CN106845460A - A kind of intelligent domestic system based on recognition of face - Google Patents
A kind of intelligent domestic system based on recognition of face Download PDFInfo
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- G—PHYSICS
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- G07C9/00—Individual registration on entry or exit
- G07C9/00174—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys
- G07C9/00563—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys using personal physical data of the operator, e.g. finger prints, retinal images, voicepatterns
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- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/211—Selection of the most significant subset of features
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- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
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- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C9/00—Individual registration on entry or exit
- G07C9/00174—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys
- G07C9/00571—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys operated by interacting with a central unit
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Abstract
The invention provides a kind of intelligent domestic system based on recognition of face, including recognition of face subsystem, gate inhibition's subsystem, control centre, smart home and mobile terminal, the recognition of face subsystem, gate inhibition's subsystem, smart home and mobile terminal are all connected with control centre；The recognition of face subsystem is used to obtain facial image and carry out recognition of face to facial image；Control centre access control subsystem in recognition of face success releases gate inhibition and carries out corresponding operating to smart home according to the parameter preset of user, facial image is sent to mobile terminal by network when recognition of face fails and informs user, user may be selected to send instruction opening gate by mobile terminal.The present invention can be communicated, it is ensured that the safety of whole system and family after can not recognizing human face data when stranger enters identification range with the mobile terminal of user.
Description
Technical field
The present invention relates to Smart Home technical field, and in particular to a kind of intelligent domestic system based on recognition of face.
Background technology
Smart home utilizes advanced computer technology, the network communications technology and comprehensive wiring technology, will be with life staying idle at home
Relevant each subsystem is organically combined together, and by managing the life style of optimization people as a whole, helps people effective
Ground arranges the time, strengthens the security of life staying idle at home, and even various energy expenditures save fund.Intelligent family in correlation technique
The ordinary sensors technologies such as products application such as infrared sensing are occupied, security aspect has great deficiency.
The content of the invention
Regarding to the issue above, the present invention provides a kind of intelligent domestic system based on recognition of face.
The purpose of the present invention is realized using following technical scheme：
There is provided a kind of intelligent domestic system based on recognition of face, including recognition of face subsystem, gate inhibition's subsystem, control
Center processed, smart home and mobile terminal, the recognition of face subsystem, gate inhibition's subsystem, smart home and mobile terminal are all
It is connected with control centre；The recognition of face subsystem is used to obtain facial image and carry out recognition of face to facial image；Institute
State control centre's access control subsystem in recognition of face success and release gate inhibition and according to the parameter preset of user to intelligent family
Residence carries out corresponding operating, and facial image is sent into mobile terminal by network when recognition of face fails informs user, user
May be selected to send instruction opening gate by mobile terminal.
Beneficial effects of the present invention are：Parameter preset according to user carries out corresponding operating to smart home so that user
Domestic environment can be adjusted correspondingly according to personal preference's custom, can more embody the intellectuality of system；The face of setting is known
Small pin for the case system can be led to after can not recognizing human face data when stranger enters identification range with the mobile terminal of user
Letter, it is ensured that the safety of whole system and family.
Brief description of the drawings
Using accompanying drawing, the invention will be further described, but embodiment in accompanying drawing is not constituted to any limit of the invention
System, for one of ordinary skill in the art, on the premise of not paying creative work, can also obtain according to the following drawings
Other accompanying drawings.
Fig. 1 structure connection block diagrams of the invention；
Fig. 2 is the structure connection block diagram of face recognition subsystem of the present invention.
Reference：
Recognition of face subsystem 1, gate inhibition's subsystem 2, control centre 3, smart home 4, mobile terminal 5, facial image are obtained
Modulus block 10, facial image identification module 20.
Specific embodiment
The invention will be further described with the following Examples.
Referring to Fig. 1, a kind of intelligent domestic system based on recognition of face is present embodiments provided, the system is known including face
Small pin for the case system 1, gate inhibition's subsystem 2, control centre 3, smart home 4 and mobile terminal 5, the recognition of face subsystem 1, gate inhibition
Subsystem 2, smart home 4 and mobile terminal 5 are all connected with control centre 3；The recognition of face subsystem 1 is used to obtain face
Image simultaneously carries out recognition of face to facial image；The access control subsystem 2 in recognition of face success of the control centre 3 is released
Gate inhibition simultaneously carries out corresponding operating according to the parameter preset of user to smart home 4, leads to facial image when recognition of face fails
Cross network and be sent to mobile terminal 5 and inform user, user may be selected to send instruction opening gate by mobile terminal 5.
Preferably, gate inhibition's subsystem 2 includes door, door lock, the controller for controlling door lock to open, the control
Device is connected with control centre 3.
Preferably, the smart home 4 includes lamp, water heater, air-conditioning.
The above embodiment of the present invention, the parameter preset according to user carries out corresponding operating to smart home 4 so that Yong Huneng
Domestic environment is adjusted correspondingly according to personal preference's custom, can more embody the intellectuality of system；The recognition of face of setting
Subsystem 1 can be led to after can not recognizing human face data when stranger enters identification range with the mobile terminal 5 of user
Letter, it is ensured that the safety of whole system and family.
Preferably, as shown in Fig. 2 the recognition of face subsystem 1 includes the He of facial image acquisition module 10 being connected
Facial image identification module 20；The facial image acquisition module 10 is used to obtain multiple facial images to be identified, and from obtaining
The maximum facial image of picture quality degree is filtered out in the facial image for taking as the optimal facial image for recognition of face；Institute
Facial image identification module 20 is stated for being identified to optimal facial image, and face recognition result is exported to control centre 3；
Wherein, the computing formula of image quality degree is：
In formula, ZiIt is i-th picture quality degree of facial image, ρ in multiple imagesiIt is i-th face figure in multiple images
The average gray value of the setting regions of picture, ρ is the gray value threshold value set according to actual conditions, viFor i-th in multiple images
The edge sharpness of facial image, v is the edge sharpness threshold value set according to actual conditions,It is the average gray value of multiple images,It is the average edge acutance of multiple images, m is the quantity of multiple images obtained from camera system；aiIt is i-th facial image
The ratio of this facial image shared by middle face, α is the proportion threshold value of setting, works as αiDuring-α >=0, f (αi- α)=1, αi-α<0
When, f (αi- α)=0.
In this preferred embodiment, filtering out suitable facial image according to customized picture quality degree computing formula is carried out
Recognition of face detects that can greatly save system memory space, the speed of raising recognition of face detection is calculated in picture quality degree
In formula, it is considered to facial image proportion, edge sharpness and gray value factor, it is to avoid carry out picture quality by single features
The limitation of evaluation, can relatively accurately screen mass image high carries out recognition of face, simplifies the fortune of optical sieving
Calculation amount, further increases the efficiency of optical sieving.
Preferably, it is described that optimal facial image is identified, including：
(1) N facial image is chosen in the face database that recognition of face subsystem 1 builds in advance and builds training sample
Collection X=[X1,X2,…,XN], the facial image that will be filtered out carries out filtering pretreatment as test sample Y to training sample set,
Retain the expression on test sample and the larger training sample of classification influence, and optimal training is built using the training sample for retaining
Sample set；
(2) R block is averagely divided into per pair facial image by what optimal training sample was concentrated, then the optimal training sample set
It is divided into R sub- sample set Ap, p=1 ..., R, each subsample collection constitutes by p-th piece of every secondary facial image；
(3) test sample is divided into R block, i.e. Y=[Yp, p=1 ..., R], to optimal training sample set and test
Sample carries out block weighting according to the following equation：
In formula, vpThe sparse residual error average of p-th piece of face images, v are concentrated for optimal training sample1、v2To set
Fixed threshold residual value, v1<v2, f (vp) it is decision function, work as vp<v1When, f (vp)=1, works as vp>v2When, f (vp)=0；
In formula, μpThe between class distance variance and the ratio of inter- object distance variance concentrated for optimal training sample, μ1、μ2To set
Fixed differentiation degree threshold value, μ1<μ2, f (μp) it is decision function, work as μp<μ2When, f (μp)=0, works as μp>μ1When, f (μp)=1；
Wherein, vpProcess of asking for be：For any one secondary facial image that optimal training sample is concentrated, with the face figure
Residual image as beyond carries out rarefaction representation to the sample, obtains each piece of the facial image of sparse residual error, then calculates
Go out p-th piece of face images of sparse residual error average；
(4) test sample after being weighted with the optimal training sample set pair after weighting carries out rarefaction representation, calculates wherein every
The reconstructed residual of individual class, most test sample is categorized as the corresponding class of minimal reconstruction residual error at last.
In this preferred embodiment, the facial image that test sample and optimal training sample are concentrated is divided into formed objects
Block, can preferably capture the information of more identification, to optimal training sample set and test specimens during recognition detection
This carries out block weighting according to above-mentioned formula, can more accurately select and block block and identification block, and reduction blocks position to people
The influence of face recognition performance such that it is able to improve the discrimination of facial image, improves the security protection effect of intelligent domestic system.
Preferably, it is described that filtering pretreatment is carried out to training sample set, retain the expression and classification influence on test sample
Larger training sample, and optimal training sample set is built using the training sample for retaining, specifically include：
(1) linear expression is carried out to test sample Y using training sample set X, calculates each training sample in training sample set X
The expression coefficient S=[S of vector1,S2,…,SN]T, wherein, the computing formula for representing coefficient S is：
S=(XTX+ξI)-1XTY
In formula, I is unit matrix, and ξ is the coefficient of setting；
(2) total M class in training sample set X is set, has n in j-th classjIndividual training sample, calculates the reconstruct of each class
Residual error is：
In formula, EjIt is j-th reconstructed residual of class, XjRepresent j-th training sample set of class, SkRepresent in j-th class
The corresponding expression coefficient of k training sample；
(3) the corresponding class of preceding m minimal reconstruction residual error alternately class is chosen, neighbour's dictionary D is built with the m alternative class
=[D1,D2,…,Dm], Dj(j=1 ..., m) represents j-th training sample set of class in alternative class, with the alternative class to test
Sample Y carries out linear expression, calculates the corresponding expression coefficient of each alternative class in neighbour's dictionary D：
S '=(DTD+ξI)-1DTY
In formula, S ' represents the corresponding expression coefficient of alternative class, S '=[S1′,S2′,…,Sm'], Sj(j=1 ..., m) represent
The corresponding expression coefficient of j-th class in alternative class；
(4) building optimal training sample set using the training sample for retaining is：
In formula,Represent j-th k-th training sample of the training sample concentration of class.
This preferred embodiment, expression and the larger training sample of classification influence on test sample are retained using aforesaid way
This, reduces training sample amount, reduces computation complexity, so as to shorten the time of recognition of face, improves smart home
The efficiency of security protection；Represent that coefficient is weighted to the training sample of the alternative class using alternative class is corresponding, weights are more big then right
Answer training sample stronger to the expression ability of test sample, therefore the optimal training sample set for building preferably can be tested approximately
Sample.
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 scope is protected, although being explained to the present invention with reference to preferred embodiment, one of ordinary skill in the art should
Work as understanding, technical scheme can be modified or equivalent, without deviating from the reality of technical solution of the present invention
Matter and scope.
Claims (6)
1. a kind of intelligent domestic system based on recognition of face, it is characterized in that, including recognition of face subsystem, gate inhibition's subsystem,
Control centre, smart home and mobile terminal, the recognition of face subsystem, gate inhibition's subsystem, smart home and mobile terminal
All it is connected with control centre；The recognition of face subsystem is used to obtain facial image and carry out recognition of face to facial image；
Control centre access control subsystem in recognition of face success releases gate inhibition and according to the parameter preset of user to intelligence
Household carries out corresponding operating, and facial image is sent into mobile terminal by network when recognition of face fails informs user, uses
Family may be selected to send instruction opening gate by mobile terminal.
2. a kind of intelligent domestic system based on recognition of face according to claim 1, it is characterized in that, gate inhibition's subsystem
System includes door, door lock, the controller for controlling door lock to open, and the controller is connected with control centre.
3. a kind of intelligent domestic system based on recognition of face according to claim 1, it is characterized in that, the smart home
Including lamp, water heater, air-conditioning.
4. a kind of intelligent domestic system based on recognition of face according to claim 1, it is characterized in that, the recognition of face
Subsystem includes the facial image acquisition module and facial image identification module that are connected；The facial image acquisition module is used for
Multiple facial images to be identified are obtained, and the maximum facial image of picture quality degree is filtered out from the facial image for obtaining and made
It is the optimal facial image for recognition of face；The facial image identification module is used to be identified optimal facial image,
And export face recognition result to control centre.
5. a kind of intelligent domestic system based on recognition of face according to claim 4, it is characterized in that, define described image
The computing formula of quality degree is：
In formula, ZiIt is i-th picture quality degree of facial image in multiple images, Z is the picture quality degree threshold value of setting, ρiFor
I-th average gray value of the setting regions of facial image in multiple images, ρ is the gray value threshold set according to actual conditions
Value, viIt is i-th edge sharpness of facial image in multiple images, v is the edge sharpness threshold value set according to actual conditions,
It is the average gray value of multiple images,It is the average edge acutance of multiple images, m is multiple images obtained from camera system
Quantity；αiIt is the ratio of this facial image shared by face in i-th facial image, α is the proportion threshold value of setting, works as αi-α
When >=0, f (αi- α)=1, αi-α<When 0, f (αi- α)=0.
6. a kind of intelligent domestic system based on recognition of face according to claim 5, it is characterized in that, it is described to optimal people
Face image is identified, including：
(1) N facial image is chosen in the face database that recognition of face subsystem builds in advance and builds training sample set X=
[X1,X2,…,XN], the facial image that will be filtered out carries out filtering pretreatment to training sample set as test sample Y, retains
Expression and the larger training sample of classification influence on test sample, and build optimal training sample using the training sample for retaining
Collection；
(2) R block is averagely divided into per pair facial image by what optimal training sample was concentrated, then the optimal training sample set is divided
It is cut into R sub- sample set Ap, p=1 ..., R, each subsample collection constitutes by p-th piece of every secondary facial image；
(3) test sample is divided into R block, i.e. Y=[Yp, p=1 ..., R], to optimal training sample set and test sample
Block weighting is carried out according to the following equation：
In formula, vpThe sparse residual error average of p-th piece of face images, v are concentrated for optimal training sample1、v2It is setting
Threshold residual value, v1<v2, f (vp) it is decision function, work as vp<v1When, f (vp)=1, works as vp>v2When, f (vp)=0；
In formula, μpThe between class distance variance and the ratio of inter- object distance variance concentrated for optimal training sample, μ1、μ2It is setting
Differentiation degree threshold value, μ1<μ2, f (μp) it is decision function, work as μp<μ2When, f (μp)=0, works as μp>μ1When, f (μp)=1；
(4) test sample after being weighted with the optimal training sample set pair after weighting carries out rarefaction representation, calculates wherein each class
Reconstructed residual, most test sample is categorized as the corresponding class of minimal reconstruction residual error at last.
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| CN108921991A (en) * | 2018-06-26 | 2018-11-30 | 佛山市中格威电子有限公司 | It is a kind of based on solar powered door-locking system |
| CN108961497A (en) * | 2018-06-26 | 2018-12-07 | 佛山市中格威电子有限公司 | A kind of door-locking system with warning function |
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| CN109658563A (en) * | 2018-12-12 | 2019-04-19 | 广州小楠科技有限公司 | A kind of effective intelligent access control system |
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