MULTI-MODAL LEARNING FOR GESTURE RECOGNITION

With the development of sensing equipments, data from different modalities is available for gesture recognition. In this paper, we propose a novel multi-modal learning framework. A coupled hidden Markov model (CHMM) is employed to discover the correlation and complementary information across different modalities. In this framework, we use two configurations: one is multi-modal learning and multi-modal testing, where all the modalities used during learning are still available during testing; the other is multi-modal learning and single-modal testing, where only one modality is available during testing. Experiments on two real-world gesture recognition data sets have demonstrated the effectiveness of our multi-modal learning framework. Improvements on both of the multi-modal and single-modal testing have been observed.