In this work a framework based on histogram of
orientation of optical flow (HOOF) and local binary pattern from
three orthogonal planes (LBP_TOP) is proposed for recognizing
dynamic hand gestures. HOOF algorithm extracts local shape
and dynamic motion information of gestures from image
sequences and local descriptor LBP is extended to three
orthogonal planes to create an efficient motion descriptor. These
features are invariant to scale, translation, illumination and
direction of motion. The performance of the new framework is
tested in two different ways. The first one is by fusing the global
and local features as one descriptor and the other is using features
separately to train the multi class support vector machine.
Performance analysis shows that the proposed approach produces
better results for recognizing dynamic hand gestures when
compared with state of the art methods