Intel Corporation

Microsoft Technology Licensing, LLC

Aharon Bar-Hillel

Eyal Krupka

Kfir Karmon

Ido Leichter

Aviv Hurvitz

Yoni Smolin

Yuval Tzairi

Ilya Gurvich

Alon Vinnikov

Noam Bloom

A gesture recognition method comprises receiving at a processor from a sensor a sequence of captured signal frames for extracting hand pose information for a hand and using at least one trained predictor executed on the processor to extract hand pose information from the received signal frames. For at least one defined gesture, defined as a time sequence comprising hand poses, with each of the hand poses defined as a conjunction or disjunction of qualitative propositions relating to interest points on the hand, truth values are computed for the qualitative propositions using the hand pose information extracted from the received signal frames, and execution of the gesture is tracked, by using the truth values to determine which of the hand poses in the time sequence have already been executed and which of the hand poses in the time sequence is expected next.

Gesture recognition

In one embodiment, the present invention includes a method for training a Support Vector Machine (SVM) on a subset of features (d') of a feature set having (d) features of a plurality of training instances to obtain a weight per instance, approximating a quality for the d features of the feature set using the weight per instance, ranking the d features of the feature set based on the approximated quality, and selecting a subset (q) of the features of the feature set based on the ranked approximated quality. Other embodiments are described and claimed.

Forward feature selection for support vector machines

Growing community of inventors

Kiryat-Ono, Israel

Aharon Bar-Hillel