University of Cambridge) |
| An Exploratory Analysis of Automated Deception Detection for Mental Health Applications | Sayde L King (University of South Florida) |
| Towards Explainable Affective Computing using Efficient and Tractable Representation Learning | Saandeep Lakshminarayan (University of South Florida) |
| Video Representation and Deep Learning Techniques for Face Presentation Attack Detection | Usman Muhammad (University of Oulu) |
| Determining Affect Intensity on a Continuous Range | Ravikiran Parameshwara (University of Canberra) |
| Pain behavior evaluation using deep learning | Kevin Reby (LaBRI) |
| Bias in Facial Analysis | Surbhi Mittal (Indian Institute of Technology, Jodhpur) |
| Deepfakes: A Revelation Seeding Distrust on Internet | Kartik Thakral (Indian Institute of Technology Jodhpur) |
Mentors
- Linlin Shen
- In Kyu Park
- Shuqiong Wu
- Mayank Vasta
- Shaun Canavan
- Xiaoming Liu
- Stefano Berretti
- Mourad Oussalah
Doctoral Consortium Program: Jan. 8th, 12:00 - 13:30 HST
| Time Slot | |
|---|---|
| 12:00 - 12:05 | Opening Remarks |
| 12:05 - 12:45 | Short presentations by DC applicants (5 min for presentation and switching) (In-person) Sayde L King, ``An Exploratory Analysis of Automated Deception Detection for Mental Health Applications’‘ (In-person) Kevin Reby, “Pain Behavior Evaluation using Deep Learning” (Virtual) Jiaee Cheong, “Causal Fairness for Affect Recognition” (Virtual) Saandeep Lakshminarayan, “Towards Explainable Affective Computing using Efficient and Tractable Representation Learning’‘ (Virtual) Usman Muhammad, Video Representation and Deep Learning Techniques for Face Presentation Attack Detection (Virtual) Ravikiran Parameshwara, Determining Affect Intensity on a Continuous Range (Virtual) Surbhi Mittal, “Bias in Facial Analysis’‘ (Virtual) Kartik Thakral, “Deepfakes: A Revelation Seeding Distrust on Internet’’ |
| 12:45 - 13:25 | Mentoring time |
| 13:25 - 13:30 | Closing remark |
Contact
For further questions, contact the Doctoral Consortium Chairs
- Ajmal Mian, University of Western Australia, Australia
- Yasushi Makihara, Osaka University, Japan