Multi-layer skin detection and fused hand pose matching

A skin detection and skin technology, applied in the field of multi-layer skin detection and fusion gesture matching, can solve the problems of effective skin color, a lot of resources or time, achieve the effect of small false positive rate and avoid power problems

Active Publication Date: 2017-12-01
INTEL CORP
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  • Application Information

AI Technical Summary

Problems solved by technology

This approach may not work on all skin tones and may require significant resources or time

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  • Multi-layer skin detection and fused hand pose matching
  • Multi-layer skin detection and fused hand pose matching
  • Multi-layer skin detection and fused hand pose matching

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Embodiment Construction

[0029] Skin detection can be based on many design principles. One principle is that successful skin filtering is achieved by simultaneously using multiple lightness-invariant color models used by separate weak color-based classifiers. By stacking many weak color classifiers together, it is possible to design stronger color classifiers that better approximate the performance of an ideal skin detector. By using a luminance-invariant color model, it is possible to allow some tolerance to light variations while still removing as many background pixels as possible.

[0030] A second principle guiding the design of the skin detector may include a color model that is periodically computed or updated from previous models in skin pixels that match poses. This way may be simpler, more reliable and not as restrictive as other options. Because this may occur less frequently (e.g., on a second or sub-timescale) than histogram backprojection or dynamic motion model updates that typically ...

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Abstract

Embodiments of a system and methods for skin detection and pose determination of a hand in an image are generally described herein. A method for may include detecting skin pixels in an image using a multi- layer skin filter and classifying the skin pixels into a set of foreground skin pixels and a set of background skin pixels. The method may include storing information about the set of background skin pixels in a persistent memory. The method may include determining a set of features from the set of foreground skin pixels, and clustering features from the set of features to create a set of hand pose descriptors. The method may include determining a set of candidate hand pose region descriptors and a set of candidate hand pose contour descriptors that match the set of hand pose descriptors and detecting a valid hand pose using the sets of candidate descriptors.

Description

[0001] priority application [0002] This application claims the benefit of priority to US Application Serial No. 14 / 667,340, filed March 24, 2015, which is incorporated herein by reference. Background technique [0003] Image-based solutions for gesture recognition and gesture detection become more sophisticated every year. Current solutions include using a global color model to detect skin. Problems with this solution may include higher error rates. Other solutions for detecting gestures use trained regression models. Disadvantages of trained regression models are that they are resource-intensive, time-consuming, or difficult to train. Still other approaches include using skin information to directly detect gestures. This approach may not work on all skin tones and may require significant resources or time. Contents of the invention [0004] Such systems described herein include a set of techniques for enhancing the quality of gesture recognition systems based on, but...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06V10/56G06V10/762G06V10/764
CPCG06V40/28G06F18/231G06F18/22G06F18/2411G06T7/70G06T7/11G06V40/11G06V10/56G06V10/762G06V10/764G06F18/23G06F18/24G06T2207/10004
Inventor M·库纳维斯A·德巴蒂斯塔
Owner INTEL CORP
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