Classifying Hand Gestures with a View-Based Distributed Representation

We present a method for learning, tracking, and recognizing human hand gestures recorded by a conventional CCD camera without any special gloves or other sensors. A view-based representation is used to model aspects of the hand relevant to the trained gestures, and is found using an unsupervised clustering technique. We use normalized correlation net(cid: 173) works, with dynamic time warping in the temporal domain, as a distance function for unsupervised clustering. Views are computed separably for space and time dimensions; the distributed response of the combination of these units characterizes the input data with a low dimensional repre(cid: 173) sentation. A supervised classification stage uses labeled outputs of the spatio-temporal units as training data. Our system can correctly classify gestures in real time with a low-cost image processing accelerator.

Authors

• Trevor Darrell
• Alex Pentland

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