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Abstract

The subject technology provides vision-based hand gesture customization. An apparatus receives input data having image representations of a first hand gestures corresponding to different types of gestures. For each of the first hand gestures, the apparatus extracts features from one of the image representations of the first hand gestures to produce a skeleton-based feature representation of a corresponding hand gesture. The apparatus produces a first trained machine learning model by training a neural network with a first dataset having skeleton-based feature representations associated with the first hand gestures to predict a classification for the first hand gestures. The apparatus generates a second dataset having training samples representing at least a second hand gesture customized by a user. The apparatus produces a second trained machine learning model by training the first trained machine learning model based on the second dataset to predict a classification for the second hand gesture.

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First Claim