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Human motion emotion identification method based on Gauss feature

A technology for human motion and emotion recognition, applied in character and pattern recognition, image analysis, instruments, etc., can solve problems such as slow learning and recognition speed and low recognition rate

Inactive Publication Date: 2009-10-21
HUAZHONG UNIV OF SCI & TECH
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The present invention provides a Gaussian feature-based human motion emotion recognition method, aiming to solve the problems of low recognition rate and slow learning and recognition speed of existing emotion recognition methods

Method used

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  • Human motion emotion identification method based on Gauss feature
  • Human motion emotion identification method based on Gauss feature
  • Human motion emotion identification method based on Gauss feature

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Embodiment Construction

[0089] figure 1 is a schematic flow chart of the present invention; figure 2 It is a schematic flow chart of the training sub-steps of the present invention.

[0090] The present invention is described in detail below in conjunction with embodiment.

[0091] This embodiment is based on a motion database including four types of human body motions such as walking, knocking on the door, raising hands, and throwing things and four emotional categories of happiness, sadness, no emotion, and anger established by Pollick et al. of the University of Glasgow. There are 30 characters in the database, each character contains four emotion categories for each movement type, and each emotion has two movement sequences. One movement type contains a total of 240 human body movement sequences, from which this embodiment extracts the door-knocking movement type of three-dimensional motion data of 15 joint points of the human body, and because most people knock on the door with their right ha...

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Abstract

The invention relates to a human motion emotion identification method based on Gauss feature, belongs to the field of computer mode identification, and solves the problems of low identification rate and low learning identification speed in the prior emotion identification method. The method comprises a training classifier step and an emotion identification step, wherein the training classifier step comprises a training data acquisition substep, a motion segmentation substep, a feature extraction substep and a training substep; and the emotion identification step comprises a data to be detected acquisition substep, a motion segmentation substep, a feature extraction substep and an identification substep. The method adopts the Gauss feature to describe human motion, has the advantages of strong descriptive power, low feature dimension, good Lie group structure, capability of effectively analyzing spatial structure, and the like, adopts a LogitBoost machine learning method based on a Lie group space to carry out multi-emotion identification, makes full use of the Lie group structure of the Gauss feature in a machine learning process, and has high training and identification efficiency and strong practicability.

Description

technical field [0001] The invention belongs to the field of computer pattern recognition, and in particular relates to a Gaussian feature-based human motion emotion recognition method. The Gaussian feature is extracted according to the three-dimensional data of each joint point in the human body motion, and the LogitBoost method is used to identify human emotion categories. Background technique [0002] Since Picard of the MIT Media Lab proposed "emotional computing" in 1997, how to automatically recognize and understand human emotions has attracted close attention from researchers in computer science, psychology and other related disciplines as well as from the business community. According to research by psychologists, in the interaction between people, the amount of information contained in the tone of voice accounts for 38% of the total amount of information transmitted, and the expression of the face accounts for 55% of the total amount of information. Intonation is wi...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/66G06K9/46G06T7/20G06T7/215
Inventor 王天江刘芳龚立宇陈幸李新仕张富强陈刚
Owner HUAZHONG UNIV OF SCI & TECH
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