Continuous Discriminant Latent Topic Model (CD-LTM) for Facial Expression Recognition and Generation
Grant Data
Project Title
Continuous Discriminant Latent Topic Model (CD-LTM) for Facial Expression Recognition and Generation
Principal Investigator
Dr Chan, Kwok Ping
(Principal Investigator (PI))
Duration
42
Start Date
2013-01-01
Amount
667000
Conference Title
Continuous Discriminant Latent Topic Model (CD-LTM) for Facial Expression Recognition and Generation
Presentation Title
Keywords
Facial Expression Generation, Facial Expression Recognition, Latent Dirichlet Allocation, Latent Tropic Models
Discipline
Artificial Intelligence (Obsolete),Others - Computing Science and Information Technology
Panel
Engineering (E)
HKU Project Code
HKU 710412E
Grant Type
General Research Fund (GRF)
Funding Year
2012
Status
Completed
Objectives
1) Based on our Temporal Latent Topic Model, we will model the correlation between topics, so that the model is more accurate; 2) Maximum Margin Learning will be incorporated to provide better discriminant information for the model; 3) A Continuous input model will be developed, which should provide better performance as compared to discrete input models; 4) More facial expression data will be collected as the current CMU database used in our study is limited; 5) Facial expression generation system will be built based on the generative Latent Topic Model obtained in the recognition process.