CN114783085A - Novel sharing bicycle based on face recognition - Google Patents
Novel sharing bicycle based on face recognition Download PDFInfo
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- CN114783085A CN114783085A CN202210280871.8A CN202210280871A CN114783085A CN 114783085 A CN114783085 A CN 114783085A CN 202210280871 A CN202210280871 A CN 202210280871A CN 114783085 A CN114783085 A CN 114783085A
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- 238000004364 calculation method Methods 0.000 abstract description 3
- 238000012986 modification Methods 0.000 description 2
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- G—PHYSICS
- G07—CHECKING-DEVICES
- 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
- G07C9/00563—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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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/08—Payment architectures
- G06Q20/14—Payment architectures specially adapted for billing systems
- G06Q20/145—Payments according to the detected use or quantity
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
- G06Q20/401—Transaction verification
- G06Q20/4014—Identity check for transactions
- G06Q20/40145—Biometric identity checks
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07F—COIN-FREED OR LIKE APPARATUS
- G07F17/00—Coin-freed apparatus for hiring articles; Coin-freed facilities or services
- G07F17/0042—Coin-freed apparatus for hiring articles; Coin-freed facilities or services for hiring of objects
- G07F17/0057—Coin-freed apparatus for hiring articles; Coin-freed facilities or services for hiring of objects for the hiring or rent of vehicles, e.g. cars, bicycles or wheelchairs
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Abstract
The invention discloses a novel shared bicycle based on face recognition, which solves the technical problems that two-dimension codes on the existing shared bicycle cannot be recognized due to damage, so that unlocking cannot be realized, and cost settlement is troublesome; the method comprises the steps that a data analysis module calculates a first feature vector coefficient and a second feature vector coefficient to obtain a similarity score, the similarity score is compared with a standard similarity score, if the similarity score is larger than or equal to the standard similarity score, an unlocking signal is sent to an execution module by the data analysis module, the execution module controls unlocking of a vehicle, if the similarity score is smaller than the standard similarity score, a re-acquisition signal is sent to a data acquisition module by the data analysis module, the data acquisition module acquires primary face information again until the primary face information is matched with face information of a person, then when the user needs to lock, the operation is repeated to obtain secondary face information, and whether the secondary face information is matched with the face information of the person is obtained through calculation.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a novel shared bicycle based on face recognition.
Background
In recent years, along with the rapid advance of urbanization, a large number of growing population brings vitality to cities and also brings urban road congestion problems, and in order to alleviate the problems, a plurality of company platforms are provided for sharing a single vehicle for people to ride. The shared bicycle provides great convenience for the travel of the majority of citizens, and in addition, the government advocates green travel, and more people select the shared bicycle as a vehicle, so that the problem of urban traffic jam is relieved to a certain extent, and carbon emission is reduced.
However, a series of problems also exist in the traditional sharing bicycle, for example, the problems that the two-dimension code on the bicycle cannot be unlocked due to damage and cannot be identified, the expense settlement is troublesome and the like are solved, and therefore, a novel sharing bicycle based on face identification is provided at present.
Disclosure of Invention
In order to solve the defects mentioned in the background technology, the invention aims to provide a novel shared bicycle based on face recognition.
The purpose of the invention can be realized by the following technical scheme: a novel shared bicycle based on face recognition is characterized by comprising a data acquisition module, a data processing module, a data analysis module, a user login module, a cloud database and an execution module, wherein the user login module is used for inputting face information, a mobile phone number and payment platform binding information of a person and sending the face information, the mobile phone number and the payment platform binding information to the cloud database for storage;
the data acquisition module is used for acquiring face information and a mobile phone number of a user, marking the acquired face information as primary face information, and then sending the primary face information to the data processing module for data processing;
the data processing module is used for processing the received primary face information and obtaining a first feature vector coefficient T1And the first eigenvector coefficient T is used1Sending the data to a data analysis module for analysis;
the data analysis moduleAccording to the received first eigenvector coefficient T1Calculating to obtain a similarity score X, and comparing the similarity score X with a set standard similarity score X for face recognitionsComparing, if the primary face information is matched with the face information of the person, sending an unlocking signal to the execution module, if the primary face information is not matched with the face information of the person, sending a re-acquisition signal to the data acquisition module, and analyzing the data re-acquired by the data acquisition module until the primary face information is matched with the face information of the person; when the user needs to lock the vehicle, the operation is repeated until the secondary face information is matched with the face information of the person, and the data analysis module sends a locking and payment signal to the execution module;
and the execution module executes corresponding operation according to the execution signal sent by the data analysis module.
Further, the processing procedure of the data processing module comprises the following steps:
the method comprises the following steps: recognizing the face position in the primary face information, and marking the coordinate information of the face;
step two: positioning the characteristic points on the primary face information, and aligning the characteristic points on the primary face information through geometric transformation;
step three: converting pixel values of primary face information image into first characteristic vector coefficient T1And the first eigenvector coefficient T is used1And sending the data to a data analysis module for analysis.
Furthermore, the data acquisition module comprises a touch control display screen and a camera.
Further, the analysis process of the data analysis module comprises the following steps:
step W1: processing the face information of the person in the cloud database and acquiring a second feature vector coefficient T2；
Step W2: setting standard similarity score X of face recognitions；
Step W3: using formulas
Calculating to obtain a similarity score X, wherein T10Is the standard coefficient of the first feature vector, T20Is a second eigenvector standard coefficient, alpha is a first eigenvector influence coefficient, and beta is a second eigenvector influence coefficient;
step W4: mixing X1And XsMaking a comparison if X1≥XsIf the face information is matched with the face information of the person, the data analysis module sends an unlocking signal to the execution module, and if the face information is X, the data analysis module sends the unlocking signal to the execution module1＜XsIf the face information is not matched with the face information of the person, the data analysis module sends the re-collected signals to the data collection module, and the data collection module collects the face information once again until the face information once conforms to the face information of the person.
Further, the execution process of the execution module comprises the following steps:
the method comprises the steps that primary face information is matched with face information of people stored in a cloud database by a data analysis module, an unlocking signal is sent to an execution module, then the execution module unlocks a shared bicycle, a locking and payment signal is sent to the execution module when secondary face information is matched with the primary face information stored in the cloud database by the data analysis module, the execution module locks the shared bicycle, and the cost needing to be paid is deducted through a bound payment platform.
The invention has the beneficial effects that:
when the invention is used, firstly, a user inputs face information, a mobile phone number and payment platform binding information through a user login module, a data acquisition module acquires primary face information and sends the primary face information to a data processing module, the data processing module processes the primary face information to obtain a first feature vector coefficient, the data processing module sends the first feature vector coefficient to a data analysis module, the data analysis module calculates the first feature vector coefficient and a second feature vector coefficient to obtain a similarity score and compares the similarity score with a standard similarity score, if the similarity score is more than or equal to the standard similarity score, the data analysis module sends an unlocking signal to an execution module, the execution module controls unlocking of a vehicle, and if the similarity score is less than the standard similarity score, the data analysis module sends a re-acquisition signal to the data acquisition module, the data acquisition module acquires primary face information again until the primary face information is matched with the face information of a person, then when a user needs to lock, the operation is repeated to obtain secondary face information, whether the secondary face information is matched with the face information of the person is obtained through calculation, if the secondary face information is matched with the face information of the person, the data analysis module sends a locking and payment signal to the execution module, the execution module locks, the payment platform information bound with the data acquisition module automatically deducts the cost, if the secondary face information is not matched with the face information of the person, a re-acquisition signal is sent to the data acquisition module, and the data acquisition module acquires the secondary face information again until the secondary face information is matched with the face information of the person; therefore, the functions of locking and unlocking and automatic payment through face recognition are realized.
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In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without creative efforts;
fig. 1 is a schematic diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a novel shared bicycle based on face recognition comprises a data acquisition module, a data processing module, a data analysis module, a user login module, a cloud database and an execution module, wherein the user login module is used for inputting face information, a mobile phone number and payment platform binding information of a person and sending the face information, the mobile phone number and the payment platform binding information to the cloud database for storage;
the data acquisition module is used for acquiring face information and a mobile phone number of a user, marking the acquired face information as primary face information, and then sending the primary face information to the data processing module for data processing;
it should be further explained that, in a specific implementation process, the data acquisition module includes a touch control display screen and a camera, the touch control display screen is used for a user to input a mobile phone number, a protective cover is installed on the touch control display screen and used for protecting the touch control display screen in rainy days, and the camera is used for capturing the face information of the person by shooting;
the data processing module is configured to process the received primary face information, and specifically, a processing process of the data processing module includes the following steps:
the method comprises the following steps: recognizing the face position in the primary face information, and marking the coordinate information of the face;
it should be further explained that, in the specific implementation process, the face position of the primary face information is identified by using the histogram of directional gradients, and the specific detection process is to first graye the picture of the primary face information, then calculate the pixel gradients in the picture, and convert the pixel gradients into the HOG format to obtain the face position;
step two: positioning the characteristic points on the primary face information, and aligning the characteristic points on the primary face information through geometric transformation;
it should be further described that the feature point alignment is to obtain a feature vector;
step three: converting pixel values of primary face information image into first characteristic vector coefficient T1And the first eigenvector coefficient T is used1The feature vectors are sent to the data analysis module for analysis, and it should be further explained that, in the specific implementation process, the feature vectors are compact and discriminable.
The data analysis module is used for receiving the characteristic vector T1Specifically, the analysis process of the data analysis module includes the following steps:
step W1: processing the face information of the person in the cloud database and acquiring a second feature vector coefficient T2；
Step W2: setting standard similarity score X of face recognitions(ii) a It should be further described that, in the specific implementation process, the similarity score of the face recognition is a standard for judging whether the face information can pass the comparison when being compared with the face information of the person once;
step W3: using a formula
Calculating to obtain a similarity score X, wherein T1Is the first eigenvector coefficient, T2Is the second eigenvector coefficient, T10Is the first eigenvector standard coefficient, T20Is a second eigenvector standard coefficient, alpha is a first eigenvector influence coefficient, and beta is a second eigenvector influence coefficient;
step W4: x is to be1And XsMaking a comparison if X1≥XsIf the similarity score is larger than the standard similarity score, the primary face information is matched with the face information of the person, the data analysis module sends an unlocking signal to the execution module, and if the similarity score is X, the data analysis module sends the unlocking signal to the execution module1＜XsIf the similarity score is smaller than the standard similarity score, the primary face information is not matched with the face information of the person, the data analysis module sends a re-acquisition signal to the data acquisition module, and the data acquisition module acquires the primary face information again until the primary face information accords with the face information of the person;
it should be further noted that, after the user finishes using the sharing bicycle, the data acquisition module acquires the face information again and marks the face information as secondary face information, the operations are repeated, and the similarity score X between the secondary face information and the face information of the person at the moment is obtained through calculation2X is to be2And XsComparing, judging whether the secondary face information is matched with the face information of the person, if X is the case2≥XsIf the data analysis module sends a locking and payment signal to the execution module, if the X is less than the preset threshold, the data analysis module sends a locking and payment signal to the execution module2＜XsAnd the data analysis module sends the re-collected signal to the data collection module, and the data collection module collects the secondary face information again until the secondary face information accords with the face information of the person.
The execution module is used for unlocking the shared bicycle by the execution module after the primary face information is matched with the face information of the person stored in the cloud database by the data analysis module and an unlocking signal is sent to the execution module, and sending a locking and payment signal to the execution module when the secondary face information is matched with the primary face information stored in the cloud database by the data analysis module, and the execution module locks the shared bicycle and deducts the cost required to be paid through the bound payment platform.
The foregoing shows and describes the general principles, principal features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are given by way of illustration of the principles of the present invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, and such changes and modifications are within the scope of the invention as claimed.
Claims (5)
1. A novel shared bicycle based on face recognition is characterized by comprising a data acquisition module, a data processing module, a data analysis module, a user login module, a cloud database and an execution module, wherein the user login module is used for inputting face information, a mobile phone number and payment platform binding information of a person and sending the face information, the mobile phone number and the payment platform binding information to the cloud database for storage;
the data acquisition module is used for acquiring face information and a mobile phone number of a user, marking the acquired face information as primary face information, and then sending the primary face information to the data processing module for data processing;
the data processing module is used for processing the received primary face information and obtaining a first characteristic vector coefficient T1And the first eigenvector coefficient T is used1Sending the data to a data analysis module for analysis;
the data analysis module is used for receiving a first feature vector coefficient T1Calculating to obtain a similarity score X, and comparing the similarity score X with a set standard similarity score X for face recognitionsComparing, if the primary face information is matched with the face information of the person, sending an unlocking signal to the execution module, if the primary face information is not matched with the face information of the person, sending a re-acquisition signal to the data acquisition module, and analyzing the data re-acquired by the data acquisition module until the primary face information is matched with the face information of the person; when the user needs to lock the vehicle, repeating the operations until the secondary face information is matched with the face information of the person, and sending a locking and payment signal to the execution module by the data analysis module;
and the execution module executes corresponding operation according to the execution signal sent by the data analysis module.
2. The novel shared bicycle based on face recognition as claimed in claim 1, wherein the processing procedure of the data processing module comprises the following steps:
the method comprises the following steps: recognizing the face position in the primary face information, and marking the coordinate information of the face;
step two: positioning the characteristic points on the primary face information, and aligning the characteristic points on the primary face information through geometric transformation;
step three: converting pixel values of primary face information image into first characteristic vector coefficient T1And the first eigenvector coefficient T is used1And sending the data to a data analysis module for analysis.
3. The novel shared bicycle based on face recognition is characterized in that the data acquisition module comprises a touch control display screen and a camera.
4. The novel shared bicycle based on face recognition as claimed in claim 1, wherein the analysis process of the data analysis module comprises the following steps:
step W1: processing the face information of the person in the cloud database and acquiring a second feature vector coefficient T2；
Step W2: setting standard similarity score X of face recognitions；
Step W3: using formulas
Calculating to obtain a similarity score X, wherein T10Is the first eigenvector standard coefficient, T20Is a second eigenvector standard coefficient, alpha is a first eigenvector influence coefficient, and beta is a second eigenvector influence coefficient;
step W4: x is to be1And XsMaking a comparison if X1≥XsIf the unlocking signal is X, the data analysis module sends the unlocking signal to the execution module, and if the unlocking signal is X, the data analysis module sends the unlocking signal to the execution module1＜XsIf the face information is not matched with the face information of the person, the data analysis module sends the re-collected signals to the data collection module, and the data collection module collects the face information once again until the face information once conforms to the face information of the person.
5. The novel shared bicycle based on face recognition as claimed in claim 1, wherein the execution module executes the following processes:
the method comprises the steps that primary face information is matched with face information of people stored in a cloud database by a data analysis module, an unlocking signal is sent to an execution module, then a shared bicycle is unlocked by the execution module, a locking and payment signal is sent to the execution module when secondary face information is matched with the primary face information stored in the cloud database by the data analysis module, the shared bicycle is locked by the execution module, and the cost needing to be paid is deducted through a bound payment platform.
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Application publication date: 20220722 |