CN105701457A - DC electromagnetic relay device based on face identification control and control method thereof - Google Patents
DC electromagnetic relay device based on face identification control and control method thereof Download PDFInfo
- Publication number
- CN105701457A CN105701457A CN201610009547.7A CN201610009547A CN105701457A CN 105701457 A CN105701457 A CN 105701457A CN 201610009547 A CN201610009547 A CN 201610009547A CN 105701457 A CN105701457 A CN 105701457A
- Authority
- CN
- China
- Prior art keywords
- face
- electromagnetic relay
- matrix
- sample
- image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06V40/161—Detection; Localisation; Normalisation
- G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06V40/172—Classification, e.g. identification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/50—Maintenance of biometric data or enrolment thereof
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01H—ELECTRIC SWITCHES; RELAYS; SELECTORS; EMERGENCY PROTECTIVE DEVICES
- H01H47/00—Circuit arrangements not adapted to a particular application of the relay and designed to obtain desired operating characteristics or to provide energising current
- H01H47/002—Monitoring or fail-safe circuits
- H01H47/004—Monitoring or fail-safe circuits using plural redundant serial connected relay operated contacts in controlled circuit
- H01H47/005—Safety control circuits therefor, e.g. chain of relays mutually monitoring each other
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Oral & Maxillofacial Surgery (AREA)
- General Health & Medical Sciences (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
The present invention discloses a DC electromagnetic relay device based on face identification control. The DC electromagnetic relay device based on face identification control comprises a face identification module, a power supply switch controller, a voice broadcasting device, a DC electromagnetic relay and a liquid crystal display, the face identification module is connected with the liquid crystal display, the power supply switch controller and the voice broadcasting device separately, and the power supply switch controller is connected with a low-voltage control circuit of the DC electromagnetic relay. According to the present invention, by a method of identifying the real-time face images of the authorized users, the purpose of controlling the on-off of a DC electromagnetic relay circuit is achieved, and the electrical equipment can be controlled simply, rapidly, safely and intelligently. The present invention also discloses a control method of the DC electromagnetic relay device based on face identification control. The method comprises the concrete steps of 1 carrying out the pre-processing on the acquired face images; 2 carrying out the pre-processing on the feature face vectors outputted by a strong classifier and then utilizing an Angle Contex matrix to carry out the similar face matching on the feature vectors.
Description
Technical field
The invention belongs to electric control system technical field, relate to a kind of direct current electromagnetic relay device controlled based on recognition of face, the invention still further relates to the control method of this direct current electromagnetic relay device。
Background technology
Along with information technology permeates to service for life each side, industry internet, cloud computing, continuous fusion application between big data and new technology of Internet, the rise adding Internet of Things promotes industry to move towards intelligent, and the demand of electrical equipment is also progressively tended to intelligentized side's exhibition by power consumer。Traditional electromagnetic relay is commonly used in automatic control circuit, uses less electric current and Control of Voltage to have the electrical equipment of larger current and high voltage。If being used for controlling the special equipment of electric aspect or high-risk equipment by traditional electromagnetic relay switch, it is easy to cause the illegal operation of layman, cause the infringement to electrical equipment, it could even be possible to make whole electrical system paralyse。Traditional electromagnetic relay switch is for controlling special equipment or the safe and reliable problem of high-risk equipment existence。
Summary of the invention
It is an object of the invention to provide a kind of direct current electromagnetic relay device controlled based on recognition of face, can by identifying that the method for authorized user's real-time face image reaches to control the purpose of the break-make of direct current electromagnetic relay circuit and control electrical equipment that can be simple, quick, safe and reliable, intelligent。
The technical solution used in the present invention is, a kind of direct current electromagnetic relay device controlled based on recognition of face, including face recognition module, power supply switch controller, voice announcer, direct current electromagnetic relay, liquid crystal display, face recognition module respectively with liquid crystal display, on and off switch controls module and voice announcer connects, on and off switch controls module and is connected with the low-voltage control circuit of direct current electromagnetic relay, the facial image of user can be demonstrated on a liquid crystal display in real time when face recognition module recognizes user's face, if on and off switch control module can export the low-voltage control circuit of 24V DC voltage supply direct current electromagnetic relay after face recognition module identification success。
It is another object of the invention to provide the control method of the above-mentioned direct current electromagnetic relay device controlled based on recognition of face。
Another technical scheme of the present invention is, based on the control method of the direct current electromagnetic relay device that recognition of face controls, specifically implements according to following steps:
Step 1, carries out pretreatment to the facial image collected, and adopts Haar wavelet basis function to calculate rectangular characteristic the structural classification device of image, face is detected,
Step 2, after eigenface vector R (D) pretreatment that strong classifier is exported, characteristic vector R (D) is carried out similar face coupling by recycling AngleContex matrix, improve recognition of face precision, set up the face database of authorized user in advance, then the face characteristic extracted utilize AngleContex matrix be calculated it and the similarity authorizing face database set up, it may be judged whether for authorized user's face。
The feature of the present invention also resides in,
Comprising the concrete steps that of step 1:
Step 1.1, rectangular characteristic value is all grey scale pixel value sums and the difference of all grey scale pixel value sums in white portion in black region, have employed a kind of rectangular characteristic being similar to Haar wavelet basis function, rectangular characteristic value in detection window, rectangular characteristic value can be calculated by the gray value of integral image pixel, in integral image, coordinate is that the gray value of (X, Y) pixel is equal to all grey scale pixel values in correspondence position upper left side cumulative on original image;
The computing formula of integral image is as follows:
I (X, Y) is the image after integration, i (x, y) for original image,
Step 1.2, trying to achieve the integral image I (X, Y) of pixel as a sequence, forms the integrogram matrix of this figure, finds the set D of matrix exgenvalue according to integrogram matrix,
D={a1,a2,a3,a4,........am}(1-2)
Step 1.3, in rectangular window, builds multistage classifier and obtains the classification function of optimal threshold, matrix exgenvalue is carried out best classification,
Step 1.4, utilizes binarization of gray value and integral projection method to carry out eye location in the human face region detected;With eyes coordinates for datum mark, two eyes are made to be in same level line by translating, rotating, by scale transformation, facial image is carried out size to unify, facial image is carried out grayscale normalization, average and the mean square deviation of facial contour inner region gray scale is calculated by histogram equalization, image smoothing, gray scale normalization etc., view picture facial image is carried out greyscale transformation, highlights face key facial features。
Concretely comprising the following steps of step 1.3:
Step 1.3.1, constructs training sample set L:
L={ (D1,K1),(D2,K2),......(Dm,Km)}(1-3)
Wherein D represents the matrix characteristic vector of sample, and K represents training result, and m is training total number of samples, m=1,2,3 ... .n;
Step 1.3.2, the initial probability distribution of sample L is sample weights:
N represents number of training, and the iterations of grader is t=1,2,3,4.......T, and T is any positive integer, calculates the weights of normalization sample:
Wherein j=1,2,3 ... .n;
Step 1.3.3, calls weak learning algorithm to the matrix characteristic vector D of each sample, trains a Weak Classifier Hj(Dm), and calculate the error rate ξ of this graderj
Wherein θjIt is the threshold value of Weak Classifier, pjFor the biasing of grader, the direction of majorization inequality, fjFor the eigenvalue of eigenmatrix,
Step 1.3.4, selects have minimal error rate ξtCorresponding Weak Classifier Ht(Dm) as optimal classification device, namely
ξt=min ξj(1-7)
Step 1.3.5, readjusting sample weights according to optimal classification device is:
Wherein m-th sample correct judgment then emOtherwise=0 em=1. so can increase grader error sample weight, reduce the weight of correct sample of classifying, and during next iteration, the selection of Weak Classifier will pay attention to the sample of last mistake classification error,
βtThe coefficient of sample weights is readjusted for optimal classification device;
Step 1.3.6, generates strong classifier:
IfThen grader output result is 1, then it is assumed that this detection window is likely human face region, and R (D) is the eigenface vector of output;
The multistage classifier utilizing structure can get rid of most of non-face region, detects almost possible human face region;If every first-level class device is output as 1, then it is assumed that this detection window is likely human face region, the window of detection is input to next stage grader and proceeds to judge, otherwise, in this level, detection window is excluded as non-face region。
Step 2 particularly as follows:
When extracting face characteristic, it is identical for belonging to the human face characteristic point distance with a part, and the difference of face is mainly manifested in the difference of face each several part contour line curvature, angle, can with 8 degree of divisions as face characteristic average distance, by 45 regions of plane division being zero with datum mark, generate AngleContex matrix, calculate the number of other discrete points falling into regional
Specifically implement according to following steps:
Step 2.1, on the eigenface that strong classifier obtains, each datum mark is selected for 9 character references points with nose, corners of the mouth midpoint, point, left eyebrow, left eye angle, the left corners of the mouth, right eyebrow, right eye angle, the right corners of the mouth, 45 regions (bin) are chosen with 8 degree of angles for dividing interval, thus can obtain 9*45 AngleContex matrix
Step 2.2, utilizes angular histogram to intersect method and the similarity of 9*45 the AngleContex matrix calculus face to be matched obtained and targeted customer's face, and computing formula is as follows:
P (s, v) is the crossing distance of two characteristic vectors, and M (R, R') is the ratio of 9*45 overall area (bin) pixel shared by each zoning (bin) in two rectangular histograms,
Wherein, Rl,gRepresent the AngleContex matrix of face to be matched, R'l,gThe AngleContex matrix of expression targeted customer's face, l=1,2 ..., 9 represent 9 character references points;G=1,2,3,4,5,6 ..., 44,45 expressions choose 45 regions with 8 degree of angles for dividing interval;
Step 2.3, the value calculating M (R, R') between 0~1, can take M (R, R') maximum just for the similar face that matching degree is the highest,
Step 2.4, after when similar face, the match is successful, voice announcer 3 can report the voice of " identifying successfully ", and otherwise, voice announcer 3 can report the voice of " identifying mistake ",
Wherein human face similarity degree coupling, on the eigenface image obtained, with nose, corners of the mouth midpoint, point, left eyebrow, left eye angle, the left corners of the mouth, right eyebrow, right eye angle, the right corners of the mouth for 9 character references points, each datum mark selected on the eigenface image obtained through strong classifier, 45 regions (bin) are chosen with 8 degree of angles for dividing interval, thus can obtaining 9*45 AngleContex matrix, multizone divides and improves matching precision as far as possible。
The invention has the beneficial effects as follows, compared with existing system, implementing the equipment used in the present invention less, simple in construction, with low cost, feasibility and the safety of method are higher。The combination of face recognition technology and direct current electromagnetic relay can be made full use of, the effective special equipment controlled in electrical system or high-risk equipment, avoid the improper operation of disabled user, for industrial implementation intellectuality, there is long-range meaning, face recognition technology is adopted to use user to carry out identification electrical equipment, giving the control loop that user identity authority remotely controls direct current electromagnetic relay, controlling thus reaching authorized user's operation to special equipment or high-risk equipment。
Accompanying drawing explanation
Fig. 1 is the structural representation of a kind of direct current electromagnetic relay controlled based on recognition of face of the present invention;
Fig. 2 is a kind of based on the power on-off control circuit figure in the direct current electromagnetic relay of recognition of face control of the present invention;
Fig. 3 is a kind of flow chart based on the face recognition module recognition methods in the direct current electromagnetic relay of recognition of face control of the present invention。
In figure, 1. face recognition module, 2. power supply switch controller, 3. voice announcer, 4. direct current electromagnetic relay, 5. liquid crystal display。
Detailed description of the invention
Below in conjunction with accompanying drawing and specific embodiment, describe the present invention。
The invention provides a kind of direct current electromagnetic relay device controlled based on recognition of face, as it is shown in figure 1, include face recognition module 1, power supply switch controller 2, voice announcer 3, direct current electromagnetic relay 4, liquid crystal display 5。
Face recognition module 1 controls module 2 with liquid crystal display 5, on and off switch respectively and voice announcer 3 is connected, and on and off switch controls module 2 and is connected with the low-voltage control circuit of direct current electromagnetic relay 4。The facial image of user can be demonstrated on liquid crystal display 5 in real time, if face recognition module 1 identifies that after successfully, on and off switch control module 2 can export the low-voltage control circuit of 24V DC voltage supply direct current electromagnetic relay 4 when face recognition module 1 recognizes user's face。
As in figure 2 it is shown, recognition of face device utilizes camera collection facial image, it is possible to recognition of face device 1 is arranged on indoor, reach remotely control electromagnetic relay and gather video image purpose clearly。When user needs to open electrical equipment, before directly walking to the recognition of face device 1 of indoor, the facial information of active user is detected and positions by the photographic head on recognition of face device 1, uses user it will be clear that facial image on liquid crystal display 5。Utilize video capture circuit that the video signal of camera acquisition is converted into digital signal, and the digital picture after gathering is carried out subsequent treatment。
As shown in Figure 2, its work process is the power on-off control circuit figure of apparatus of the present invention, and after voice announcer 3 reports the voice of " identifying successfully ", power supply switch controller 2 can be exported high level that signal is 1 by recognition of face device 1,
Audion Q119013 in power supply switch controller will turn on, the grid so making the field effect transistor Q7 of enhancement mode P-channel is in negative potential, the source electrode of field effect transistor Q7 has the voltage of-24V under the DC voltage effect of 24V, operation principle according to field effect transistor Q7, make QX end, and export the low-voltage control circuit of 24V DC voltage conducting direct current electromagnetic relay 4, the armature of relay 4 is inhaled by electric magnet, after movable contact contacts with stationary contact simultaneously, authorized user can remote opening electrical equipment。Otherwise, voice announcer 3 reports the voice of " identifying mistake ", power supply switch controller 2 output low level, audion is in cut-off state, the grid of field effect transistor Q7 and source electrode both end voltage are 0, and field effect transistor Q7 is in cut-off state, and the low-voltage control circuit of direct current electromagnetic relay 4 will not be energized, armature remains static, and the electrical equipment of peripheral circuit fails normally-open。
A kind of control method of the direct current electromagnetic relay device controlled based on recognition of face, as it is shown on figure 3, specifically implement according to following steps:
Step 1, carries out pretreatment to the facial image collected, and adopts Haar wavelet basis function to calculate rectangular characteristic the structural classification device of image, face is detected, and concrete step is:
Step 1.1, rectangular characteristic value is all grey scale pixel value sums and the difference of all grey scale pixel value sums in white portion in black region, have employed a kind of rectangular characteristic being similar to Haar wavelet basis function, the rectangular characteristic value in detection window。Rectangular characteristic value can be calculated by the gray value of integral image pixel, and in integral image, coordinate is that the gray value of (X, Y) pixel is equal to all grey scale pixel values in correspondence position upper left side cumulative on original image;
The computing formula of integral image is as follows:
I (X, Y) is the image after integration, i (x, y) for original image,
Step 1.2, trying to achieve the integral image I (X, Y) of pixel as a sequence, forms the integrogram matrix of this figure, finds the set D of matrix exgenvalue according to integrogram matrix,
D={a1,a2,a3,a4,........am}(1-2)
Step 1.3, in rectangular window, builds multistage classifier and obtains the classification function of optimal threshold, matrix exgenvalue is carried out best classification, and specific algorithm is as follows:
Step 1.3.1, constructs training sample set L:
L={ (D1,K1),(D2,K2),......(Dm,Km)}(1-3)
Wherein D represents the matrix characteristic vector of sample, and K represents training result, and m is training total number of samples, m=1,2,3 ... .n;
Step 1.3.2, the initial probability distribution of sample L is sample weights:
N represents number of training, and the iterations of grader is t=1,2,3,4.......T, and T is any positive integer, calculates the weights of normalization sample:
Wherein j=1,2,3 ... .n;
Step 1.3.3, calls weak learning algorithm to the matrix characteristic vector D of each sample, trains a Weak Classifier Hj(Dm), and calculate the error rate ξ of this graderj
Wherein θjIt is the threshold value of Weak Classifier, pjFor the biasing of grader, the direction of majorization inequality, fjEigenvalue for eigenmatrix。
Step 1.3.4, selects have minimal error rate ξtCorresponding Weak Classifier Ht(Dm) as optimal classification device, namely
ξt=min ξj(1-7)
Step 1.3.5, readjusting sample weights according to optimal classification device is:
Wherein m-th sample correct judgment then emOtherwise=0 em=1. so can increase grader error sample weight, reduce the weight of correct sample of classifying, and during next iteration, the selection of Weak Classifier will pay attention to the sample of last mistake classification error。
βtThe coefficient of sample weights is readjusted for optimal classification device;
Step 1.3.6, generates strong classifier:
IfThen grader output result is 1, then it is assumed that this detection window is likely human face region, and R (D) is the eigenface vector of output;
The multistage classifier utilizing structure can get rid of most of non-face region, detects almost possible human face region;If every first-level class device is output as 1, then it is assumed that this detection window is likely human face region, the window of detection is input to next stage grader and proceeds to judge。Otherwise, in this level, detection window is excluded as non-face region;
Step 1.4, utilizes binarization of gray value and integral projection method to carry out eye location in the human face region detected;With eyes coordinates for datum mark, two eyes are made to be in same level line by translating, rotating, by scale transformation, facial image is carried out size to unify, facial image is carried out grayscale normalization, average and the mean square deviation of facial contour inner region gray scale is calculated by histogram equalization, image smoothing, gray scale normalization etc., view picture facial image is carried out greyscale transformation, highlights face key facial features;
Step 2, after eigenface vector R (D) pretreatment that strong classifier is exported, characteristic vector R (D) is carried out similar face coupling by recycling AngleContex matrix, improves recognition of face precision。Set up the face database of authorized user in advance, then the face characteristic extracted utilize AngleContex matrix be calculated it and the similarity authorizing face database set up, it may be judged whether for authorized user's face, concrete step is:
When extracting face characteristic, it is identical for belonging to the human face characteristic point distance with a part, and the difference of face is mainly manifested in the difference of face each several part contour line curvature, angle, can with 8 degree of divisions as face characteristic average distance, by 45 regions of plane division being zero with datum mark, generate AngleContex matrix, calculate the number of other discrete points falling into regional
Specific algorithm illustrates:
Step 2.1, on the eigenface that strong classifier obtains, each datum mark is selected for 9 character references points with nose, corners of the mouth midpoint, point, left eyebrow, left eye angle, the left corners of the mouth, right eyebrow, right eye angle, the right corners of the mouth, choose 45 regions (bin) with 8 degree of angles for dividing interval, thus can obtain 9*45 AngleContex matrix;
Step 2.2, utilizes angular histogram to intersect method and the similarity of 9*45 the AngleContex matrix calculus face to be matched obtained and targeted customer's face, and computing formula is as follows:
(s, v) is the crossing distance of two characteristic vectors to P, and M (R, R') is the ratio of 9*45 overall area (bin) pixel shared by each zoning (bin) in two rectangular histograms;
Wherein, Rl,gRepresent the AngleContex matrix of face to be matched, R'l,gThe AngleContex matrix of expression targeted customer's face, l=1,2 ..., 9 represent 9 character references points;G=1,2,3,4,5,6 ..., 44,45 expressions choose 45 regions with 8 degree of angles for dividing interval;
Step 2.3, the value calculating M (R, R') between 0~1, can take M (R, R') maximum just for similar face that matching degree is the highest;
Step 2.4, after when similar face, the match is successful, voice announcer 3 can report the voice of " identifying successfully ", and otherwise, voice announcer 3 can report the voice of " identifying mistake "。
Its feature of the control method of the present invention is mainly reflected in the following aspects:
1) setting up face database, the face characteristic of authorized user carries out registration typing, in order to improve the safety of face database, the data preserved in face database can only be checked by the user having permission and change。
2) use video camera that user is carried out face image collection, the image gathered is carried out Face datection and location, and can to given arbitrary image, it is determined that wherein whether there is face, and provide the information such as the position of face, size and state。
3) utilize video capture circuit that the video signal of camera acquisition is converted into digital signal, and the digital picture after gathering is carried out pretreatment。Mainly for the noise in image, illumination is not enough, size and angle is bad etc. reason causes gather after the not high problem of picture quality, adopt image enhaucament means carry out extracting to facial image feature and select。
4) carrying out recognition of face, be calculated processing by the face feature vector of acquisition and the characteristic vector in face database, find out the most close vector, obtain the maximum of result of calculation, being considered as it is the face to look for。
5) result that recognition of face goes out is authorized user's face, voice announcer can report out " identifying successfully " voice messaging, it is the high level of 1 that direct current electromagnetic relay control circuit will be exported signal by face recognition module, audion and field effect transistor in energy supply control module turn on, the control circuit of output 24V DC voltage supply direct current electromagnetic relay, after direct current electromagnetic relay energising, armature is inhaled by electric magnet, movable contact contacts with stationary contact simultaneously, and the electrical equipment of peripheral circuit just can authorized user start。
6) if the face that recognition result is undelegated user, voice announcer can report out " identifying mistake " voice messaging, it is the low level of 0 that direct current electromagnetic relay control circuit can be exported signal by face recognition module, audion is in cut-off state, the grid of field effect transistor and source electrode both end voltage are 0, field effect transistor is in cut-off state, and the control circuit of electromagnetic relay does not turn on, and the electrical equipment of peripheral circuit can not authorized user be started。
Compared with existing system, implementing the equipment used in the present invention less, simple in construction, with low cost, feasibility and the safety of method are higher。The combination of face recognition technology and direct current electromagnetic relay can be made full use of, the effective special equipment controlled in electrical system or high-risk equipment, avoid the improper operation of disabled user, for industrial implementation intellectuality, there is long-range meaning, face recognition technology is adopted to use user to carry out identification electrical equipment, giving the control loop that user identity authority remotely controls direct current electromagnetic relay, controlling thus reaching authorized user's operation to special equipment or high-risk equipment。
Claims (5)
1. the direct current electromagnetic relay device controlled based on recognition of face, it is characterized in that, including face recognition module (1), power supply switch controller (2), voice announcer (3), direct current electromagnetic relay (4), liquid crystal display (5)
Described face recognition module (1) respectively with liquid crystal display (5), on and off switch controls module (2) and voice announcer (3) connects, described on and off switch controls module (2) and is connected with the low-voltage control circuit of direct current electromagnetic relay (4), the facial image of user can be demonstrated on liquid crystal display (5) in real time when face recognition module (1) recognizes user's face, if face recognition module (1) identifies that after successfully, on and off switch control module (2) can export the low-voltage control circuit of 24V DC voltage supply direct current electromagnetic relay (4)。
2. the control method of the direct current electromagnetic relay device controlled based on recognition of face of a claim 1, it is characterised in that specifically implement according to following steps:
Step 1, carries out pretreatment to the facial image collected, and adopts Haar wavelet basis function to calculate rectangular characteristic the structural classification device of image, face is detected,
Step 2, after eigenface vector R (D) pretreatment that strong classifier is exported, characteristic vector R (D) is carried out similar face coupling by recycling AngleContex matrix, improve recognition of face precision, set up the face database of authorized user in advance, then the face characteristic extracted utilize AngleContex matrix be calculated it and the similarity authorizing face database set up, it may be judged whether for authorized user's face。
3. the control method of the direct current electromagnetic relay device controlled based on recognition of face according to claim 2, it is characterised in that comprising the concrete steps that of described step 1:
Step 1.1, rectangular characteristic value is all grey scale pixel value sums and the difference of all grey scale pixel value sums in white portion in black region, have employed a kind of rectangular characteristic being similar to Haar wavelet basis function, rectangular characteristic value in detection window, rectangular characteristic value can be calculated by the gray value of integral image pixel, in integral image, coordinate is that the gray value of (X, Y) pixel is equal to all grey scale pixel values in correspondence position upper left side cumulative on original image;
The computing formula of integral image is as follows:
I (X, Y) is the image after integration, i (x, y) for original image,
Step 1.2, trying to achieve the integral image I (X, Y) of pixel as a sequence, forms the integrogram matrix of this figure, finds the set D of matrix exgenvalue according to integrogram matrix,
D={a1,a2,a3,a4,........am}(1-2)
Step 1.3, in rectangular window, builds multistage classifier and obtains the classification function of optimal threshold, matrix exgenvalue is carried out best classification,
Step 1.4, utilizes binarization of gray value and integral projection method to carry out eye location in the human face region detected;With eyes coordinates for datum mark, two eyes are made to be in same level line by translating, rotating, by scale transformation, facial image is carried out size to unify, facial image is carried out grayscale normalization, average and the mean square deviation of facial contour inner region gray scale is calculated by histogram equalization, image smoothing, gray scale normalization etc., view picture facial image is carried out greyscale transformation, highlights face key facial features。
4. the control method of the direct current electromagnetic relay device controlled based on recognition of face according to claim 3, it is characterised in that concretely comprising the following steps of described step 1.3:
Step 1.3.1, constructs training sample set L:
L={ (D1,K1),(D2,K2),......(Dm,Km)}(1-3)
Wherein D represents the matrix characteristic vector of sample, and K represents training result, and m is training total number of samples, m=1,2,3 ... .n;
Step 1.3.2, the initial probability distribution of sample L is sample weights:
N represents number of training, and the iterations of grader is t=1,2,3,4.......T, and T is any positive integer, calculates the weights of normalization sample:
Wherein j=1,2,3 ... .n;
Step 1.3.3, calls weak learning algorithm to the matrix characteristic vector D of each sample, trains a Weak Classifier Hj(Dm), and calculate the error rate ξ of this graderj
Wherein θjIt is the threshold value of Weak Classifier, pjFor the biasing of grader, the direction of majorization inequality, fjFor the eigenvalue of eigenmatrix,
Step 1.3.4, selects have minimal error rate ξtCorresponding Weak Classifier Ht(Dm) as optimal classification device, namely
ξt=min ξj(1-7)
Step 1.3.5, readjusting sample weights according to optimal classification device is:
Otherwise wherein m-th sample correct judgment then em=0 em=1. so can increase grader error sample weight, reducing the weight of correct sample of classifying, during next iteration, the selection of Weak Classifier will pay attention to the sample of last mistake classification error,
Step 1.3.6, generates strong classifier:
IfThen grader output result is 1, then it is assumed that this detection window is likely human face region, and R (D) is the eigenface vector of output;
The multistage classifier utilizing structure can get rid of most of non-face region, detects almost possible human face region;If every first-level class device is output as 1, then it is assumed that this detection window is likely human face region, the window of detection is input to next stage grader and proceeds to judge, otherwise, in this level, detection window is excluded as non-face region。
5. the control method of direct current electromagnetic relay device controlled based on recognition of face according to right 2, it is characterised in that described step 2 particularly as follows:
When extracting face characteristic, it is identical for belonging to the human face characteristic point distance with a part, and the difference of face is mainly manifested in the difference of face each several part contour line curvature, angle, can with 8 degree of divisions as face characteristic average distance, by 45 regions of plane division being zero with datum mark, generate AngleContex matrix, calculate the number of other discrete points falling into regional
Specifically implement according to following steps:
Step 2.1, on the eigenface that strong classifier obtains, each datum mark is selected for 9 character references points with nose, corners of the mouth midpoint, point, left eyebrow, left eye angle, the left corners of the mouth, right eyebrow, right eye angle, the right corners of the mouth, 45 regions (bin) are chosen with 8 degree of angles for dividing interval, thus can obtain 9*45 AngleContex matrix
Step 2.2, utilizes angular histogram to intersect method and the similarity of 9*45 the AngleContex matrix calculus face to be matched obtained and targeted customer's face, and computing formula is as follows:
P (s, v) is the crossing distance of two characteristic vectors, and M (R, R') is the ratio of 9*45 overall area (bin) pixel shared by each zoning (bin) in two rectangular histograms,
Wherein, Rl,gRepresent the AngleContex matrix of face to be matched, R'l,gThe AngleContex matrix of expression targeted customer's face, l=1,2 ..., 9 represent 9 character references points;G=1,2,3,4,5,6 ..., 44,45 expressions choose 45 regions with 8 degree of angles for dividing interval;
Step 2.3, the value calculating M (R, R') between 0~1, can take M (R, R') maximum just for the similar face that matching degree is the highest,
Step 2.4, after when similar face, the match is successful, voice announcer 3 can report the voice of " identifying successfully ", and otherwise, voice announcer 3 can report the voice of " identifying mistake ",
Wherein human face similarity degree coupling, on the eigenface image obtained, with nose, corners of the mouth midpoint, point, left eyebrow, left eye angle, the left corners of the mouth, right eyebrow, right eye angle, the right corners of the mouth for 9 character references points, each datum mark selected on the eigenface image obtained through strong classifier, 45 regions (bin) are chosen with 8 degree of angles for dividing interval, thus can obtaining 9*45 AngleContex matrix, multizone divides and improves matching precision as far as possible。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610009547.7A CN105701457B (en) | 2016-01-08 | 2016-01-08 | Direct current electromagnetic relay device and its control method based on recognition of face control |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610009547.7A CN105701457B (en) | 2016-01-08 | 2016-01-08 | Direct current electromagnetic relay device and its control method based on recognition of face control |
Publications (2)
Publication Number | Publication Date |
---|---|
CN105701457A true CN105701457A (en) | 2016-06-22 |
CN105701457B CN105701457B (en) | 2019-09-24 |
Family
ID=56226248
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201610009547.7A Active CN105701457B (en) | 2016-01-08 | 2016-01-08 | Direct current electromagnetic relay device and its control method based on recognition of face control |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN105701457B (en) |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107358207A (en) * | 2017-07-14 | 2017-11-17 | 重庆大学 | A kind of method for correcting facial image |
CN108319829A (en) * | 2017-01-11 | 2018-07-24 | 中兴通讯股份有限公司 | A kind of voice print verification method and apparatus |
CN109117745A (en) * | 2018-07-23 | 2019-01-01 | 青岛理工大学 | Cloud face recognition and positioning method based on building information model |
CN109544721A (en) * | 2018-12-04 | 2019-03-29 | 北京诺士诚国际工程项目管理有限公司 | A kind of long-range punch card method and system |
CN109739101A (en) * | 2018-12-30 | 2019-05-10 | 武汉市新源科创科技有限公司 | Intelligent home control system based on recognition of face |
CN109816642A (en) * | 2019-01-16 | 2019-05-28 | 尚晓斌 | Compatible immediate treatment mechanism |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103902978A (en) * | 2014-04-01 | 2014-07-02 | 浙江大学 | Face detection and identification method |
CN104574597A (en) * | 2014-12-30 | 2015-04-29 | 苏州福丰科技有限公司 | Face recognition door lock system with lighting function |
CN104816707A (en) * | 2015-03-25 | 2015-08-05 | 张彬彬 | Intelligent identification type car burglar alarm |
-
2016
- 2016-01-08 CN CN201610009547.7A patent/CN105701457B/en active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103902978A (en) * | 2014-04-01 | 2014-07-02 | 浙江大学 | Face detection and identification method |
CN104574597A (en) * | 2014-12-30 | 2015-04-29 | 苏州福丰科技有限公司 | Face recognition door lock system with lighting function |
CN104816707A (en) * | 2015-03-25 | 2015-08-05 | 张彬彬 | Intelligent identification type car burglar alarm |
Non-Patent Citations (1)
Title |
---|
谢剑斌 等: "《视觉感知与智能视频监控》", 31 March 2012 * |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108319829A (en) * | 2017-01-11 | 2018-07-24 | 中兴通讯股份有限公司 | A kind of voice print verification method and apparatus |
CN107358207A (en) * | 2017-07-14 | 2017-11-17 | 重庆大学 | A kind of method for correcting facial image |
CN109117745A (en) * | 2018-07-23 | 2019-01-01 | 青岛理工大学 | Cloud face recognition and positioning method based on building information model |
CN109117745B (en) * | 2018-07-23 | 2021-11-09 | 青岛理工大学 | Cloud face recognition and positioning method based on building information model |
CN109544721A (en) * | 2018-12-04 | 2019-03-29 | 北京诺士诚国际工程项目管理有限公司 | A kind of long-range punch card method and system |
CN109739101A (en) * | 2018-12-30 | 2019-05-10 | 武汉市新源科创科技有限公司 | Intelligent home control system based on recognition of face |
CN109816642A (en) * | 2019-01-16 | 2019-05-28 | 尚晓斌 | Compatible immediate treatment mechanism |
CN109816642B (en) * | 2019-01-16 | 2020-03-24 | 尚晓斌 | Compatible instant treatment mechanism |
Also Published As
Publication number | Publication date |
---|---|
CN105701457B (en) | 2019-09-24 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN105701457A (en) | DC electromagnetic relay device based on face identification control and control method thereof | |
CN109460699A (en) | A kind of pilot harness's wearing recognition methods based on deep learning | |
CN109522853B (en) | Face datection and searching method towards monitor video | |
CN108268850B (en) | Big data processing method based on image | |
KR101653278B1 (en) | Face tracking system using colar-based face detection method | |
CN110991315A (en) | Method for detecting wearing state of safety helmet in real time based on deep learning | |
CN111611849A (en) | Face recognition system for access control equipment | |
CN108196680B (en) | Robot vision following method based on human body feature extraction and retrieval | |
CN111079625A (en) | Control method for camera to automatically rotate along with human face | |
Moallem et al. | Fuzzy inference system optimized by genetic algorithm for robust face and pose detection | |
CN104090658A (en) | Human body image detection method based on HOG features | |
CN104091150B (en) | A kind of human eye state judgment method based on recurrence | |
Loutas et al. | Probabilistic multiple face detection and tracking using entropy measures | |
Ikegami et al. | A study on mobile robot control by hand gesture detection | |
CN106446870B (en) | A kind of human body contour outline feature extracting method and device | |
Lee et al. | Distinction Between Real Faces and Photos by Analysis of Face Data. | |
Zhang et al. | A novel efficient method for abnormal face detection in ATM | |
Guo et al. | A fast algorithm face detection and head pose estimation for driver assistant system | |
Patel et al. | Occlusion detection and recognizing human face using neural network | |
Paul et al. | Automatic adaptive facial feature extraction using CDF analysis | |
Mahmoud et al. | An effective hybrid method for face detection | |
Sharma et al. | Study and implementation of face detection algorithm using Matlab | |
Mamatha et al. | Automatic eyewinks interpretation system using face orientation recognition for human-machine interface | |
Cho et al. | A method of remote control for home appliance using free hand gesture | |
Yeom | Photon-counting linear discriminant analysis for face recognition at a distance |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
TR01 | Transfer of patent right | ||
TR01 | Transfer of patent right |
Effective date of registration: 20210816 Address after: 710065 No. 11301, block B, Huajing Business Plaza, No. 20, Fenghui South Road, Zhangba street, high tech Zone, Xi'an, Shaanxi Province Patentee after: XI'AN JIN POWER ELECTRICAL Co.,Ltd. Address before: 710048 No. 19 Jinhua South Road, Shaanxi, Xi'an Patentee before: XI'AN POLYTECHNIC University |