CN110222736A - Method, apparatus, electronic equipment and the computer readable storage medium of training classifier - Google Patents
Method, apparatus, electronic equipment and the computer readable storage medium of training classifier Download PDFInfo
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Abstract
The present disclosure discloses a kind of methods of trained classifier, it is characterized in that, it include: to obtain ID Card Image set, ID Card Image in the ID Card Image set is corresponding with classification information, the classification information includes first category information and second category information, the first category information indicates that the classification of ID Card Image corresponding with the first category information includes normal category, and the second category information indicates that the classification of ID Card Image corresponding with the first category information includes abnormal class；Determine the output project of classifier；According to the ID Card Image set and convolutional neural networks (CNN) the training classifier.Method, apparatus, electronic equipment and the computer readable storage medium for the training classifier that the embodiment of the present disclosure provides, it can be according to ID Card Image set training classifier, to whether be normal ID Card Image by the image of classifier identification input, to be applied to various ID card verification occasions.
Description
Technical field
This disclosure relates to field of information processing more particularly to a kind of method, apparatus, electronic equipment and the meter of trained classifier
Calculation machine readable storage medium storing program for executing.
Background technique
With the progress of network technology, internet has become the important medium for serving various businesses, such as by mutual
When the important services such as networking processing banking, need to obtain the ID Card Image of user, to pass through ID Card Image
The identity information of user is extracted to be used for ID card verification, to carry out important service.
In above-mentioned verification process, the prior art is often through the body for identifying that equipment and/or recognizer are uploaded from user
ID Card Image region is extracted in part card image, then extracts the identity of user according to predeterminated position in ID Card Image region
Information, such as name, birthday, identity card validity period etc..But in above process, the identity card figure that discovery certain customers upload
There are many problems for picture, such as the ID Card Image that certain customers upload not is ID Card Image, or due to light or screening
Reasons, the copy images for belonging to unsharp ID Card Image or identity card such as gear etc. identify equipment and/or identification
Program possibly will can not extract identity information from these ID Card Images of problems, or extract the identity letter of mistake
Breath for example when user uploads the image of other certificates rather than ID Card Image, identifies equipment and/or recognizer
The image-region of other certificates is extracted, but possibly can not extract identity card in the predeterminated position of identity card validity period has
Effect phase information or extracted content may not be correct identity card validity period information.
Summary of the invention
Method, apparatus, electronic equipment and the computer readable storage medium for the training classifier that the embodiment of the present disclosure provides,
It can be according to ID Card Image set training classifier, to whether be normal body by the image of classifier identification input
Part card image, to be applied to various ID card verification occasions.
In a first aspect, the embodiment of the present disclosure provides a kind of method of trained classifier characterized by comprising obtain body
Part demonstrate,proves image collection, and the ID Card Image in the ID Card Image set is corresponding with classification information, and the classification information includes
First category information and second category information, the first category information indicate identity card corresponding with the first category information
The classification of image includes normal category, and the second category information indicates ID Card Image corresponding with the first category information
Classification include abnormal class；Determine that the output project of classifier, the output project include that the first output project and second are defeated
Project out, the first output project is corresponding with the first category information, the second output project and the second category
Information is corresponding；According to the ID Card Image set and convolutional neural networks (CNN) the training classifier.
Further, the output item purpose output valve of the classifier and be 1.
Further, the second category information includes multiple subcategory informations；The second output project includes multiple
Sub- output project, the multiple sub- output project are corresponding with the multiple subcategory information.
Further, the multiple subcategory information includes one of following or a variety of: instruction false identities demonstrate,prove image
The subcategory information of classification；Indicate the subcategory information of the infull identity card image category of information；Indicate copy ID Card Image
The subcategory information of classification；Indicate the subcategory information of the certificate image classification of non-identity card.
Further, after according to the ID Card Image set and the CNN training classifier, further includes: receive
Input picture；Output item purpose output valve corresponding with the input picture is determined by the classifier；According to the input
The corresponding output item purpose output valve of image determines the classification of the input picture.
Further, in the class for determining the input picture according to the corresponding output item purpose output valve of the input picture
After not, further includes: in the case where the classification of the input picture is abnormal class, generate abnormal instruction information.
Further, the reception input picture, including the input picture is received from client；Generating the exception
After instruction information, further includes: Xiang Suoshu client sends the abnormal instruction information.
Second aspect, the embodiment of the present disclosure provide a kind of device of trained classifier characterized by comprising image set
It closes and obtains module, ID Card Image and classification information for obtaining ID Card Image set, in the ID Card Image set
Corresponding, the classification information indicates the classification of ID Card Image corresponding with the classification information；Determining module is divided for determining
The output project of class device, the output project are corresponding with the classification information；Training module, for according to the ID Card Image
Set and convolutional neural networks (CNN) training classifier.
Further, the output item purpose output valve of the classifier and be 1.
Further, the second category information includes multiple subcategory informations；The second output project includes multiple
Sub- output project, the multiple sub- output project are corresponding with the multiple subcategory information.
Further, the multiple subcategory information includes one of following or a variety of: instruction false identities demonstrate,prove image
The subcategory information of classification；Indicate the subcategory information of the infull identity card image category of information；Indicate copy ID Card Image
The subcategory information of classification；Indicate the subcategory information of the certificate image classification of non-identity card.
Further, the device of the trained classifier further includes receiving module, classification results determining module, wherein institute
Receiving module is stated for receiving input picture；The classification results determining module be used for by the classifier it is determining with it is described defeated
Enter the corresponding output item purpose output valve of image, and for true according to the corresponding output item purpose output valve of the input picture
The classification of the fixed input picture.
Further, the device of the trained classifier further includes generation module, and the generation module is used for described defeated
In the case where entering the classification of image for abnormal class, abnormal instruction information is generated.
Further, the receiving module, for receiving the input picture from client；The dress of the trained classifier
Setting further includes sending module, and the sending module is used to send the abnormal instruction information to the client.
The third aspect, the embodiment of the present disclosure provide a kind of electronic equipment, comprising: memory, it is computer-readable for storing
Instruction；And the one or more processors coupled with the memory, for running the computer-readable instruction, so that institute
State the method that any trained classifier in aforementioned first aspect is realized when processor operation.
Fourth aspect, the embodiment of the present disclosure provide a kind of non-transient computer readable storage medium, which is characterized in that described
Non-transient computer readable storage medium stores computer instruction, when the computer instruction is computer-executed, so that institute
The method for stating any trained classifier that computer executes in aforementioned first aspect.
The present disclosure discloses method, apparatus, electronic equipment and the computer readable storage mediums of a kind of trained classifier.Its
Described in training classifier method characterized by comprising obtain ID Card Image set, the ID Card Image set
In ID Card Image it is corresponding with classification information, the classification information includes first category information and second category information, described
First category information indicates that the classification of ID Card Image corresponding with the first category information includes normal category, described second
Classification information indicates that the classification of ID Card Image corresponding with the first category information includes abnormal class；Determine classifier
Output project, the output project include the first output project and the second output project, the first output project and described the
One classification information is corresponding, and the second output project is corresponding with the second category information；According to the ID Card Image set
With convolutional neural networks (CNN) training classifier.The method, apparatus for the training classifier that the embodiment of the present disclosure provides, electricity
Sub- equipment and computer readable storage medium, can be according to ID Card Image set training classifier, to pass through the classifier
Whether the image of identification input is normal ID Card Image, to be applied to various ID card verification occasions.
Above description is only the general introduction of disclosed technique scheme, in order to better understand the technological means of the disclosure, and
It can be implemented in accordance with the contents of the specification, and to allow the above and other objects, features and advantages of the disclosure can be brighter
Show understandable, it is special below to lift preferred embodiment, and cooperate attached drawing, detailed description are as follows.
Detailed description of the invention
In order to illustrate more clearly of the embodiment of the present disclosure or technical solution in the prior art, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is this public affairs
The some embodiments opened for those of ordinary skill in the art without creative efforts, can be with root
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is the flow chart of the embodiment of the method one for the training classifier that the embodiment of the present disclosure provides；
Fig. 2 is a kind of convolutional neural networks model schematic that the disclosure provides；
Fig. 3 is the flow chart of the embodiment of the method two for the training classifier that the embodiment of the present disclosure provides；
Fig. 4 is the structural schematic diagram of the Installation practice for the training classifier that the embodiment of the present disclosure provides；
Fig. 5 is the structural schematic diagram of the electronic equipment provided according to the embodiment of the present disclosure.
Specific embodiment
Illustrate embodiment of the present disclosure below by way of specific specific example, those skilled in the art can be by this specification
Disclosed content understands other advantages and effect of the disclosure easily.Obviously, described embodiment is only the disclosure
A part of the embodiment, instead of all the embodiments.The disclosure can also be subject to reality by way of a different and different embodiment
It applies or applies, the various details in this specification can also be based on different viewpoints and application, in the spirit without departing from the disclosure
Lower carry out various modifications or alterations.It should be noted that in the absence of conflict, the feature in following embodiment and embodiment can
To be combined with each other.Based on the embodiment in the disclosure, those of ordinary skill in the art are without creative efforts
Every other embodiment obtained belongs to the range of disclosure protection.
It should be noted that the various aspects of embodiment within the scope of the appended claims are described below.Ying Xian
And be clear to, aspect described herein can be embodied in extensive diversified forms, and any specific structure described herein
And/or function is only illustrative.Based on the disclosure, it will be understood by one of ordinary skill in the art that one described herein
Aspect can be independently implemented with any other aspect, and can combine the two or both in these aspects or more in various ways.
For example, carry out facilities and equipments in terms of any number set forth herein can be used and/or practice method.In addition, can make
With other than one or more of aspect set forth herein other structures and/or it is functional implement this equipment and/or
Practice the method.
It should also be noted that, diagram provided in following embodiment only illustrates the basic structure of the disclosure in a schematic way
Think, component count, shape and the size when only display is with component related in the disclosure rather than according to actual implementation in diagram are drawn
System, when actual implementation kenel, quantity and the ratio of each component can arbitrarily change for one kind, and its assembly layout kenel can also
It can be increasingly complex.
In addition, in the following description, specific details are provided for a thorough understanding of the examples.However, fields
The skilled person will understand that the aspect can be practiced without these specific details.
The method of the training classifier provided in this embodiment can be executed by the device of a trained classifier, the dress
It sets and can be implemented as software, can be implemented as hardware, be also implemented as the combination of software and hardware, such as the training classification
The device of device includes computer equipment, to execute the training classifier provided in this embodiment by the computer equipment
Method, as understood by those skilled in the art, computer equipment can be desk-top or portable computer device, can also be shifting
Dynamic terminal device etc..
Fig. 1 is the flow chart of the embodiment of the method one for the training classifier that the embodiment of the present disclosure provides, as shown in Figure 1, this
The method of the training classifier of open embodiment includes the following steps:
Step S101 obtains ID Card Image set, and the ID Card Image and classification in the ID Card Image set are believed
Breath corresponds to, and the classification information includes first category information and second category information, the first category information instruction with it is described
The classification of the corresponding ID Card Image of first category information includes normal category, the second category information instruction and described first
The classification of the corresponding ID Card Image of classification information includes abnormal class；
It needs to gather training classifier by training, therefore obtains ID Card Image set in step s101 as training
Set is to train classifier.
Since the embodiment of the present disclosure wishes to identify input picture for normal ID Card Image still by training classifier
Abnormal ID Card Image, therefore, the body for the ID Card Image set as training set, in the ID Card Image set
Part card image needs to include classification (the e.g. identity card figure of normal category of the classification information to indicate corresponding ID Card Image
The ID Card Image of picture or abnormal class), to train classifier.
Wherein, the classification information includes first category information and second category information, the first category information instruction
The classification of ID Card Image corresponding with the first category information includes normal category, the second category information instruction and institute
The classification for stating the corresponding ID Card Image of first category information includes abnormal class.As an example, the ID Card Image set
Including two subsets: the ID Card Image in one of subset is corresponding with first category information, it means that a son
The classification of the ID Card Image of concentration includes normal category；ID Card Image in another one subset is believed with second category
Breath corresponds to, it means that the classification of the ID Card Image in another subset includes abnormal class.Those skilled in the art can
To understand, the classification information that the picture material of ID Card Image should be made as far as possible corresponding matches, such as in above-mentioned example,
ID Card Image in a subset is designated as normal category by first category information, therefore the ID Card Image in a subset
Answer the ID Card Image quilt that greater probability can be identified equipment and/or recognizer correctly identifies, in another subset
Second category information is designated as abnormal class, the ID Card Image in another subset should greater probability can not be identified
Equipment and/or recognizer correctly identify, thus could be during training classifier, so that classifier correctly learns
It has corresponded to the characteristics of image of the ID Card Image of normal category and has corresponded to the characteristics of image of the ID Card Image of abnormal class, from
And guarantee classifier accuracy with higher.
As optional example, the identity can be obtained according to identification equipment in the prior art and/or recognizer
Image collection is demonstrate,proved, such as the ID Card Image for identifying that equipment and/or recognizer can identify is opposite with first category information
It answers, identification equipment and/or the unrecognized ID Card Image of recognizer is corresponding with second category information, it also for example will just
It is abnormal ID Card Image that normal ID Card Image is handled by image processing techniques, and how the embodiment of the present disclosure is for construct
Or obtain the ID Card Image set without limitation.Also, those skilled in the art can define, can by array,
The data structures such as table are corresponding with classification information by ID Card Image, for use in training classifier.
Step S102 determines that the output project of classifier, the output project include the first output project and the second output
Project, the first output project is corresponding with the first category information, and the second output project and the second category are believed
Breath corresponds to；
As understood by one of ordinary skill in the art, as the ID Card Image set of training set, body therein
The quantity of the classification of the ID Card Image of classification information instruction corresponding to part card image is divided with what training set according to this was trained
The output project of the classification results of class device has corresponding relationship, in general, the output project of the classification results of classifier
Quantity be equal to the ID Card Image set acquired in step s101 in ID Card Image included by classification information institute
The quantity of the classification of instruction, such as the ID Card Image set acquired in step S101 include M ID Card Image, the M
A corresponding M classification information of ID Card Image indicates N kind identity card classification, and (N is natural number, such as N=2, then 2
Kind identity card classification is normal category and abnormal class respectively), then the classification of the classifier at the trained place of image collection according to this
It as a result include N number of output project.
The output project of classifier in step S102 is used for the classification results of presentation class device, such as those skilled in the art
Understood, the difference of the calculation according to used by classifier, the output project of classifier can pass through diversified forms table
Show classification results, as an optional embodiment, the value of some output project output is A in each output project of classifier
The value of (such as A=1), other output project outputs are B (such as B=0), then the output project for meaning that the value of output is A refers to
Classification of the classifier to input picture is shown, as an example, classifier includes above-mentioned first output project and above-mentioned second output
Project, for input picture, if the first output item purpose output valve is 1, the second output item purpose output valve is
0, then mean that the classification of the input picture is normal category；As another optional embodiment, the classifier it is defeated
Out the output valve of project and be 1, as an example, the value that is exported of each output project of the classifier of such as training is big
In or be equal to 0 and be less than or equal to 1, the value that each output project of the classifier is exported and be 1, i.e. classifier
The value that is exported of each output project represent the classifier to the class probability of input picture, the maximum output item of probability value
Mesh indicates classification of the classifier to input picture.
Step S103, according to the ID Card Image set and convolutional neural networks (CNN) the training classifier.
The ID Card Image set of the training classifier has been determined in step s101, institute has been determined in step s 102
The output project of classifier is stated, therefore in step s 103 can be according to the ID Card Image set and convolutional neural networks
(CNN) the training classifier, the output project of the classifier includes the identified classifier in the step S102
Output project.
Optionally, the convolutional neural networks include but is not limited to MobileNetV2, AlexNet, googleNet,
VGGNet, DenseNet etc..
It will be appreciated by those skilled in the art that different convolutional neural networks have different frameworks, this is embodied in possibility
Layer including different layer and different number.As shown in Fig. 2, the frame of typical convolutional neural networks include convolutional layer, it is non-
Linear layer, pond layer and it is fully connected layer.
Convolutional layer is mainly used for extracting characteristics of image from input picture, can be by one or more filters (also referred to as
Characteristic detector) according to preset step-length characteristics of image is extracted from input picture.As understood by those skilled in the art, scheme
As being made of pixel, each of image pixel, such as input picture packet can be characterized by color parameter and location parameter
48*48 pixel is included, is exported according to step-length by the 1 available convolutional layer of extractor characteristics of image by the filter of 5*5
The image characteristic matrix of 44*44.
It can connect non-linear layer or pond layer after convolutional layer, wherein non-linear layer is used for the image exported to convolutional layer
Feature carries out Further Feature Extraction, pond layer can using be averaged by the way of pond or the mode in maximum pond to convolutional layer or
The output result of non-linear layer is handled, and can reduce the dimension of characteristics of image, reduces operation times.
The last of convolutional neural networks is to be fully connected layer, and the last layer for being fully connected layer is output layer, can also be claimed
For the output layer of classifier, it is fully connected the characteristics of image of the layer before layer receives, and described image feature is located layer by layer
Reason, finally, treated characteristics of image is input to output layer, is carried out by activation primitive to the characteristics of image in output layer
It calculates, and calculated result is mapped to multiple output projects included by output layer, multiple output project can be used as classification
The output project of device.
During based on convolutional neural networks training classifier, for the image in training set, volume can be input to
Product neural network, calculates and handles layer by layer according to the framework of convolutional neural networks, finally in the output layer output for being fully connected layer
Then classification results are compared to construct loss function with the classification information of image according to the classification results, are passing through gradient
Decline scheduling algorithm according to the loss function update training process involved in weight and biasing etc. parameters and then according to update after
Parameter recalculate classification results, such iteration completes the training to classifier after obtaining optimal classification results, thus
For the image of input, it can be classified and/or be identified by the classifier.
Therefore in step s 103, in the manner described above, the ID Card Image in ID Card Image set is input to volume
Product neural network by each layer of feature extraction and is calculated to train classifier, to the classifier completed by the training
Identify the classification of input picture, such as the output project of the classifier includes aforementioned first output project and aforementioned second output
Project can identify input picture about the first output project and the second output item purpose output valve according to classifier described defeated
The classification for entering image is normal category or abnormal class.
In an alternative embodiment, the second category information includes multiple subcategory informations；Second output
Project includes multiple sub- output projects, and the multiple sub- output project is corresponding with the multiple subcategory information.As the disclosure is carried on the back
Described in scape technology, the ID Card Image of problems that certain customers upload has many different forms, for different form
ID Card Image of problems can be opposite from different subcategory informations by it in disclosure optional embodiment
It answers, and the second output project includes multiple sub- output projects to the output project of the classifier in other words, it is the multiple
Sub- output project is corresponding from the different subcategory information respectively, thus the training provided by disclosure optional embodiment
The classifier that the method for classifier is trained can not only identify the ID Card Image of normal category, for body of problems
The ID Card Image of part card image abnormal class in other words, additionally it is possible to identify which kind of abnormal class it belongs to.
Optionally, the multiple subcategory information includes one of following or a variety of: instruction false identities demonstrate,prove image class
Other subcategory information (such as manual draw or other images etc. unrelated with certificate)；Indicate the infull ID Card Image of information
The subcategory information (such as light is reflective or blocks) of classification；Indicate the subcategory information of copy ID Card Image classification；
Indicate the subcategory information (such as Hong Kong, Macao and Taiwan pass etc.) of the certificate image classification of non-identity card.As an example, described
Multiple subcategory informations include indicating the subcategory information of false identities card image category, indicate the infull ID Card Image class of information
Other subcategory information indicates the subcategory information of copy ID Card Image classification, and the certificate image of the non-identity card of instruction
The subcategory information of classification amounts to four subcategory informations, that is to say, that the ID Card Image set acquired in step S101
In include M ID Card Image, having respectively corresponded five classification informations, (in addition to aforementioned four subcategory information, there are also the first kind
Other information), correspondingly, it is the first output project respectively that the output project of the classifier, which includes five output projects, and with institute
The corresponding four sub- output projects of four subcategory informations are stated, thus according to the classifier energy of above-mentioned ID Card Image set training
It reaches input picture classification or is identified as normal category, false identities card image category, the infull identity card image category of information,
One of copy ID Card Image classification and the certificate image classification of non-identity card.
As shown in figure 3, in the embodiment of the method two of the training classifier of the disclosure, in step S103: according to the body
After part card image collection and the CNN training classifier, further includes:
S301 receives input picture；
S302 determines output item purpose output valve corresponding with the input picture by the classifier；
S303 determines the classification of the input picture according to the corresponding output item purpose output valve of the input picture.
Due to having trained the classifier by step S103, input picture can be carried out using the classifier
Classification, to identify whether the input picture is normal ID Card Image, therefore receives input picture in step S301, and
And output item purpose output valve corresponding with the input picture is determined by the classifier in step s 302, so as in step
The classification of the input picture is determined in rapid S303 according to the output valve of projects.Those skilled in the art can define, and pass through
The process that step S301~S303 classifies to input picture or identifies actually gathers training with when training classifier
In the process that is handled of ID Card Image it is identical, but when being classified to input picture or being identified, do not need root
Related parameter when training classifier is updated according to classification results, it is only necessary to which classification results are provided by classifier.
As an optional embodiment, in step S303: being exported according to the corresponding output item purpose of the input picture
Value determines after the classification of the input picture, further includes:
S304 generates abnormal instruction information in the case where the classification of the input picture is abnormal class.
In the ID Card Image for needing to obtain user, to extract the identity information of user by ID Card Image to be used for
ID card verification occasion identifies that equipment and/or recognizer can succeed if user uploads the ID Card Image of normal category
Identity information is extracted from the ID Card Image that user uploads, and sends specific business for the identity information of extraction, if
User uploads the ID Card Image of abnormal class, such as belongs to one kind of a variety of subcategory informations in previous embodiment, then
Abnormal instruction information can be generated by step S304, exception instruction information is used to indicate the ID Card Image that user is uploaded
There are exceptions.
As another optional embodiment, the reception input picture, including the input picture is received from client；
In step S304: after the generation abnormal instruction information, further includes:
S305: Xiang Suoshu client sends the abnormal instruction information.
There is exception since exception instruction information is used to indicate the ID Card Image that user is uploaded, in step
The abnormal instruction information is sent to client in S305, to play the role of prompting user.
Optionally, the input picture can be received from client by network, the client is, for example, terminal device,
Such as smart phone, desktop computer etc., laptop etc..Optionally, the abnormal instruction information can be sent by network
To client, to prompt to belong to the ID Card Image of abnormal class about its image uploaded using the user of client.
Optionally, the abnormal class of the input picture may include false identities card image category above-mentioned, and information is not
Full identity card image category, one of copy ID Card Image classification and the certificate image classification of non-identity card, accordingly
Ground generates and sends and is used to indicate a kind of exception class corresponding to the input picture to the abnormal instruction information of client
Not.
Fig. 4 show the structural schematic diagram of 400 embodiment of device of the training classifier of embodiment of the present disclosure offer, such as schemes
Shown in 4, the device 400 of the trained classifier includes that image collection obtains module 401, determining module 402, training module 403.
Wherein, the acquisition module 401, the body for obtaining ID Card Image set, in the ID Card Image set
Part card image is corresponding with classification information, and the classification information indicates the classification of ID Card Image corresponding with the classification information；
The determining module 402, for determining that the output project of classifier, the output project are corresponding with the classification information；It is described
Training module 403, for according to the ID Card Image set and convolutional neural networks (CNN) the training classifier.
Optionally, Fig. 4 shown device can also include the receiving module for receiving input picture, for passing through described point
Class device determines output item purpose output valve corresponding with the input picture and according to the corresponding output item of the input picture
Purpose output valve determines the classification results determining module of the classification of the input picture, for generating the generation of abnormal instruction information
Module, and/or the sending module for sending the abnormal instruction information.
The method that Fig. 4 shown device can execute Fig. 1 and/or embodiment illustrated in fig. 3, the portion that the present embodiment is not described in detail
Point, it can refer to the related description to Fig. 1 and/or embodiment illustrated in fig. 3.The implementation procedure and technical effect of the technical solution referring to
Description in Fig. 1 and/or embodiment illustrated in fig. 3, details are not described herein.
Below with reference to Fig. 5, it illustrates the structural representations for the electronic equipment 500 for being suitable for being used to realize the embodiment of the present disclosure
Figure.Electronic equipment in the embodiment of the present disclosure can include but is not limited to such as mobile phone, laptop, digital broadcasting and connect
Receive device, PDA (personal digital assistant), PAD (tablet computer), PMP (portable media player), car-mounted terminal (such as vehicle
Carry navigation terminal) etc. mobile terminal and such as number TV, desktop computer etc. fixed terminal.Electricity shown in Fig. 5
Sub- equipment is only an example, should not function to the embodiment of the present disclosure and use scope bring any restrictions.
As shown in figure 5, electronic equipment 500 may include processing unit (such as central processing unit, graphics processor etc.)
501, random access can be loaded into according to the program being stored in read-only memory (ROM) 502 or from storage device 508
Program in memory (RAM) 503 and execute various movements appropriate and processing.In RAM 503, it is also stored with electronic equipment
Various programs and data needed for 500 operations.Processing unit 501, ROM 502 and RAM 503 pass through bus or communication line
504 are connected with each other.Input/output (I/O) interface 505 is also connected to bus or communication line 504.
In general, following device can connect to I/O interface 505: including such as touch screen, touch tablet, keyboard, mouse, figure
As the input unit 506 of sensor, microphone, accelerometer, gyroscope etc.；Including such as liquid crystal display (LCD), loudspeaking
The output device 507 of device, vibrator etc.；Storage device 508 including such as tape, hard disk etc.；And communication device 509.It is logical
T unit 509 can permit electronic equipment 500 and wirelessly or non-wirelessly be communicated with other equipment to exchange data.Although Fig. 5 shows
The electronic equipment 500 with various devices is gone out, it should be understood that being not required for implementing or having all dresses shown
It sets.It can alternatively implement or have more or fewer devices.
Particularly, in accordance with an embodiment of the present disclosure, it may be implemented as computer above with reference to the process of flow chart description
Software program.For example, embodiment of the disclosure includes a kind of computer program product comprising be carried on computer-readable medium
On computer program, which includes the program code for method shown in execution flow chart.In such reality
It applies in example, which can be downloaded and installed from network by communication device 509, or from storage device 508
It is mounted, or is mounted from ROM 502.When the computer program is executed by processing unit 501, the embodiment of the present disclosure is executed
Method in the above-mentioned function that limits.
It should be noted that the above-mentioned computer-readable medium of the disclosure can be computer-readable signal media or meter
Calculation machine readable storage medium storing program for executing either the two any combination.Computer readable storage medium for example including but be not limited to
Electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor system, device or device, or any above combination.It is computer-readable
The more specific example of storage medium can include but is not limited to: have electrical connection, the portable computing of one or more conducting wires
Machine disk, hard disk, random access storage device (RAM), read-only memory (ROM), erasable programmable read only memory (EPROM
Or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light storage device, magnetic memory device or above-mentioned
Any appropriate combination.In the disclosure, computer readable storage medium can be it is any include or storage program it is tangible
Medium, the program can be commanded execution system, device or device use or in connection.And in the disclosure,
Computer-readable signal media may include in a base band or as the data-signal that carrier wave a part is propagated, wherein carrying
Computer-readable program code.The data-signal of this propagation can take various forms, including but not limited to electromagnetic signal,
Optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be other than computer readable storage medium
Any computer-readable medium, which can send, propagates or transmit for by instruction execution
System, device or device use or program in connection.The program code for including on computer-readable medium can
To transmit with any suitable medium, including but not limited to: electric wire, optical cable, RF (radio frequency) etc. are above-mentioned any appropriate
Combination.
Above-mentioned computer-readable medium can be included in above-mentioned electronic equipment；It is also possible to individualism, and not
It is fitted into the electronic equipment.
Above-mentioned computer-readable medium carries one or more program, when said one or multiple programs are by the electricity
When sub- equipment executes, so that the method that the electronic equipment executes the training classifier in above-described embodiment.
The calculating of the operation for executing the disclosure can be write with one or more programming languages or combinations thereof
Machine program code, above procedure design language include object oriented program language-such as Java, Smalltalk, C++,
It further include conventional procedural programming language-such as " C " language or similar programming language.Program code can be complete
It executes, partly executed on the user computer on the user computer entirely, being executed as an independent software package, part
Part executes on the remote computer or executes on a remote computer or server completely on the user computer.It is relating to
And in the situation of remote computer, remote computer can include local area network (LAN) or wide area network by the network-of any kind
(WAN)-it is connected to subscriber computer, or, it may be connected to outer computer (such as led to using ISP
Cross internet connection).
Flow chart and block diagram in attached drawing are illustrated according to the system of the various embodiments of the disclosure, method and computer journey
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
A part of one module, program segment or code of table, a part of the module, program segment or code include one or more use
The executable instruction of the logic function as defined in realizing.It should also be noted that in some implementations as replacements, being marked in box
The function of note can also occur in a different order than that indicated in the drawings.For example, two boxes succeedingly indicated are actually
It can be basically executed in parallel, they can also be executed in the opposite order sometimes, and this depends on the function involved.Also it to infuse
Meaning, the combination of each box in block diagram and or flow chart and the box in block diagram and or flow chart can be with holding
The dedicated hardware based system of functions or operations as defined in row is realized, or can use specialized hardware and computer instruction
Combination realize.
Being described in unit involved in the embodiment of the present disclosure can be realized by way of software, can also be by hard
The mode of part is realized.Wherein, the title of unit does not constitute the restriction to the unit itself under certain conditions.
Above description is only the preferred embodiment of the disclosure and the explanation to institute's application technology principle.Those skilled in the art
Member is it should be appreciated that the open scope involved in the disclosure, however it is not limited to technology made of the specific combination of above-mentioned technical characteristic
Scheme, while should also cover in the case where not departing from design disclosed above, it is carried out by above-mentioned technical characteristic or its equivalent feature
Any combination and the other technical solutions formed.Such as features described above has similar function with (but being not limited to) disclosed in the disclosure
Can technical characteristic replaced mutually and the technical solution that is formed.
Claims (10)
1. a kind of method of trained classifier characterized by comprising
ID Card Image set is obtained, the ID Card Image in the ID Card Image set is corresponding with classification information, the class
Other information includes first category information and second category information, and the first category information indicates and the first category information pair
The classification for the ID Card Image answered includes normal category, and the second category information instruction is corresponding with the first category information
The classification of ID Card Image includes abnormal class；
Determining the output project of classifier, the output project includes the first output project and the second output project, and described first
Output project is corresponding with the first category information, and the second output project is corresponding with the second category information；
According to the ID Card Image set and convolutional neural networks (CNN) the training classifier.
2. the method for trained classifier according to claim 1, which is characterized in that the output item purpose of the classifier is defeated
It is being worth out and be 1.
3. the method for trained classifier according to claim 1 or 2, which is characterized in that the second category information includes
Multiple subcategory informations；
The second output project includes multiple sub- output projects, the multiple sub- output project and the multiple subcategory information
It is corresponding.
4. the method for trained classifier according to claim 3, which is characterized in that the multiple subcategory information includes such as
It is one of lower or a variety of:
Indicate the subcategory information of false identities card image category；
Indicate the subcategory information of the infull identity card image category of information；
Indicate the subcategory information of copy ID Card Image classification；
Indicate the subcategory information of the certificate image classification of non-identity card.
5. the method for trained classifier according to claim 1, which is characterized in that according to the ID Card Image set
After the CNN training classifier, further includes:
Receive input picture；
Output item purpose output valve corresponding with the input picture is determined by the classifier；
The classification of the input picture is determined according to the corresponding output item purpose output valve of the input picture.
6. the method for trained classifier according to claim 5, which is characterized in that corresponding according to the input picture
Output item purpose output valve determines after the classification of the input picture, further includes:
In the case where the classification of the input picture is abnormal class, abnormal instruction information is generated.
7. the method for trained classifier according to claim 6, which is characterized in that the reception input picture, including from
Client receives the input picture；
After generating the abnormal instruction information, further includes:
The abnormal instruction information is sent to the client.
8. a kind of device of trained classifier characterized by comprising
Image collection obtains module, the ID Card Image for obtaining ID Card Image set, in the ID Card Image set
Corresponding with classification information, the classification information indicates the classification of ID Card Image corresponding with the classification information；
Determining module, for determining that the output project of classifier, the output project are corresponding with the classification information；
Training module, for according to the ID Card Image set and convolutional neural networks (CNN) the training classifier.
9. a kind of electronic equipment, comprising:
Memory, for storing computer-readable instruction；And
Processor, for running the computer-readable instruction, so that realizing according to claim 1-7 when the processor is run
Any one of described in training classifier method.
10. a kind of non-transient computer readable storage medium, for storing computer-readable instruction, when the computer-readable finger
When order is executed by computer, so that the computer perform claim requires the side of training classifier described in any one of 1-9
Method.
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Application publication date: 20190910 |