Dynamic hand gesture recognition using depth data
First Claim
1. In a computing environment, a method performed at least in part on at least one processor, the method comprising:
- detecting a hand by;
identifying a wrist area as a thinnest part of an arm portion; and
separating the arm portion at the identified wrist area;
segmenting depth data to isolate the hand represented in a plurality of frames that include hand movement;
rotating the hand such that a palm of the hand has a normalized and oriented position relative to an image plane;
extracting feature values corresponding to the rotated hand; and
recognizing the hand movement as a hand gesture based upon the feature values.
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Accused Products
Abstract
The subject disclosure is directed towards a technology by which dynamic hand gestures are recognized by processing depth data, including in real-time. In an offline stage, a classifier is trained from feature values extracted from frames of depth data that are associated with intended hand gestures. In an online stage, a feature extractor extracts feature values from sensed depth data that corresponds to an unknown hand gesture. These feature values are input to the classifier as a feature vector to receive a recognition result of the unknown hand gesture. The technology may be used in real time, and may be robust to variations in lighting, hand orientation, and the user'"'"'s gesturing speed and style.
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Citations
20 Claims
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1. In a computing environment, a method performed at least in part on at least one processor, the method comprising:
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detecting a hand by; identifying a wrist area as a thinnest part of an arm portion; and separating the arm portion at the identified wrist area; segmenting depth data to isolate the hand represented in a plurality of frames that include hand movement; rotating the hand such that a palm of the hand has a normalized and oriented position relative to an image plane; extracting feature values corresponding to the rotated hand; and recognizing the hand movement as a hand gesture based upon the feature values. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system comprising:
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a memory; a computing device; and a processor programmed to; detect a hand by; identifying a wrist area as a thinnest part of an arm portion; and separating the arm portion at the identified wrist area; segment depth data to isolate the hand represented in a plurality of frames that include hand movement; rotate the hand such that a palm of the hand has a normalized and oriented position relative to an image plane; extract feature values corresponding to the rotated hand; and recognize the hand movement as a hand gesture based upon the feature values. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. One or more computer-readable storage devices having computer-executable instructions, which when executed perform operations comprising:
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detecting a hand by; identifying a wrist area as a thinnest part of an arm portion; and separating the arm portion at the identified wrist area; segmenting depth data to isolate the hand represented in a plurality of frames that include hand movement; rotating the hand such that a palm of the hand has a normalized and oriented position relative to an image plane; extracting feature values corresponding to the rotated hand; and recognizing the hand movement as a hand gesture based upon the feature values. - View Dependent Claims (18, 19, 20)
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Specification