CN111553277B - Chinese signature identification method and terminal introducing consistency constraint - Google Patents
Chinese signature identification method and terminal introducing consistency constraint Download PDFInfo
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- 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
- G06V40/30—Writer recognition; Reading and verifying signatures
- G06V40/33—Writer recognition; Reading and verifying signatures based only on signature image, e.g. static signature recognition
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
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- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G06N3/00—Computing arrangements based on biological models
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Abstract
The invention belongs to the field of supervised learning, and particularly discloses a Chinese signature identification method and a terminal introducing consistency constraint, wherein the method utilizes a loss function to train a Chinese signature identification full convolution network model, and the loss function L comprises a Focal loss function; the loss function L also includes a consistency constraint loss function used in conjunction with the Focal loss function. The invention creatively applies consistency loss in the full supervision field of Chinese offline signature authentication, firstly, a first group of training samples are relied to make correct prediction on verification signatures, and accordingly, verification result information is transmitted to corresponding amplified samples through the consistency loss, and more overturned samples can be correctly predicted along with the lengthening of model training time, which also shows that the model reduces the interference of background information and extracts more handwriting information characteristics, thereby improving the difficulty of handwriting information sparseness and improving the signature authentication rate.
Description
Technical Field
The invention relates to the field of supervised learning, in particular to a Chinese signature identification method and a terminal introducing consistency constraint.
Background
Since the last 60 s, foreign researchers began research related to the field of signature authentication. Compared with the foreign research state, the research on signature identification in China starts late, although the development is rapid, the identification rate still needs to be further improved, particularly in the field of Chinese signature identification, because a relatively authoritative public Chinese data set is lacked, the related research on Chinese signature identification is often carried out on a small data set made by individuals, and the research progress of China in the field of off-line signature identification is severely limited. It should be noted that, because the structure of chinese characters is unique and the signature characteristics have their particularity compared with the english language system, most of the effective methods for identifying chinese characters in english signatures cannot be directly transferred to the identification of chinese characters, which has been proved in some documents, but a series of foreign researches still have important reference and reference values.
In the prior art, the identification effect is good when the offline signature identification is carried out by utilizing the Focal loss function. However, in the experimental process, the fact that the strokes of the Chinese signature are relatively thin and long leads to that most of the area of the image is background information, so that the information of the off-line signature is relatively sparse, and the identification effect of the existing algorithm is greatly influenced.
Disclosure of Invention
The invention mainly solves the technical problem of providing a signature identification method which can effectively identify off-line signatures.
In order to solve the technical problems, the invention adopts a technical scheme that: a Chinese signature authentication method introducing consistency constraint is provided. The method comprises the following steps:
training a Chinese signature authentication full convolution network model by using a loss function, wherein the loss function L comprises a Focal loss function; the method is characterized in that:
the loss function L also includes a consistency constraint loss function used in conjunction with the Focal loss function.
Before the training of the full convolution network model for Chinese signature authentication by using the loss function, the method further comprises the following steps:
preparing a Chinese signature identification data set, constructing a Chinese signature identification full convolution network model, and selecting a training signature sample according to the Chinese signature identification data set; the chinese signature authentication dataset includes a reference signature and a verification signature, and the training signature samples include a plurality of training signature sample sets.
The training of the Chinese signature authentication full convolution network model by using the loss function L comprises the following steps:
inputting each training signature sample group into the Chinese signature identification full convolution network model respectively, and performing forward propagation to obtain output results, wherein the output results corresponding to each training signature sample group are marked as opt0, opt1,. and.optx respectively;
and performing one-time back propagation on all output results by using the loss function L to update the full convolution network model for the Chinese signature authentication.
The training signature sample comprises:
a first training signature sample set comprising the reference signature and a verification signature;
preferably, the training signature sample further comprises:
a second training signature sample set and/or a third training signature sample set;
the second training signature sample set comprises a reference signature and the verification signature for flipping foreground and background; the third training signature sample set includes a reference signature and a verification signature that flips the foreground and background.
The foreground refers to the handwriting part of the signature, and the background refers to the part without handwriting
Preferably, the loss function L is calculated by the formula:
L＝FL(y,opt0)+m1|opt0-opt1|+m2|opt0-opt2|+...+mx|opt0-optx|；
where FL (y, opt0) represents the Focal loss function, m1|opt0-opt1|+m2|opt0-opt2|+...+mxI opt0-opt | represents the consistency constraint loss function, m1～mxRespectively representing weights of corresponding training signature sample groups; y represents the actual value of the sample specified.
After the training of the Chinese signature authentication full convolution network model by using the loss function, the method further comprises the following steps:
and authenticating the newly input Chinese signature and/or testing a Chinese signature authentication full convolution network model.
Preferably, when the chinese signature is authenticated, the chinese signature to be authenticated and N reference signatures are respectively combined to form N sets of inputs, the N sets of inputs are input into the chinese signature authentication full convolution network model, N determination results are output, and an average value of the N determination results is counted.
Preferably, the authenticating the chinese signature further includes setting a determination threshold, and when an average value of the N determination results is greater than the determination threshold, the chinese signature is a true signature.
Preferably, a terminal comprises a memory and a processor, the memory stores computer instructions executable on the processor, and the processor executes the computer instructions to perform the steps of the method for identifying chinese signatures with consistency constraints.
The invention has the beneficial effects that:
(1) the method introduces consistency constraint loss on the basis of the Focal loss function, reduces the interference of background information, extracts more handwriting information characteristics and further improves the difficulty of handwriting information sparseness.
(2) Data are augmented by creatively using a mode of turning over the foreground and the background to obtain a plurality of groups of training signature samples, and then the identification rate of signature identification is improved.
Drawings
FIG. 1 is a schematic diagram of an embodiment of the present invention;
Detailed Description
In order to make the technical scheme, the purpose and the beneficial effects of the invention clearer, the invention is further explained by combining the embodiment and the attached drawings.
In an exemplary embodiment, a method for Chinese signature authentication incorporating a consistency constraint includes the steps of:
training a Chinese signature authentication full convolution network model using loss functions, including Focal loss functions.
The loss function also includes a consistency constraint loss function used in conjunction with the Focal loss function.
In the prior art, when offline signature authentication is performed only by using a Focal loss function, because the strokes of the Chinese signature are relatively thin and long, most of the area of an image is background information, the offline signature information is relatively sparse, which greatly influences the identification effect of the conventional algorithm.
And the method creatively utilizes a mode of turning over the foreground and the background to perform data amplification to obtain three groups of training signature samples, and the model transmits the original sample information to the turned samples by minimizing consistency constraint loss.
The model firstly depends on the first group of training samples to make correct prediction on the verification signature, and accordingly, the verification result information is transmitted to the corresponding amplified samples through consistency loss, and more turned samples can be correctly predicted along with the lengthening of the model training time, which also shows that the model reduces the interference of background information and extracts more handwriting information characteristics, thereby improving the difficulty of handwriting information sparseness.
Further, before the training of the full convolution network model for Chinese signature authentication by using the loss function, the method further comprises the following steps:
preparing a Chinese signature identification data set, constructing a Chinese signature identification full convolution network model, and selecting a training signature sample according to the Chinese signature identification data set; the chinese signature authentication dataset includes a reference signature and a verification signature, and the training signature samples include a plurality of training signature sample sets.
The reference signature refers to a collected real signature, and the verification signature refers to a collected similar signature and is also a signature to be verified.
Further, as shown in fig. 1, the training of the full convolution network model for chinese signature authentication by using a loss function L includes:
inputting each training signature sample group into the Chinese signature identification full convolution network model respectively, and performing forward propagation to obtain output results, wherein the output results corresponding to each training signature sample group are marked as opt0, opt1,. and.optx respectively;
as shown in fig. 1, 3 training signature sample sets were selected in this example.
And performing one-time back propagation on all output results by using the loss function L to update the full convolution network model for the Chinese signature authentication.
Further, the selecting the training signature sample includes:
a first training signature sample set comprising the reference signature and a verification signature;
further, the training signature sample further comprises:
a second training signature sample set and/or a third training signature sample set;
the second training signature sample set comprises a reference signature and the verification signature for flipping foreground and background; the third training signature sample set includes a reference signature and a verification signature that flips the foreground and background.
The foreground refers to the handwriting portion of the signature and the background refers to the portion that does not contain the handwriting.
Further, the loss function L is calculated by the formula:
L＝FL(y,opt0)+m1|opt0-opt1|+m2|opt0-opt2|+...+mx|opt0-optx|；
wherein FL (y, opt0) represents the Focal loss function,
FL(y,opt0)＝-at(1-opt0)γlog(opt0),
m1|opt0-opt1|+m2|opt0-opt2|+...+mxI opt0-opt | represents the consistency constraint loss function, m1～mxWeighting parameters respectively representing the corresponding training signature sample groups as balance consistency constraint loss and FocalLoss loss are obtained through network training; y represents the actual value of the sample specified.
Further, using Adam as an optimizer, the sample size of each batch is 128, the initial learning rate of the model is set to 0.001, the learning rate of five thousand batches per iteration is 0.1 times the current learning rate, and when the learning rate is reduced to 0.00001, the learning rate is not further reduced.
Further, the consistency constraint loss constrains the distance of opt0 from opt1 and opt2, respectively, and the model passes the original sample information to the flipped sample by minimizing the consistency constraint loss.
After the Chinese signature authentication full convolution network model is trained by using the loss function, the Chinese signature is authenticated.
Further, when the Chinese signature is authenticated, the Chinese signature to be authenticated is combined with N reference signatures to form N groups of inputs, the N groups of inputs are input into the Chinese signature authentication full convolution network, N judgment results are output, and the average value of the N judgment results is counted.
Further, when the Chinese signature is identified, firstly, the correct prediction is made on the Chinese signature by relying on the first group of training samples, and the verification result information is transmitted to the corresponding amplified sample.
Further, the formula for counting the average value of the N determination results is:
wherein R isuFor the reference signature set of user u, P (u | R)uS) is the summary that the authentication signature S belongs to the user uThe ratio of the total weight of the particles,
is the output of the convolutional neural network model.
Further, the authentication of the chinese signature further includes setting a determination threshold, where T is 0.5, if P (u | R)u,S)>And T, the Chinese signature is a real signature, otherwise, the Chinese signature is a fake signature.
Further, the Chinese signature authentication system introducing the consistency constraint comprises a memory and a processor, wherein the memory module stores the reference signature and the verification signature, and the processor realizes the steps of the Chinese signature authentication method introducing the consistency constraint.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (8)
1. A Chinese signature identification method introducing consistency constraint comprises the following steps:
training a Chinese signature authentication full convolution network model by using a loss function, wherein the loss function L comprises a Focal loss function; the method is characterized in that:
the loss function L further comprises a consistency constraint loss function used in conjunction with the Focal loss function;
before the training of the Chinese signature authentication full convolution network model by using the loss function, the method further comprises the following steps:
preparing a Chinese signature identification data set, constructing a Chinese signature identification full convolution network model, and selecting a training signature sample according to the Chinese signature identification data set; the Chinese signature authentication dataset comprises a reference signature and a verification signature, and the training signature sample comprises a plurality of training signature sample groups;
the training of the Chinese signature authentication full convolution network model by using the loss function L comprises the following steps:
inputting each training signature sample group into the Chinese signature identification full convolution network model respectively, and performing forward propagation to obtain output results, wherein the output results corresponding to each training signature sample group are marked as opt0, opt1,. and.optx respectively;
and performing one-time back propagation on all output results by using the loss function L to update the full convolution network model for the Chinese signature authentication.
2. The method of claim 1, wherein the method comprises: the training signature sample comprises:
a first training signature sample set including the reference signature and a verification signature.
3. The method of claim 2, wherein the method comprises: the training signature samples further comprise:
a second training signature sample set and/or a third training signature sample set;
the second training signature sample set comprises a reference signature and the verification signature for flipping foreground and background; the third training signature sample group comprises a reference signature and a verification signature for turning over a foreground and a background;
the foreground refers to the handwriting portion of the signature and the background refers to the portion that does not contain the handwriting.
4. The method of claim 1, wherein the method comprises: the loss function L is calculated by the formula:
L=FL(y,opt0)+m1|opt0-opt1|+m2|opt0-opt2|+...+mx|opt0-optx|;
where FL (y, opt0) represents the Focal loss function, m1|opt0-opt1|+m2|opt0-opt2|+...+mxI opt0-opt | represents the consistency constraint loss function, m1~mxRespectively generation by generationThe table corresponds to the weights of the training signature sample set; y represents the actual value of the sample specified.
5. The method of claim 1, wherein the method comprises: after the training of the Chinese signature authentication full convolution network model by using the loss function, the method further comprises the following steps:
and authenticating the newly input Chinese signature and/or testing a Chinese signature authentication full convolution network model.
6. The method of claim 5, wherein the method comprises: and when the Chinese signature is identified, inputting the Chinese signature to be identified into the Chinese signature identification full convolution network model, outputting N judgment results, and counting the average value of the N judgment results.
7. The method of claim 6, wherein the method comprises: and when the Chinese signature is identified, setting a judgment threshold value, and when the average value of the N judgment results is greater than the judgment threshold value, the Chinese signature is a real signature.
8. A terminal comprising a memory and a processor, the memory having stored thereon computer instructions executable on the processor, the terminal comprising: the computer instructions when executed by the processor perform the steps of a method for chinese signature authentication incorporating a consistency constraint according to any one of claims 1 to 7.
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