CN109034129B - Robot with face recognition function - Google Patents
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Abstract
The invention discloses a robot with face recognition function, comprising: the robot comprises a robot body, and an image acquisition device, an image processor, a microprocessor, an alarm device and a memory which are arranged on the robot body. The method comprises the steps of collecting a face image through an image collecting device, processing the face image, extracting a feature vector representing feature information of the face image, matching the feature vector of the face image obtained through processing with a feature vector of the face image of a dangerous figure prestored in a memory, and generating a control instruction and sending the control instruction to an alarm device if the matching result is consistent; the alarm device receives the control instruction of the microprocessor, and performs voice broadcast and triggers the warning lamp. The robot can accurately identify the collected face image and has the advantages of high reliability, perfection and low cost.
Description
Technical Field
The invention relates to the field of robot control, in particular to a robot based on a face recognition function.
Background
The face recognition technology has wide application in the fields of national important organs and social security, has good concealment compared with other human body biological feature recognition technologies, and is the most important scientific and technological means for the international anti-terrorism security at present. In addition, the face recognition technology can also be applied to video retrieval of multimedia databases and media production. In recent years, with the development of computer hardware and software, the functions of robots are gradually improved, robots for cleaning, security and protection, and the like, and robots with various functions gradually replace human beings and participate in more important tasks. However, the robot systems developed today for face recognition still have many bugs, low reliability, imperfect system, and high cost.
Disclosure of Invention
In view of the above problems, the present invention provides a robot having a face recognition function.
The purpose of the invention is realized by adopting the following technical scheme:
a robot having a face recognition function, the robot comprising: the robot comprises a robot body, and an image acquisition device, an image processor, a microprocessor, an alarm device and a memory which are arranged on the robot body.
The image acquisition device is used for acquiring a face image and sending the face image to the image processor to process the face image; the image processor is used for processing the received face image and extracting the characteristic information of the face image to obtain the characteristic vector of the face image; the microprocessor is used for matching the characteristic vector of the face image with dangerous figures prestored in the memory, and if the matching result is consistent, a control instruction is generated and sent to the alarm device; the alarm device is used for receiving a control instruction of the microprocessor, carrying out voice broadcast and triggering the alarm lamp; the memory is used for storing preset feature vectors of the face images of the persons with dangerousness.
The invention has the beneficial effects that: the method comprises the steps of collecting a face image through an image collecting device, processing the face image, extracting a feature vector representing feature information of the face image, matching the feature vector of the face image obtained through processing with a feature vector of the face image of a dangerous figure prestored in a memory, and generating a control instruction and sending the control instruction to an alarm device if the matching result is consistent; the alarm device receives the control instruction of the microprocessor, and performs voice broadcast and triggers the warning lamp. The robot can accurately identify the collected face image and has the advantages of high reliability, perfection and low cost.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a block diagram of the present invention;
FIG. 2 is a block diagram of the image processor of the present invention;
fig. 3 is a frame configuration view of the alarm device of the present invention.
Reference numerals: an image acquisition device 1; an image processor 2; a microprocessor 3; an alarm device 4; a memory 5; an image preprocessing module 6; an image segmentation module 7; a feature extraction module 8; an image denoising unit 61; an image enhancement unit 62; a single chip microcomputer 41; a voice player 42; a warning light 43.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1, a robot having a face recognition function includes: the robot comprises a robot body, and an image acquisition device 1, an image processor 2, a microprocessor 3, an alarm device 4 and a memory 5 which are arranged on the robot body.
The image acquisition device 1 is used for acquiring a face image and sending the face image to the image processor to process the face image;
the image processor 2 is configured to process the received face image, and extract feature information of the face image to obtain a feature vector of the face image;
the microprocessor 3 is configured to match the feature vector of the face image with the feature vector of the face image of the dangerous figure pre-stored in the memory 5, and if the matching result is consistent, generate a control instruction and send the control instruction to the alarm device 4;
the alarm device 4 is used for receiving a control instruction of the microprocessor 3, and performing voice broadcast and triggering the alarm lamp;
the memory 5 is used for storing preset feature vectors of face images of people with dangerousness.
Has the advantages that: the method comprises the steps of collecting a face image through an image collecting device, processing the face image, extracting a feature vector representing feature information of the face image, matching the feature vector of the face image obtained through processing with a feature vector of the face image of a dangerous figure prestored in a memory, and generating a control instruction and sending the control instruction to an alarm device if the matching result is consistent; the alarm device receives the control instruction of the microprocessor, and performs voice broadcast and triggers the warning lamp. The robot can accurately identify the collected face image and has the advantages of high reliability, perfection and low cost.
Preferably, the image capturing device 1 is a CCD camera.
Preferably, referring to fig. 2, the image processor 2 comprises an image pre-processing module 6, an image segmentation module 7 and a feature extraction module 8.
The image preprocessing module 6 is used for preprocessing the face image; the image segmentation module 7 is used for segmenting the preprocessed face image; the feature extraction module 8 is configured to extract feature information of the face image from the segmented face image, so as to obtain a feature vector of the face image.
Preferably, the image preprocessing module 6 includes an image denoising unit 61 and an image enhancement unit 62.
The image denoising unit 61 is configured to remove random noise in the face image; the image enhancement unit 62 is configured to perform enhancement processing on the denoised face image.
Preferably, referring to fig. 3, the alarm device 4 includes a single chip 41, a voice player 42 and a warning lamp 43.
Preferably, the removing of the random noise in the face image specifically includes:
(1) performing J-layer wavelet decomposition on the face image by using wavelet transformation to obtain a group of wavelet coefficients z ═ z { (z)1,z2…zQQ is the number of wavelet coefficients;
(2) processing the wavelet coefficient z by using a threshold, wherein the threshold processing function is as follows:
wherein z is the wavelet coefficient before denoising, and z' is the wavelet coefficient after denoisingWavelet coefficient, λ1Is the upper threshold, λ2Is a lower threshold, and λ1、λ2Satisfy lambda1＝αλ2Alpha is more than 0 and less than 1; m and tau are regulating factors, m is more than 1, tau is more than 1, sgn (f) is a sign function, when f is a positive number, 1 is taken, and when f is a negative number, 0 is taken;
(3) and reconstructing z' by utilizing wavelet inverse transformation to obtain a denoised face image.
Has the advantages that: the images containing noise are processed by using the threshold processing function, so that the images containing noise can be effectively filtered; according to λ1、λ2Selecting different threshold functions to process the wavelet coefficients according to the absolute value difference of the wavelet coefficients z, so that the noise of the face image can be removed in a self-adaptive manner, and the effective information of the face image is kept; the actually acquired image contains various noises, and the waveform of the threshold processing function can be adjusted by adjusting the size of the adjusting factor m, so that the noises in the face image can be removed to the maximum extent.
Preferably, in the above embodiment, the lower threshold value of the wavelet coefficient of the j-th layer is calculated by the following formula:
in the formula, λ2,jIs the threshold lower limit value of the J-th level wavelet coefficient, J is the number of decomposition levels of the wavelet transform, and is 1,2, …, J, …, J, σQIs the estimated variance of Q wavelet coefficients, Q being the number of wavelet coefficients, σjIs the estimated variance of the layer j wavelet coefficients, DjIs the number of wavelet coefficients of the j-th layer, sigmar,jEstimated variance, k, for a noise-free signal r at layer j1、k2Is a weight factor and satisfies k1+k2＝1。
Has the advantages that: the lower limit values of the threshold values of different decomposition layers are respectively calculated by utilizing the algorithm, the obtained lower limit values of the threshold values of all the decomposition layers are substituted into a threshold processing function, the denoising processing of the face image is completed, the process realizes the self-adaptive adjustment of the lower threshold value and the upper threshold value, can select different lower threshold values and lower threshold values to complete the de-noising process of the human face image according to the actual situation of each decomposition layer of the wavelet transform, avoids the noise wavelet coefficient caused by setting a fixed threshold value from being reserved, so that a large amount of noise still exists in the denoised image, and simultaneously, the useful wavelet coefficient is prevented from being used as noise information, the denoised target is too smooth, so that the detail information is lost, the denoising accuracy is improved, and the subsequent accurate recognition of the acquired face image and the confirmation of the identity of the personnel are facilitated.
Preferably, the enhancing processing is performed on the denoised face image, specifically:
(1) using formulasAnd the denoised face image is reversed, wherein,which is the reverse image of the denoised face image,
c, any color channel in an image RGB color model is used as a denoised face image;
(2) respectively calculating the global atmospheric light and the transmittance value of the reverse image, wherein:
the global atmospheric light has the formula:
wherein k is a weight coefficient, Y (x) is a luminance map at a pixel point x in the inverted image,
the values of R channel, G channel and B channel at the position of pixel point x in the reverse image, A0Is an initial global atmospheric light; a. theCThe method comprises the following steps of forming a matrix by initial global atmospheric light in three RGB channels, wherein C is one of an R channel, a G channel and a B channel;
the transmission value is calculated as:
where t (x) is the transmittance value, ω is the custom tuning parameter, Ω (x) is the neighborhood centered on pixel x, y is the pixel in the neighborhood of pixel x,
the value of the C channel at the position of the pixel point y in the reverse image is obtained;
(3) and substituting the obtained global atmospheric light and transmittance into the following model function to obtain a restored scene light image, wherein the model function is as follows:
in the formula (I), the compound is shown in the specification,
is a scene light image;
(4) using formulasLight imaging a scene
Is subjected to inversion to obtain
Namely the enhanced face image.
Has the advantages that: by utilizing the algorithm, the denoised face image is reversed to obtain a reversed image, and the reversed image is further processed to obtain a scene light image.
Preferably, the transmittance value t (x) is corrected by the following formula to obtain a corrected transmittance value, and the calculation formula of the corrected transmittance value t' (x) is:
has the advantages that: by correcting the obtained transmittance value t (x) by using the formula, the transmittance value t' (x) not only can effectively increase the detail information of the target to be identified, but also can keep the spatial continuity of the transmittance, so that the restored scene image has a smoother visual effect.
Preferably, the feature vector of the face image is matched with the feature vector of the face image of the person with danger prestored in the memory 5, and if the matching result is consistent, a control instruction is generated and sent to the alarm device 4, specifically: the feature vector of the face image obtained by the image processor 2
And the characteristic vector of the pre-stored face image with dangerous figures
Performing a match if the feature vector
And the samePrestored characteristic vector of face image with dangerous figure
Satisfy the requirement of
The person in the acquired face image is dangerous, otherwise the person in the acquired face image is not dangerous, if the judgment result is that the person is dangerous, a control instruction is generated to the alarm device 4, wherein,
for the feature vectors of the face image processed by the image processor 2,
the feature vectors of the pre-stored face images of the dangerous figures are represented, and delta is a self-defined similarity factor.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (4)
1. A robot having a face recognition function, comprising: the robot comprises a robot body, and an image acquisition device, an image processor, a microprocessor, an alarm device and a memory which are arranged on the robot body;
the image acquisition device is used for acquiring a face image and sending the face image to the image processor to process the face image;
the image processor is used for processing the received face image and extracting the characteristic information of the face image to obtain the characteristic vector of the face image;
the microprocessor is used for matching the characteristic vector of the face image with dangerous figures prestored in the memory, and if the matching result is consistent, a control instruction is generated and sent to the alarm device;
the alarm device is used for receiving a control instruction of the microprocessor, carrying out voice broadcast and triggering the alarm lamp;
the memory is used for storing preset characteristic vectors of the face images of the dangerous persons;
the image processor comprises an image preprocessing module, an image segmentation module and a feature extraction module;
the image preprocessing module is used for preprocessing the face image;
the image segmentation module is used for segmenting the preprocessed face image;
the feature extraction module is used for extracting feature information of the face image from the segmented face image to obtain a feature vector of the face image;
the image preprocessing module comprises an image denoising unit and an image enhancement unit;
the image denoising unit is used for removing random noise in the face image;
the image enhancement unit is used for enhancing the denoised face image;
the method for removing the random noise in the face image specifically comprises the following steps:
(1) decomposing the face image by using wavelet transformation to obtain a group of wavelet coefficients z ═ z1，z2...zQQ is the number of wavelet coefficients;
(2) processing the wavelet coefficient z by using a threshold, wherein the threshold processing function is as follows:
wherein z is the wavelet coefficient before denoising, z' is the wavelet coefficient after denoising, and λ1Is the upper threshold, λ2Is a lower threshold, and λ1、λ2Satisfy lambda1＝αλ2Alpha is more than 0 and less than 1; m and tau are regulating factors, m is more than 1, tau is more than 1, sgn (f) is a sign function, when f is a positive number, 1 is taken, and when f is a negative number, 0 is taken;
(3) reconstructing z' by utilizing wavelet inverse transformation to obtain a denoised face image;
the enhancement processing is carried out on the denoised face image, and specifically comprises the following steps:
(1) using formulas
And the denoised face image is reversed, wherein,
which is the reverse image of the denoised face image,
c, any color channel in an image RGB color model is used as a denoised face image;
(2) respectively calculating the global atmospheric light and the transmittance value of the reverse image, wherein:
the global atmospheric light has the formula:
wherein k is a weight coefficient, Y (x) is a luminance map at a pixel point x in the inverted image,
the values of R channel, G channel and B channel at the position of pixel point x in the reverse image, A0Is an initial global atmospheric light; a. theCThe method comprises the following steps of forming a matrix by initial global atmospheric light in three RGB channels, wherein C is one of an R channel, a G channel and a B channel;
the transmission value is calculated as:
where t (x) is the transmittance value, ω is the custom tuning parameter, Ω (x) is the neighborhood centered on pixel x, y is the pixel in the neighborhood of pixel x,
the value of the C channel at the position of the pixel point y in the reverse image is obtained;
(3) and substituting the obtained global atmospheric light and transmittance into the following model function to obtain a restored scene light image, wherein the model function is as follows:
in the formula (I), the compound is shown in the specification,
is a scene light image;
(4) using formulasLight imaging a scene
Is subjected to inversion to obtain
The image is the enhanced face image;
the transmittance value t (x) can be corrected by the following formula to obtain a corrected transmittance value, and the calculation formula of the corrected transmittance value t' (x) is as follows:
2. a robot as claimed in claim 1, wherein the image capturing device is a CCD camera.
3. The robot of claim 1, wherein the alarm device comprises a single chip microcomputer, a voice player and a warning light.
4. The robot according to claim 1, wherein the matching of the feature vector of the face image with the feature vector of the face image of the dangerous person pre-stored in the memory is performed, and if the matching result is consistent, a control command is generated and sent to the alarm device, specifically: the feature vector of the face image obtained by the image processor
And the characteristic vector of the pre-stored face image with dangerous figuresPerforming a match if the feature vector
And the pre-stored characteristic vector of the face image with dangerous figureSatisfy the requirement ofThe person in the acquired face image is dangerous, otherwise, the person in the acquired face image is not dangerous, if the judgment result is that the person is dangerous, a control instruction is generated to the alarm device, wherein,
for the feature vectors of the face image processed by the image processor,
the feature vectors of the pre-stored face images of the dangerous figures are represented, and delta is a self-defined similarity factor.
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