CN109034129A - A kind of robot with face identification functions - Google Patents
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- CN109034129A CN109034129A CN201811012807.1A CN201811012807A CN109034129A CN 109034129 A CN109034129 A CN 109034129A CN 201811012807 A CN201811012807 A CN 201811012807A CN 109034129 A CN109034129 A CN 109034129A
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
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- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
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Abstract
The invention discloses a kind of robot with face identification functions, which includes: robot body and the image collecting device, image processor, microprocessor, warning device and the memory that are arranged on the robot body.The present invention passes through image acquisition device facial image, and facial image is handled, and extract the feature vector of the characteristic information of characterization facial image, and the feature vector of the facial image with risk personage that the feature vector of facial image by obtaining processing and memory prestore matches, if matching result is consistent, generates control instruction and be sent to warning device;Warning device receives the control instruction of microprocessor, carries out voice broadcast and triggering warning lamp.The robot can accurately identify the facial image of acquisition, have the advantages that high reliablity, improve and it is at low cost.
Description
Technical field
The present invention relates to robot control fields, and in particular to a kind of robot based on face identification functions.
Background technique
Face recognition technology has been widely used in important organ of country and social safety-security area tool, with other human-body biologicals
Feature identification technique is compared, the good concealment of face recognition technology, is the technological means that current International Terrorism security protection is most paid attention to.Separately
Outside, face recognition technology can also be applied to the video frequency searching and media production aspect of multimedia database.In recent years, with
The development of computer hardware and software, the function of robot are also stepping up, and clean robot, security robot etc. have
The robot of various functions gradually replaces the mankind, takes part in more more important task.But that develops now is used for recognition of face
Robot system there is also many loopholes, reliability is not high, system is incomplete and it is at high cost the disadvantages of.
Summary of the invention
In view of the above-mentioned problems, the present invention provides a kind of robot with face identification functions.
The purpose of the present invention is realized using following technical scheme:
A kind of robot with face identification functions, the robot include: robot body and are arranged in the machine
Image collecting device, image processor, microprocessor, warning device and memory on human body.
Described image acquisition device, for acquiring facial image, and be sent to described image processor to facial image into
Row processing;Described image processor for handling the facial image received, and extracts the feature of the facial image
Information obtains the feature vector of the facial image;The microprocessor, for by the feature vector of the facial image and institute
The feature vector for stating the facial image with risk personage that memory prestores matches, if matching result is consistent, gives birth to
The warning device is sent at control instruction;The warning device is carried out for receiving the control instruction of the microprocessor
Voice broadcast and triggering warning lamp;The memory, for storing the preset facial image with risk personage
Feature vector.
The invention has the benefit that the present invention is by image acquisition device facial image, and to facial image into
Row processing, and the feature vector of the characteristic information of characterization facial image is extracted, and the spy by the way that obtained facial image will be handled
Sign vector is matched with the feature vector for the facial image with risk personage that memory prestores, if matching result one
It causes, then generates control instruction and be sent to warning device;Warning device receive microprocessor control instruction, carry out voice broadcast and
Trigger warning lamp.The robot can accurately identify the facial image of acquisition, have high reliablity, perfect and cost
Low advantage.
Detailed description of the invention
The present invention will be further described with reference to the accompanying drawings, but the embodiment in attached drawing is not constituted to any limit of the invention
System, for those of ordinary skill in the art, without creative efforts, can also obtain according to the following drawings
Other attached drawings.
Fig. 1 is structure chart of the invention;
Fig. 2 is the frame construction drawing of image processor of the present invention;
Fig. 3 is the frame construction drawing of warning device of the present invention.
Appended drawing reference: image collecting device 1;Image processor 2;Microprocessor 3;Warning device 4;Memory 5;Image is pre-
Processing module 6;Image segmentation module 7;Characteristic extracting module 8;Image denoising unit 61;Image enhancing unit 62;Single-chip microcontroller 41;
Speech player 42;Warning lamp 43.
Specific embodiment
The invention will be further described with the following Examples.
Referring to Fig. 1, a kind of robot with face identification functions, the robot includes: that robot body and setting exist
Image collecting device 1, image processor 2, microprocessor 3, warning device 4 and memory 5 on the robot body.
Described image acquisition device 1, for acquiring facial image, and be sent to described image processor to facial image into
Row processing;
Described image processor 2 for handling the facial image received, and extracts the spy of the facial image
Reference breath, obtains the feature vector of the facial image;
The microprocessor 3 has danger for prestore the feature vector of the facial image and the memory 5
The feature vector of the facial image of property personage matches, if matching result is consistent, generates control instruction and is sent to the report
Alarm device 4;
The warning device 4 carries out voice broadcast and triggering warning for receiving the control instruction of the microprocessor 3
Lamp;
The memory 5, for storing the feature vector of the preset facial image with risk personage.
The utility model has the advantages that the present invention passes through image acquisition device facial image, and facial image is handled, and mentions
Take the feature vector of the characteristic information of characterization facial image, and feature vector and storage by the way that obtained facial image will be handled
The feature vector for the facial image with risk personage that device prestores matches, if matching result is consistent, generates control
Instruction is sent to warning device;Warning device receives the control instruction of microprocessor, carries out voice broadcast and triggering warning lamp.It should
Robot can accurately identify the facial image of acquisition, have the advantages that high reliablity, improve and it is at low cost.
Preferably, described image acquisition device 1 is CCD camera.
Preferably, referring to fig. 2, described image processor 2 includes image pre-processing module 6, image segmentation module 7 and feature
Extraction module 8.
Described image preprocessing module 6, for being pre-processed to the facial image;Described image divides module 7, uses
It is split in pretreated facial image;The characteristic extracting module 8, for being extracted from the facial image after segmentation
The characteristic information of the facial image obtains the feature vector of the facial image.
Preferably, described image preprocessing module 6 includes image denoising unit 61 and image enhancing unit 62.
Described image denoises unit 61, for removing the random noise in the facial image;Described image enhancement unit
62, for carrying out enhancing processing to the facial image after denoising.
Preferably, referring to Fig. 3, warning device 4 includes single-chip microcontroller 41, speech player 42 and warning lamp 43.
Preferably, the random noise in the removal facial image, specifically:
(1) J layers of wavelet decomposition are carried out to the facial image using wavelet transformation, obtains one group of wavelet coefficient z={ z1,
z2…zQ, Q is wavelet coefficient number;
(2) wavelet coefficient z is handled using threshold value, wherein thresholding functions are as follows:
In formula, z is the wavelet coefficient before denoising, and z ' is the wavelet coefficient after denoising, λ1It is upper threshold, λ2It is under threshold value
Limit, and λ1、λ2Meet λ1=α λ2, 0 < α < 1;M, τ is regulatory factor, and m > 1, τ > 1, sgn (f) are sign function, when f is
When positive number, 1 is taken, when being negative, takes 0;
(3) z ' is reconstructed using wavelet inverse transformation, the facial image after being denoised.
The utility model has the advantages that noise-containing image is handled using thresholding functions, it can be effectively to noise-containing figure
As being filtered;According to λ1、λ2With the absolute difference of wavelet coefficient z, different threshold function tables is selected to handle wavelet coefficient, it can
The noise for adaptively removing facial image, retains the effective information of facial image;Actual acquisition to image in contain there are many
Noise, and by adjusting the size of regulatory factor m, adjustable threshold handles the waveform of function, makes it possible to go to the maximum extent
Except the noise in facial image.
Preferably, in the above-described embodiment, the bottom threshold value of the wavelet coefficient of jth layer is calculated using following formula:
In formula, λ2,jIt is the bottom threshold value of jth layer wavelet coefficient, J is the Decomposition order of wavelet transformation, and just=1,
2 ..., j ..., J, σQFor the estimate variance of Q wavelet coefficient, Q is the quantity of wavelet coefficient, σjFor estimating for jth layer wavelet coefficient
Count variance, DjFor the number of jth layer wavelet coefficient, σr,jEstimate variance for noise-free signal r in jth layer, k1、k2For weight because
Son, and meet k1+k2=1.
The utility model has the advantages that calculating separately the bottom threshold value of different decomposition layer using above-mentioned algorithm, and then each point will be obtained
The bottom threshold value for solving layer substitutes into thresholding functions, completes the denoising to facial image, which realizes to threshold
It is worth the automatic adjusument of lower limit value and upper threshold value, can be selected according to the actual conditions of each decomposition layer of wavelet transformation different
Bottom threshold value and bottom threshold value complete to avoid setting fixed threshold bring noise to the denoising process of facial image
Wavelet coefficient is retained, and to still remain much noise in the image after denoising, while also avoiding will be useful
Wavelet coefficient treats as noise information, and makes the target after denoising too smooth, has lost detailed information, improves the standard of denoising
Exactness, be conducive to the subsequent facial image to acquisition accurately identify and the confirmation of personnel identity.
Preferably, the facial image after described pair of denoising carries out enhancing processing, specifically:
(1) formula is utilizedFacial image after denoising is inverted, wherein
For denoising after facial image reverse image,For the facial image after denoising, C is any in image RGB color model
One Color Channel;
(2) the global atmosphere light and transmittance values of the reverse image are sought respectively, in which:
The formula of global atmosphere light are as follows:
AC=[A0 A0 A0]T
In formula, k is weight coefficient, and Y (x) is the luminance graph in reverse image at pixel x,The value in the channel R, the channel G, channel B respectively in reverse image at pixel x, A0It is initial
Global atmosphere light;ACFor the matrix that initial global atmosphere light is constituted in tri- channels RGB, C is the channel R, the channel G, in channel B
One of them;
The formula of transmittance values are as follows:
In formula, t (x) is transmittance values, and ω is customized adjusting parameter, and Ω (x) is the neighbour centered on pixel x
Domain, y are the pixels in pixel x neighborhood,For the value of the C-channel in reverse image at pixel y;
(3) the global atmosphere light and transmissivity that acquire are substituted into following pattern function, the field after being restored
Scape light image, wherein the pattern function are as follows:
In formula,For scene light image;
(4) formula is utilizedBy scene light imageIt is inverted, is obtainedAs enhanced facial image.
The utility model has the advantages that the facial image after denoising is inverted using above-mentioned algorithm, reverse image is obtained, and then right
Reverse image is handled, and scene light image is obtained, which can reduce in facial image due to light, mist, dust etc. pair
The influence for acquiring facial image clarity, can highlight the edge feature and minutia of facial image, so that enhanced people
The visual effect of face image is truer, can more reflect the color information and its texture information of facial image, also in order to subsequent
Facial image is accurately identified, in favor of the subsequent facial image to acquisition whether be risk personage accurate judgement.
Preferably, transmittance values t (x) is modified using following formula, obtains revised transmittance values, revised
The calculation formula of radiance rate value t ' (x) are as follows:
The utility model has the advantages that being modified using above formula to obtained transmittance values t (x), transmittance values t ' (x) can not only have
Effect increases the detailed information of target to be identified and is also able to maintain the spatial continuity of transmissivity, so that the scene image after restoring
With more smooth visual effect.
Preferably, the people with the risk personage feature vector of the facial image and the memory 5 prestored
The feature vector of face image is matched, if matching result is consistent, is generated control instruction and is sent to the warning device 4, tool
Body are as follows: the feature vector for the facial image for obtaining the processing of described image processor 2Have risk personage's with what is prestored
The feature vector of facial imageIt is matched, if described eigenvectorWith the security feature vectorMeetThe personage in facial image then acquired has risk, and the personage in facial image otherwise acquired does not have
Dangerous property, if it is determined that the result is that personage then generates control instruction to warning device 4 with risk, whereinFor the figure
As the feature vector of facial image that the processing of processor 2 obtains,For the spy of the facial image with risk personage prestored
Vector is levied, δ is the customized similarity factor.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of range is protected, although explaining in detail referring to preferred embodiment to the present invention, those skilled in the art are answered
Work as understanding, it can be with modification or equivalent replacement of the technical solution of the present invention are made, without departing from the reality of technical solution of the present invention
Matter and range.
Claims (7)
1. a kind of robot with face identification functions characterized by comprising robot body and setting are in the machine
Image collecting device, image processor, microprocessor, warning device and memory on human body;
Described image acquisition device, for acquiring facial image, and be sent to described image processor to facial image at
Reason;
Described image processor for handling the facial image received, and extracts the feature letter of the facial image
Breath, obtains the feature vector of the facial image;
The microprocessor has risk personage for prestore the feature vector of the facial image and the memory
The feature vector of facial image matched, if matching result is consistent, generates control instruction and be sent to the warning device;
The warning device carries out voice broadcast and triggering warning lamp for receiving the control instruction of the microprocessor;
The memory, for storing the feature vector of the preset facial image with risk personage.
2. robot according to claim 1, which is characterized in that described image acquisition device is CCD camera.
3. robot according to claim 1, which is characterized in that described image processor include image pre-processing module,
Image segmentation module and characteristic extracting module;
Described image preprocessing module, for being pre-processed to the facial image;
Described image divides module, for being split to pretreated facial image;
The characteristic extracting module obtains institute for extracting the characteristic information of the facial image from the facial image after segmentation
State the feature vector of facial image.
4. robot according to claim 3, which is characterized in that described image preprocessing module includes image denoising unit
And image enhancing unit;
Described image denoises unit, for removing the random noise in the facial image;
Described image enhancement unit, for carrying out enhancing processing to the facial image after denoising.
5. robot according to claim 4, which is characterized in that the warning device includes single-chip microcontroller, speech player
And warning lamp.
6. robot according to claim 5, which is characterized in that the random noise in the removal facial image, specifically
Are as follows:
(1) facial image is decomposed using wavelet transformation, obtains one group of wavelet coefficient z={ z1, z2…zQ, Q is small
Wave coefficient number;
(2) wavelet coefficient z is handled using threshold value, wherein thresholding functions are as follows:
In formula, z is the wavelet coefficient before denoising, and z ' is the wavelet coefficient after denoising, λ1It is upper threshold, λ2It is bottom threshold, and
λ1、λ2Meet λ1=α λ2, 0 < α < 1;M, τ is regulatory factor, and m > 1, τ > 1, sgn (f) are sign function, when f is positive number
When, 1 is taken, when being negative, takes 0;
(3) z ' is reconstructed using wavelet inverse transformation, the facial image after being denoised.
7. robot according to claim 6, which is characterized in that the feature vector by the facial image with it is described
The feature vector for the facial image with risk personage that memory 5 prestores matches, if matching result is consistent, generates
Control instruction is sent to the warning device 4, specifically: the feature for the facial image for obtaining the processing of described image processor 2
VectorWith the feature vector of the facial image with risk personage prestoredIt is matched, if described eigenvector
With the security feature vectorMeetThe personage in facial image then acquired has risk, otherwise acquires
Facial image in personage do not have risk, if it is determined that the result is that personage have risk, then generate control instruction to report
Alarm device, whereinFor the feature vector for the facial image that described image processor is handled,There is danger for what is prestored
Property personage facial image feature vector, δ be the customized similarity factor.
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Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116631038A (en) * | 2023-06-06 | 2023-08-22 | 湖南三湘银行股份有限公司 | Method and system for verifying identity of bank user based on image |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8036473B1 (en) * | 2006-01-17 | 2011-10-11 | Teradici Corporation | Pixel selective wavelet transform methods |
CN104243927A (en) * | 2014-09-27 | 2014-12-24 | 江阴延利汽车饰件股份有限公司 | Security robot control platform with automatic suspect recognizing function |
CN104700094A (en) * | 2015-03-31 | 2015-06-10 | 江苏久祥汽车电器集团有限公司 | Face recognition method and system for intelligent robot |
CN105931193A (en) * | 2016-04-01 | 2016-09-07 | 南京理工大学 | Night traffic block port image enhancement method based on dark channel prior |
CN106855940A (en) * | 2016-11-23 | 2017-06-16 | 河池学院 | A kind of face identification system based on robot |
US20170236002A1 (en) * | 2016-02-15 | 2017-08-17 | Electronics And Telecommunications Research Institute | Face recognition method |
CN107832696A (en) * | 2017-11-01 | 2018-03-23 | 广州供电局有限公司 | A kind of electric operating object in situ security feature identifying system |
-
2018
- 2018-08-31 CN CN201811012807.1A patent/CN109034129B/en active Active
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8036473B1 (en) * | 2006-01-17 | 2011-10-11 | Teradici Corporation | Pixel selective wavelet transform methods |
CN104243927A (en) * | 2014-09-27 | 2014-12-24 | 江阴延利汽车饰件股份有限公司 | Security robot control platform with automatic suspect recognizing function |
CN104700094A (en) * | 2015-03-31 | 2015-06-10 | 江苏久祥汽车电器集团有限公司 | Face recognition method and system for intelligent robot |
US20170236002A1 (en) * | 2016-02-15 | 2017-08-17 | Electronics And Telecommunications Research Institute | Face recognition method |
CN105931193A (en) * | 2016-04-01 | 2016-09-07 | 南京理工大学 | Night traffic block port image enhancement method based on dark channel prior |
CN106855940A (en) * | 2016-11-23 | 2017-06-16 | 河池学院 | A kind of face identification system based on robot |
CN107832696A (en) * | 2017-11-01 | 2018-03-23 | 广州供电局有限公司 | A kind of electric operating object in situ security feature identifying system |
Non-Patent Citations (4)
Title |
---|
GUANGHUI DENG 等: "A Wavelet Image Denoising Based on the New Threshold Function", 《2015 11TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS)》 * |
储鹏鹏: "基于小波变换的图像去噪方法研究", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
彭希璐: "低照度图像的增强去噪算法研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
高银 等: "基于TV模型的暗原色理论雾天图像复原算法", 《中国激光》 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116631038A (en) * | 2023-06-06 | 2023-08-22 | 湖南三湘银行股份有限公司 | Method and system for verifying identity of bank user based on image |
CN116631038B (en) * | 2023-06-06 | 2024-05-28 | 湖南三湘银行股份有限公司 | Method and system for verifying identity of bank user based on image |
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