Joint Biostatistics Seminar: Transfer Learning Assisted Face Recognition
Zhengming Ding, PhD, Assistant Professor
Department of Computer, Information and Technology – Purdue School of Engineering and Technology
Friday, January 25th, 2019, 1-2pm, HITS1110
Transfer learning aims to mimic human cognitive process to adapt previous well-learn knowledge to facilitate the new challenging learning tasks. In this presentation, I will briefly summarize transfer learning in visual recognition tasks and focus on two specific face recognition topics, i.e., missing modality and one-shot learning. First, we always confront the problem that we have no face samples of target modality available in the training stage, which arises when the face data are multi-modal. To overcome this, we first borrow an auxiliary database with complete modalities, then propose a two-directional knowledge transfer to solve the missing modality issue. Second, there is always a challenge that only one sample of some persons are accessible in the training process. It is very difficult for existing learning approaches to handle it, since limited data cannot well represent the data variance. Thus, we develop a novel generative model to synthesize meaningful data for one-shot persons by adapting the data variances from other normal persons.