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Random projection multi-kernel learning-based hand gesture identification method

A random projection, multi-core learning technology, applied in the field of gesture recognition based on random projection multi-core learning, can solve the problems of high complexity, low recognition rate, background interference, etc.

Active Publication Date: 2017-06-30
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0008] In order to overcome the shortcomings and deficiencies of the existing technology, the present invention provides a gesture recognition method based on random projection multi-core learning, which solves the problems of background interference, high complexity, long time consumption and low recognition rate in the current traditional gesture recognition method And other issues

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  • Random projection multi-kernel learning-based hand gesture identification method
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  • Random projection multi-kernel learning-based hand gesture identification method

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Embodiment Construction

[0066] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be further described in detail below in conjunction with the drawings and specific embodiments.

[0067] Such as figure 1 As shown, this example presents a gesture recognition method based on random projection multi-core learning, which mainly includes the following steps:

[0068] S1 collects gesture images and preprocesses the images. The preprocessing includes gesture positioning and gesture segmentation. The specific process is as follows:

[0069] S1.1 Use the Grayworld light compensation algorithm to reduce the impact of the lighting environment on the subsequent gesture segmentation; respectively calculate the average value r of the three color components of the gesture image avg , g avg , b avg , define the average gray value of the image as gray avg =(r avg +g avg ...

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Abstract

The invention discloses a random projection multi-kernel learning-based hand gesture identification method comprising the following steps: hand gesture images are collected and preprocessed, preprocessing operation comprises hand gesture positioning operation and hand gesture segmenting operation, sift characteristics are extracted from preprocessed and segmented hand gestures, a K-means algorithm is adopted for training a learning dictionary, an iteration dictionary is used for updating the algorithm and the dictionary, the gesture images are subjected to space pyramid dividing operation, the trained dictionary is used for encoding the sift characteristics of the hand gesture images in each space pyramid layer, and therefore characteristic vectors can be obtained and subjected to cascading operation; random projection is adopted for subjecting the characteristic vectors to dimensional reducing operation; as for a characteristic vector learning kernel matrix after dimensional reducing of each pyramid layer, a multi-kernel model learning algorithm is adopted for classified learning, and an optimal kernel matrix combination coefficient is obtained. Via the method disclosed in the invention, problems of background interference, high complexity, long time consumption, low identification rate and the like in a conventional hand gesture identification method can be solved.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to a gesture recognition method based on random projection multi-core learning. Background technique [0002] At present, with the continuous advancement of science and technology, human-computer interaction has developed rapidly, and human-computer interaction has become one of the hotspots of researchers. The goal of human-computer interaction is to realize natural communication between users and machines, and to provide users with real-time and intuitive interactive experience. Since information is transmitted between people through language, body and expression, and gestures are natural and intuitive, human-computer interaction based on gesture recognition has attracted more and more attention. play an increasingly important role. Gesture recognition involves multiple disciplines, such as computing science, machine learning, pattern recognition, image and video pro...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06K9/46G06K9/34G06T7/90
CPCG06V40/28G06V10/267G06V10/462G06F18/28G06F18/213G06F18/2163G06F18/24
Inventor 王淼孙季丰余家林宋治国
Owner SOUTH CHINA UNIV OF TECH
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