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Face direction change recognition method based on neural network and sensitivity parameter

A neural network and steering recognition technology, which is applied in the field of face steering recognition based on neural network and sensitivity parameters, can solve the problem of low recognition accuracy, and achieve high recognition accuracy, wide application range and high recognition accuracy. Effect

Inactive Publication Date: 2016-07-20
CENT SOUTH UNIV
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Problems solved by technology

[0009] The purpose of the present invention is to provide a face turning recognition method based on neural network and sensitivity parameters, so as to solve the technical problem that the existing face recognition is applied to the dynamic process of interaction with mobile robots, and the recognition accuracy is not high

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  • Face direction change recognition method based on neural network and sensitivity parameter
  • Face direction change recognition method based on neural network and sensitivity parameter
  • Face direction change recognition method based on neural network and sensitivity parameter

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Embodiment Construction

[0065] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways defined and covered by the claims.

[0066] The sensitivity parameters referred to in this embodiment refer to a set of parameters defined according to the face turning habits of different people for realizing the interactive control of the mobile robot, including face turning speed, face turning magnitude and control rights competition. The face turning speed represents the speed of the individual's head turning speed. The face turning range refers to the specific angle at which the human face rotates to the front of the robot. In the interactive control of high-quality mobile robot transportation, in addition to the face turning speed and face turning range are very important, the parameters of the control right competition also directly affect the intelligence level of the interacti...

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Abstract

The invention discloses a face direction change recognition method based on a neural network and sensitivity parameters. The face direction change recognition method comprises the steps of: carrying out first static face orientation recognition on acquired single-frame color images one by one, including preprocessing the single-frame color images and extracting facial feature vectors, and judging whether the face orientation of each single-frame color image is frontal, leftward or rightward according to positions of eyes and / or nose of the facial feature vectors; carrying out the first static face orientation recognition on all the acquired single-frame color images within given acquisition time, so as to obtain a first face orientation result set with results arranged in turn according to time sequence and a plurality of facial feature vectors; and adopting neural networks classification for carrying out process analysis on the plurality of facial feature vectors and the first face orientation result set, recognizing instruction intention, and acquiring a first instruction result given in the face direction change process. The face direction change recognition method can achieve accurate face direction change recognition under the condition of strong backlight of the acquired images.

Description

technical field [0001] The invention relates to the field of mobile robots, in particular to a human face turning recognition method for robots based on neural networks and sensitivity parameters. Background technique [0002] In complex indoor environments, such as modern laboratories, transportation and logistics factories, etc., mobile robots are often used to replace personnel to perform simple, dangerous, and repetitive tasks to save a lot of human resource costs. In an indoor environment where the process control is particularly cumbersome, such as in a chemical laboratory, the use of mobile robots can reduce the chances of researchers coming into contact with dangerous goods, not only to ensure the accuracy of experiments, but also to effectively reduce the probability of accidents. [0003] Human-computer interaction is an important part of the intelligence of mobile robots. Today, humans have developed from sending instructions to machines through touch-based media ...

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V40/171G06V40/16
Inventor 刘辉李燕飞张雷张健
Owner CENT SOUTH UNIV
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