Gesture Recognition Using Enhanced Depth Motion Map and Static Pose Map

In this paper, we propose a gesture recognition method using Enhanced Depth Motion Map (eDMM) and Static Pose Map (SPM) from depth videos. Firstly, the eDMM is proposed to describe motions in gesture videos, which is more robust to noise than DMM. The SPM is constructed to describe static postures of a gesture, which provides complementary information for the eDMM. Then a 2-CNN architecture is used to extract features from eDMM and SPM. The extracted features are fused to form gesture feature which contains both dynamic movement information and static pose information. Finally an ANN is trained for gesture recognition. The proposed method is evaluated on the Chalearn IsoGD dataset and the NATOPS dataset. The experimental results show that the proposed method achieves higher recognition rate than the baseline method of the Chalearn IsoGD dataset and is competitive with the-state-of-art of the NATOPS dataset.