Dynamic hand gesture recognition: An exemplar-based approach from motion divergence fields

Exemplar-based approaches for dynamic hand gesture recognition usually require a large collection of gestures to achieve high-quality performance. Efficient visual representation of the motion patterns hence is very important to offer a scalable solution for gesture recognition when the databases are large. In this paper, we propose a new visual representation for hand motions based on the motion divergence fields, which can be normalized to gray-scale images. Salient regions such as Maximum Stable Extremal Regions (MSER) are then detected on the motion divergence maps. From each detected region, a local descriptor is extracted to capture local motion patterns. We further leverage indexing techniques from image search into gesture recognition. The extracted descriptors are indexed using a pre-trained vocabulary. A new gesture sample accordingly can be efficiently matched with database gestures through a term frequency-inverse document frequency (TF-IDF) weighting scheme. We have collected a hand gesture database with 10 categories and 1050 video samples for performance evaluation and further applications. The proposed method achieves higher recognition accuracy than other state-of-the-art motion and spatio-temporal features on this database. Besides, the average recognition time of our method for each gesture sequence is only 34.53 ms.

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► We use motion divergence fields to transform motion patterns into spatial patterns.
► Maximum Stable Extremal Regions (MSER) are detected from the divergence fields.
► The descriptors extracted from MSER are matched with indexing technique.
► Our method provides a scalable solution for gesture recognition on large databases.
► The recognition rate achieves 97.62% on our dataset, with recognition time 34.53 ms.