WO2021027336A1 - 基于***和签名的身份验证方法、装置和计算机设备 - Google Patents
基于***和签名的身份验证方法、装置和计算机设备 Download PDFInfo
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- WO2021027336A1 WO2021027336A1 PCT/CN2020/088000 CN2020088000W WO2021027336A1 WO 2021027336 A1 WO2021027336 A1 WO 2021027336A1 CN 2020088000 W CN2020088000 W CN 2020088000W WO 2021027336 A1 WO2021027336 A1 WO 2021027336A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/56—Extraction of image or video features relating to colour
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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
Definitions
- This application relates to the field of artificial intelligence technology, in particular to an identity verification method, device, computer equipment, and storage medium based on seals and signatures.
- Identity verification is an indispensable part of modern production and life.
- signatures, seals and other methods are used for identity verification.
- the inventor realizes that the traditional technology has relatively simple verification methods for signatures or seals, and it is easy to be deceived by criminals by forging signatures or seals, causing verification errors. Therefore, the verification accuracy of the traditional technology for identity verification technical solutions has defects.
- the main purpose of this application is to provide an identity verification method, device, computer equipment and storage medium based on seals and signatures, aiming to improve the accuracy of identity verification.
- this application proposes an identity verification method based on seal and signature, which includes the following steps:
- the signature pattern is the projection pattern of a pre-stored virtual three-dimensional seal
- the handwriting features of the handwritten signature are extracted, and the handwriting features are input into the handwriting recognition model based on neural network model training for calculation, so as to obtain the recognition result output by the handwriting recognition model, where the handwriting recognition model Training based on pre-collected handwritten text and sample data composed of writers corresponding to the pre-collected handwritten text;
- the recognition result is the same as the signature text, obtain the designated projection pattern of the pre-stored virtual three-dimensional seal in the designated projection direction, and use the preset image similarity judgment method to determine whether the designated projection pattern is the same as the signature pattern;
- This application provides a seal and signature-based identity verification device, including:
- the acquiring unit is used to acquire the handwritten signature and the signature pattern input by the user, where the signature pattern is a projection pattern of a pre-stored virtual three-dimensional seal;
- the name judging unit is used to recognize the handwritten signature using a preset text recognition technology to obtain the signature text, and determine whether the signature text is the same as the preset name;
- the handwriting recognition unit is used to extract the handwriting features of the handwritten signature if the signature text is the same as the preset name, and input the handwriting features into the handwriting recognition model based on neural network model training for calculation, thereby obtaining the recognition output by the handwriting recognition model
- the handwriting recognition model is trained based on pre-collected handwritten characters and sample data composed of writers corresponding to the pre-collected handwritten characters;
- the signature text identical judging unit is used to judge whether the recognition result is the same as the signature text
- the signature pattern judgment unit is used to obtain the designated projection pattern of the pre-stored virtual three-dimensional seal in the designated projection direction if the recognition result is the same as the signature text, and use a preset image similarity judgment method to determine whether the designated projection pattern and the signature pattern are the same;
- the identity verification passing determining unit is configured to determine that the user's identity verification is passed if the designated projection pattern is the same as the signature pattern.
- the present application provides a computer device, including: one or more processors; memory; one or more computer programs, wherein one or more computer programs are stored in the memory and configured to be executed by one or more processors , One or more computer programs are configured to execute an identity verification method based on seal and signature: wherein, the identity verification method based on seal and signature includes:
- the signature pattern is the projection pattern of a pre-stored virtual three-dimensional seal
- the handwriting features of the handwritten signature are extracted, and the handwriting features are input into the handwriting recognition model based on neural network model training for calculation, so as to obtain the recognition result output by the handwriting recognition model, where the handwriting recognition model Training based on pre-collected handwritten text and sample data composed of writers corresponding to the pre-collected handwritten text;
- the recognition result is the same as the signature text, obtain the designated projection pattern of the pre-stored virtual three-dimensional seal in the designated projection direction, and use the preset image similarity judgment method to determine whether the designated projection pattern is the same as the signature pattern;
- the present application provides a computer-readable storage medium with a computer program stored on the computer-readable storage medium.
- a seal and signature-based identity verification method is implemented, wherein the seal and signature-based identity verification method It includes the following steps:
- the signature pattern is the projection pattern of a pre-stored virtual three-dimensional seal
- the handwriting features of the handwritten signature are extracted, and the handwriting features are input into the handwriting recognition model based on neural network model training for calculation, so as to obtain the recognition result output by the handwriting recognition model, where the handwriting recognition model Training based on pre-collected handwritten text and sample data composed of writers corresponding to the pre-collected handwritten text;
- the recognition result is the same as the signature text, obtain the designated projection pattern of the pre-stored virtual three-dimensional seal in the designated projection direction, and use the preset image similarity judgment method to determine whether the designated projection pattern is the same as the signature pattern;
- the seal and signature-based identity verification method, device, computer equipment, and storage medium of this application obtain the handwritten signature and signature pattern input by the user; recognize the handwritten signature to obtain the signature text; if the signature text is the same as the preset name, then Extract the handwriting features of the handwritten signature, input the handwriting features into the handwriting recognition model based on neural network model training, and then get the recognition result output by the handwriting recognition model; if the recognition result is the same as the signature text, get the pre-stored virtual three-dimensional seal
- FIG. 1 is a schematic flowchart of an identity verification method based on a seal and a signature according to an embodiment of the application;
- FIG. 2 is a schematic block diagram of the structure of a seal and signature-based identity verification device according to an embodiment of the application;
- FIG. 3 is a schematic block diagram of the structure of a computer device according to an embodiment of the application.
- an embodiment of the present application provides a seal and signature-based identity verification method, including the following steps:
- the handwritten signature and the signature pattern input by the user are acquired, wherein the signature pattern is a projection pattern of a pre-stored virtual three-dimensional seal.
- the handwritten signature and signature pattern of this application are used to cross-verify the identity of the user.
- the signature pattern is the projection pattern of a pre-stored virtual three-dimensional seal, thereby increasing the risk of inverting the front shape and pattern of the seal only through the seal pattern. This increases the security of the seal and the accuracy of identity verification.
- the handwritten signature is recognized by the preset text recognition technology to obtain the signature text, and it is determined whether the signature text is the same as the preset name.
- the preset text recognition technology is, for example, OCR (Optical Character Recognition) technology, in which one or more of the following technical means can be used in the recognition process: Grayscale: RGB model is used to represent each image For each pixel, take the average value of R, G, and B for each pixel instead of the original R, G, and B values to obtain the gray value of the image; binarization: divide the pixels of the image into black and white Part, to distinguish the handwritten signature; noise reduction: use median filter, mean filter, adaptive Wiener filter, etc.
- tilt correction use Hough transform and other methods to process the image to correct the photo
- the image is tilted
- text segmentation use projection operations to split text, project a single line of text or multiple lines of text on the X axis, and accumulate the value.
- the text area must have a relatively large value, and the interval area must have no value (or a value Small) to segment a single text
- feature extraction extract the special points of these pixels, such as extreme points, isolated points, etc., as the feature points of the image
- classification use SVM (Support Vector Machine, support vector The machine) classifier performs classification and obtains the initial recognition result.
- the characteristic data is, for example, the position of the repetitive strokes and the number of repetitive strokes in the handwritten text.
- the pen of the handwritten text is divided into multiple points for data collection and analysis, and the pressure value of each point is obtained by identifying the data change trend of the pixel point, The sharpness of the sequence during writing, etc., and then the feature data including the position of the heavy pen and the number of the heavy pen are obtained, where the heavy pen refers to the stroke with the greatest force in the handwritten text.
- the handwriting features of the handwritten signature are extracted, and the handwriting features are input into the handwriting recognition model based on neural network model training for calculation, so as to obtain the recognition result output by the handwriting recognition model ,
- the handwriting recognition model is trained based on pre-collected handwritten characters and sample data composed of writers corresponding to the pre-collected handwritten characters.
- the neural network model can be any model, such as VGG-F model, VGG16 model, ResNet152 model, ResNet50 model, DPN131 model, AlexNet model, DenseNet model, etc., and the DPN model is preferred.
- DPN Deep Path Network
- step S4 it is judged whether the recognition result is the same as the signature text. If the recognition result is the same as the signature text, it means that the handwritten signature is indeed genuine and not a forged signature, and the subsequent verification process can be carried out accordingly.
- the designated projection pattern of the pre-stored virtual three-dimensional seal in the designated projection direction is acquired, and the predetermined image similarity determination method is used to determine whether the designated projection pattern and the signature pattern are the same. Since the virtual three-dimensional seal is not known to outsiders, the designated projection pattern of the pre-stored virtual three-dimensional seal in the designated projection direction is also not known to outsiders, so it can be used for identity verification. Moreover, the virtual three-dimensional seal cannot be deduced from the known projection pattern, thereby ensuring the safety of the seal.
- the preset image similarity judging method can be any method, for example, comparing the corresponding pixels in the two pictures sequentially. If the number of the same pixels or the proportion of the number is greater than a predetermined threshold, the judgment is the same; if the same pixels If the number of points or the proportion of the number is not greater than a predetermined threshold, the determination is different.
- step S6 if the designated projection pattern is the same as the signature pattern, it is determined that the user's identity verification is passed. If the designated projection pattern is the same as the signature pattern, the signature verification is passed, and the aforementioned signature verification is combined to cross-verify the user's identity, and the user's identity verification is determined based on this.
- the handwritten signature is located in a designated picture
- the step S2 of using a preset text recognition technology to recognize the handwritten signature to obtain the signature text includes:
- S202 Obtain pixels whose value of reference value F1 is not equal to A, record them as handwritten signature pixels, and record the graphics formed by handwritten signature pixels as handwritten signature graphics;
- S203 Extract the text features of the handwritten signature graphics, and input them into a preset support vector machine for classification, so as to obtain recognized handwritten text and printed text.
- the preset text recognition technology is used to recognize the handwritten signature to obtain the signature text.
- support vector machine is a kind of generalized linear classifier that binary classification of data according to supervised learning method. It is suitable for comparing the recognized text with the pre-stored text to output the most similar text.
- the character features are, for example, special points in the pixel points corresponding to the character, such as extreme points and isolated points.
- step S3 includes:
- S21 Call the pre-collected sample data and divide it into a training set and a test set; where the sample data includes pre-collected handwritten characters and writers corresponding to the pre-collected handwritten characters;
- the initial handwriting recognition model is recorded as the handwriting recognition model.
- the set handwriting recognition model is realized.
- This application is based on a neural network model to train a handwriting recognition model.
- the neural network model can be VGG16 model, VGG-F model, AlexNet model, ResNet152 model, ResNet50 model, DPN131 model and DenseNet model.
- the stochastic gradient descent method is to randomly sample some training data to replace the entire training set. If the sample size is large, only some of the samples are used to iterate to the optimal solution, which can increase the training speed. Further, the training can also use the reverse conduction law to update the parameters of each layer of the neural network.
- the reverse conduction law is based on the gradient descent method, and its input-output relationship is essentially a mapping relationship: the function of a neural network with n inputs and m outputs is from n-dimensional Euclidean space to m-dimensional Ou A continuous mapping of a finite field in the space. This mapping is highly non-linear, which is beneficial to the update of the parameters of each layer of the neural network model.
- the initial handwriting recognition model In order to obtain the initial handwriting recognition model. Then use the sample data of the test set to verify the initial handwriting recognition model. If the verification passes, the initial handwriting recognition model is recorded as the handwriting recognition model.
- the handwritten signature is located in a designated picture, and the step S3 of extracting the handwriting features of the handwritten signature includes:
- this application obtains the color value of handwritten signature pixels, and records adjacent pixels with color values in the same preset range as the detail unit, and the color value of the detail unit Recorded as the average value of the color values of adjacent pixels; obtain the color value change trend of the adjacent detail unit, use the detail unit, the color value and the color value change trend of the detail unit as the handwriting feature of the handwritten signature, and extract the handwriting feature
- the method uses the detail unit, the color value of the detail unit and the change trend of the color value as the basis for subsequent identification of the writer of the handwritten signature. Among them, due to the different writing habits of different writers, there will be subtle differences in the shape, color, and color value trend of the detail unit when the strength is different, and the correct writer can be identified based on this.
- the step S1 of acquiring the handwritten signature and signature pattern input by the user includes:
- the step S5 of obtaining the designated projection pattern of the pre-stored virtual three-dimensional seal in the designated projection direction includes:
- S501 According to the preset correspondence between the signature and the virtual three-dimensional seal, retrieve the designated virtual three-dimensional seal corresponding to the handwritten signature;
- S503 Record the direction from the designated coordinate point to the origin as the designated projection direction, and project the designated virtual three-dimensional seal from the designated projection direction to obtain the designated projection pattern.
- the designated virtual three-dimensional seal is projected from the designated projection direction to obtain the designated projection pattern.
- this application adopts a virtual three-dimensional seal to prevent the seal from being forged.
- This application uses the preset corresponding relationship between time and space coordinate points to obtain the designated coordinate point of the generation time of the signature pattern, the direction of the designated coordinate point to the origin is recorded as the designated projection direction, and the designated virtual The three-dimensional seal is projected to obtain a flat projection image, which ensures the safety of the signature (planar projection images at different times, so it is impossible to reverse the flat projection image or specify a virtual three-dimensional seal).
- the front side of the designated virtual three-dimensional seal can be any one of the preset sides of the designated virtual three-dimensional seal, preferably a side of the designated virtual three-dimensional seal with a specific pattern, where the specific pattern is, for example, the same as the signature of the physical seal or the same as that of the physical seal
- the embossing corresponding to the signature (the signature of the physical seal is in negative).
- the step S502 of obtaining the designated coordinate point corresponding to the generation time of the signature pattern according to the preset correspondence relationship between time and space coordinate point includes:
- the designated coordinate point corresponding to the generation time of the signature pattern is obtained according to the preset correspondence between time and space coordinate points.
- the designated projection pattern can provide a certain degree of information feedback, which is on the premise of ensuring information security. This will help improve the utilization of information.
- the step S5 of judging whether the designated projection pattern and the signature pattern are the same using a preset image similarity judgment method includes:
- S501 Perform gray-scale processing on the designated projection pattern and the signature pattern respectively to obtain a first gray-scale picture and a second gray-scale picture;
- S502 Calculate the average value Am of the gray values of all pixels in the m-th column or the m-th row of the gray-scale picture, and calculate the average value B of the gray values of all the pixels in the gray-scale picture;
- grayscale refers to the color representing a grayscale color.
- the color represents a grayscale color
- N is the total number of columns or rows in the grayscale image.
- the overall variance is used to measure the difference between the average value Am of the gray values of the pixels in the mth column or the mth row of the grayscale image and the average value B of all pixels in the grayscale image.
- the difference According to the formula: Get the difference between the overall variance of the mth column or mth row of two grayscale images
- Difference of population variance Reflects the difference in the gray value of the mth column or mth row of the two grayscale pictures.
- the gray value of the mth column or row of the first grayscale image is the same or approximately the same as the gray value of the mth column or row of the second grayscale image (approximate judgment to save computing power , And because the overall variance of the two different pictures is generally not equal, the accuracy of the judgment is very high), on the contrary, the gray value of the mth column or mth row of the first grayscale image is the same as the second grayscale value.
- the gray value of the mth column or mth row of the picture is different. judgment Whether it is less than the preset variance error threshold. among them The return value is The maximum value in. If If it is less than the preset variance error threshold, it is determined that the designated projection pattern is the same as the signature pattern.
- the identity verification method based on seal and signature of this application obtains the handwritten signature and signature pattern input by the user; recognizes the handwritten signature to obtain the signature text; if the signature text is the same as the preset name, the handwriting features of the handwritten signature are extracted, Handwriting feature input is calculated based on the handwriting recognition model trained by the neural network model to obtain the recognition result output by the handwriting recognition model; if the recognition result is the same as the signature text, the designated projection pattern of the pre-stored virtual three-dimensional seal in the designated projection direction is obtained ; If the designated projection pattern is the same as the signature pattern, the user's identity verification is determined to pass, thereby improving the accuracy of identity verification.
- an embodiment of the present application provides an identity verification device based on a seal and a signature, including:
- the acquiring unit 10 is configured to acquire a handwritten signature and a signature pattern input by a user, wherein the signature pattern is a projection pattern of a pre-stored virtual three-dimensional seal;
- the name judging unit 20 is used for recognizing the handwritten signature using a preset text recognition technology to obtain the signature text, and judging whether the signature text is the same as the preset name;
- the handwriting recognition unit 30 is configured to extract the handwriting features of the handwritten signature if the signature text is the same as the preset name, and input the handwriting features into the handwriting recognition model based on neural network model training for calculation, thereby obtaining the output of the handwriting recognition model Recognition results, where the handwriting recognition model is trained based on pre-collected handwritten text and sample data composed of writers corresponding to the pre-collected handwritten text;
- the signature text identity judgment unit 40 is used to judge whether the recognition result is the same as the signature text
- the signature pattern judging unit 50 is configured to, if the recognition result is the same as the signature text, obtain the designated projection pattern of the pre-stored virtual three-dimensional seal in the designated projection direction, and use a preset image similarity determination method to determine the designated projection pattern and the signature pattern Are they the same
- the identity verification passing determining unit 60 is configured to determine that the user's identity verification is passed if the designated projection pattern is the same as the signature pattern.
- the handwritten signature is located in the designated picture
- the name judgment unit 20 includes:
- the handwritten signature pixel acquisition subunit is used to acquire the pixels whose reference value F1 is not equal to A, record them as handwritten signature pixels, and record the graphics formed by handwritten signature pixels as handwritten signature graphics;
- the classification subunit is used to extract the text characteristics of the handwritten signature graphics and input them into a preset support vector machine for classification, so as to obtain the recognized handwritten text and printed text.
- the device includes:
- the sample data calling unit is used to call pre-collected sample data and divide it into a training set and a test set; wherein the sample data includes pre-collected handwritten characters and writers corresponding to the pre-collected handwritten characters;
- the training unit is used to input the sample data of the training set into the preset neural network model for training, thereby obtaining the initial handwriting recognition model, where the stochastic gradient descent method is used in the training process;
- the verification unit is used to verify the initial handwriting recognition model with the sample data of the test set
- the marking unit is used to record the initial handwriting recognition model as a handwriting recognition model if the verification of the initial handwriting recognition model is passed.
- the handwritten signature is located in a designated picture
- the handwriting recognition unit 30 includes:
- the handwritten signature pixel mark subunit is used to obtain the pixels corresponding to the handwritten signature and record them as the handwritten signature pixels;
- the detail unit marking sub-unit is used to obtain the color value of the handwritten signature pixel, and record the adjacent pixel with the color value within the same preset range as the detail unit, and record the color value of the detail unit as the adjacent pixel The average value of the color values;
- the handwriting feature extraction subunit is used to obtain the color value change trend of the adjacent detail unit, use the detail unit, the color value and the color value change trend of the detail unit as the handwriting feature of the handwritten signature, and extract the handwriting feature.
- the acquiring unit 10 includes:
- the obtaining subunit is used to obtain the handwritten signature, the signature pattern and the generation time of the signature pattern input by the user;
- the signature pattern judgment unit 50 includes:
- the designated virtual three-dimensional seal retrieval subunit is used to retrieve the designated virtual three-dimensional seal corresponding to the handwritten signature according to the preset correspondence between the signature and the virtual three-dimensional seal;
- the designated coordinate point acquisition subunit is used to take the front center of the designated virtual three-dimensional seal as the origin, and acquire the designated coordinate point corresponding to the generation time of the signature pattern according to the preset correspondence relationship between time and space coordinate points;
- the designated projection pattern acquisition subunit is used to record the direction from the designated coordinate point to the origin as the designated projection direction, and project the designated virtual three-dimensional seal from the designated projection direction to obtain the designated projection pattern.
- the designated coordinate point acquisition subunit includes:
- a plane rectangular coordinate system module used to specify the front center of the virtual three-dimensional seal as the origin, the line between the origin and the preset point in the front as the x-axis, and the straight line perpendicular to the x-axis and passing through the origin in the front as the y-axis , The vertical line passing through the origin on the front is the z-axis, thus establishing a plane rectangular coordinate system;
- the designated coordinate point calculation module is used to obtain the current time and according to the formula:
- the signature pattern determining unit 50 includes:
- the gray-scale processing sub-unit is used to perform gray-scale processing on the designated projection pattern and the signature pattern to obtain the first gray-scale picture and the second gray-scale picture;
- Average value calculation subunit used to calculate the average value Am of the gray values of all pixels in the mth column or mth row of the grayscale image, and calculate the average value B of all pixels in the grayscale image ;
- the overall variance calculation subunit is used according to the formula: Calculate the overall variance of the mth column or mth row of the grayscale image Where N is the total number of columns or rows in the grayscale image;
- the difference of the population variance is calculated as a subunit, which is used according to the formula: Get the difference between the overall variance of the mth column or mth row of two grayscale images among them, Is the overall variance of the mth column or mth row of the first grayscale image, Is the overall variance of the mth column or mth row of the second grayscale image;
- Variance error threshold judgment subunit used to judge Whether it is less than the preset variance error threshold
- the seal and signature-based identity verification device of the present application obtains the handwritten signature and signature pattern input by the user; recognizes the handwritten signature to obtain the signature text; if the signature text is the same as the preset name, the handwriting features of the handwritten signature are extracted, and Handwriting feature input is calculated based on the handwriting recognition model trained by the neural network model to obtain the recognition result output by the handwriting recognition model; if the recognition result is the same as the signature text, the designated projection pattern of the pre-stored virtual three-dimensional seal in the designated projection direction is obtained ; If the designated projection pattern is the same as the signature pattern, the user's identity verification is determined to pass, thereby improving the accuracy of identity verification.
- an embodiment of the present application also provides a computer device.
- the computer device may be a server, and its internal structure may be as shown in the figure.
- the computer equipment includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the computer designed processor is used to provide calculation and control capabilities.
- the memory of the computer device includes a non-volatile storage medium and an internal memory.
- the non-volatile storage medium stores an operating system, a computer program, and a database.
- the memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium.
- the database of the computer equipment is used to store the data used in the identity verification method based on the seal and signature.
- the network interface of the computer device is used to communicate with an external terminal through a network connection.
- the computer program is executed by the processor to realize an identity verification method based on seal and signature.
- the above-mentioned processor executes the above-mentioned seal-and-signature-based identity verification method, wherein the steps included in the method respectively correspond to the steps of executing the seal-and-signature-based identity verification method in the foregoing embodiment, and will not be repeated here.
- the computer device of this application obtains the handwritten signature and signature pattern input by the user; recognizes the handwritten signature to obtain the signature text; if the signature text is the same as the preset name, the handwriting feature of the handwritten signature is extracted, and the handwriting feature input is based on the neural network
- the calculation is performed in the handwriting recognition model completed by the model training to obtain the recognition result output by the handwriting recognition model; if the recognition result is the same as the signature text, the designated projection pattern of the pre-stored virtual three-dimensional seal in the designated projection direction is obtained; if the designated projection pattern is the same If the signature pattern is the same, it is determined that the user's identity verification is passed, thereby improving the accuracy of identity verification.
- An embodiment of the present application also provides a computer-readable storage medium.
- the computer-readable storage medium is a volatile storage medium or a non-volatile storage medium, and a computer program is stored thereon.
- the computer program is executed by a processor, A seal and signature-based identity verification method is implemented, and the steps included in the method respectively correspond to the steps of performing the seal and signature-based identity verification method of the foregoing embodiment, and will not be repeated here.
- the computer-readable storage medium of this application obtains the handwritten signature and signature pattern input by the user; recognizes the handwritten signature to obtain the signature text; if the signature text is the same as the preset name, extract the handwriting characteristics of the handwritten signature and input the handwriting characteristics Perform calculations in the handwriting recognition model completed based on the neural network model training to obtain the recognition result output by the handwriting recognition model; if the recognition result is the same as the signature text, obtain the designated projection pattern of the pre-stored virtual three-dimensional seal in the designated projection direction; if designated If the projection pattern is the same as the signature pattern, it is determined that the user's identity verification is passed, thereby improving the accuracy of identity verification.
- Non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
- Volatile memory may include random access memory (RAM) or external cache memory.
- RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
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Abstract
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- 一种基于***和签名的身份验证方法,其中,包括:获取用户输入的手写签名与签章图案,其中所述签章图案是预存的虚拟立体***的投影图案;采用预设的文字识别技术识别所述手写签名从而获取签名文本,并判断所述签名文本是否与预设的姓名相同;若所述签名文本与预设的姓名相同,则提取所述手写签名的字迹特征,将所述字迹特征输入基于神经网络模型训练完成的字迹识别模型中进行计算,从而得到所述字迹识别模型输出的识别结果,其中所述字迹识别模型基于预先采集的手写文字,以及与所述预先采集的手写文字对应的书写者组成的样本数据训练而成;判断所述识别结果是否与所述签名文本相同;若所述识别结果与所述签名文本相同,则获取预存的虚拟立体***在指定投影方向的指定投影图案,并利用预设的图像相似判断方法判断所述指定投影图案与所述签章图案是否相同;若所述指定投影图案与所述签章图案相同,则判定所述用户的身份验证通过。
- 根据权利要求1所述的基于***和签名的身份验证方法,其中,所述手写签名位于指定图片中,所述采用预设的文字识别技术识别所述手写签名从而获取签名文本的步骤,包括:采集所述指定图片中的像素点的RGB颜色模型中的R颜色通道的数值、G颜色通道的数值和B颜色通道的数值,并根据公式:F1=MIN{ROUND[(a1R+a2G+a3B)/L,0],A},获取参考数值F1,其中MIN为最小值函数,ROUND为四舍五入函数,a1、a2、a3和L均为预设参数,a1、a2、a3均为大于0且小于L的正数,L为大于0的整数,A为预设的取值在范围(0,255)之内阈值参数,R、G、B分别为所述指定图片中的指定像素点的RGB颜色模型中的R颜色通道的数值、G颜色通道的数值和B颜色通道的数值;获取所述参考数值F1的值不等于A的像素点,记为手写签名像素点,并将所述手写签名像素点构成的图形记为手写签名图形;提取所述手写签名图形的文字特征,并输入预设的支持向量机中进行分类,从而获得识别而得的手写文字文本和印刷体文字文本。
- 根据权利要求1所述的基于***和签名的身份验证方法,其中,所述若所述签名文本与预设的姓名相同,则提取所述手写签名的字迹特征,将所述字迹特征输入基于神经网络模型训练完成的字迹识别模型中进行计算,从而得到所述字迹识别模型输出的识别结果,其中所述字迹识别模型基于预先采集的手写文字,以及与所述预先采集的手写文字对应的书写者组成的样本数据训练而成的步骤之前,包括:调用预先采集的样本数据,并分成训练集和测试集;其中,所述样本数据包括预先采集的手写文字,以及与所述预先采集的手写文字对应的书写者;将训练集的样本数据输入到预设的神经网络模型中进行训练,从而得到初始字迹识别模型,其中,训练的过程中采用随机梯度下降法;利用测试集的样本数据验证所述初始字迹识别模型;若所述初始字迹识别模型验证通过,则将所述初始字迹识别模型记为所述字迹识别模型。
- 根据权利要求1所述的基于***和签名的身份验证方法,其中,所述手写签名位于指定图片中,所述提取所述手写签名的字迹特征的步骤,包括:获取所述手写签名对应的像素点,并记为手写签名像素点;获取所述手写签名像素点的颜色数值,并将颜色数值处于同一预设范围内的相邻像素点记为细节单位,并将所述细节单位的颜色数值记为所述相邻像素点的颜色数值的平均值;获取相邻的所述细节单位的颜色数值变化趋势,将所述细节单位、所述细节单位的颜色数值和所述颜色数值变化趋势作为所述手写签名的字迹特征,并提取所述字迹特征。
- 根据权利要求1所述的基于***和签名的身份验证方法,其中,所述获取用户输入的手写签名与签章图案的步骤,包括:获取用户输入的手写签名、签章图案和签章图案的生成时间;所述获取预存的虚拟立体***在指定投影方向的指定投影图案的步骤,包括:根据预设的签名与虚拟立体***的对应关系,调取所述手写签名对应的指定虚拟立体***;以所述指定虚拟立体***的正面中心为原点,根据预设的时间与空间坐标点的对应关系,获取与所述签章图案的生成时间对应的指定坐标点;将所述指定坐标点指向所述原点的方向记为指定投影方向,并从所述指定投影方向对所述指定虚拟立体***进行投影,从而得到指定投影图案。
- 根据权利要求5所述的基于***和签名的身份验证方法,其中,所述以所述指定虚拟立体***的正面中心为原点,根据预设的时间与空间坐标点的对应关系,获取与所述签章图案的生成时间对应的指定坐标点的步骤,包括:以所述指定虚拟立体***的正面中心为原点,所述原点与所述正面中的预设点的连线作为x轴,所述正面中与所述x轴垂直的且过原点的直线作为y轴,所述正面的过所述原点的垂线为z轴,从而建立平面直角坐标系;获取当前时间,并根据公式:x=k1×M+b1;y=k2×D+b2;z=k3×T+b3,获取指定坐标点(x,y,z),其中所述签章图案的生成时间为签章年份的第M月中的第D天中的第T个小时,其中k1、k2、k3、b1、b2和b3均为预设的参数。
- 根据权利要求1所述的基于***和签名的身份验证方法,其中,所述利用预设的图像相似判断方法判断所述指定投影图案与所述签章图案是否相同的步骤,包括:分别对所述指定投影图案与所述签章图案进行灰度化处理,得到第一灰度图片和第二灰度图片;计算灰度图片的第m列或者第m行的所有像素点的灰度值的平均值Am,以及计算灰度图片中所有像素点的灰度值的平均值B;
- 一种基于***和签名的身份验证装置,其中,包括:获取单元,用于获取用户输入的手写签名与签章图案,其中所述签章图案是预存的虚拟立体***的投影图案;姓名判断单元,用于采用预设的文字识别技术识别所述手写签名从而获取签名文本,并判断所述签名文本是否与预设的姓名相同;字迹识别单元,用于若所述签名文本与预设的姓名相同,则提取所述手写签名的字迹特征,将所述字迹特征输入基于神经网络模型训练完成的字迹识别模型中进行计算,从而得到所述字迹识别模型输出的识别结果,其中所述字迹识别模型基于预先采集的手写文字,以及与所述预先采集的手写文字对应的书写者组成的样本数据训练而成;签名文本相同判断单元,用于判断所述识别结果是否与所述签名文本相同;签章图案判断单元,用于若所述识别结果与所述签名文本相同,则获取预存的虚拟立体***在指定投影方向的指定投影图案,并利用预设的图像相似判断方法判断所述指定投影图案与所述签章图案是否相同;身份验证通过判定单元,用于若所述指定投影图案与所述签章图案相同,则判定所述用户的身份验证通过。
- 一种计算机设备,其中,包括:一个或多个处理器;存储器;一个或多个计算机程序,其中所述一个或多个计算机程序被存储在所述存储器中并被配置为由所述一个或多个处理器执行,所述一个或多个计算机程序配置用于执行一种基于***和签名的身份验证方法:其中,所述基于***和签名的身份验证方法包括:获取用户输入的手写签名与签章图案,其中所述签章图案是预存的虚拟立 体***的投影图案;采用预设的文字识别技术识别所述手写签名从而获取签名文本,并判断所述签名文本是否与预设的姓名相同;若所述签名文本与预设的姓名相同,则提取所述手写签名的字迹特征,将所述字迹特征输入基于神经网络模型训练完成的字迹识别模型中进行计算,从而得到所述字迹识别模型输出的识别结果,其中所述字迹识别模型基于预先采集的手写文字,以及与所述预先采集的手写文字对应的书写者组成的样本数据训练而成;判断所述识别结果是否与所述签名文本相同;若所述识别结果与所述签名文本相同,则获取预存的虚拟立体***在指定投影方向的指定投影图案,并利用预设的图像相似判断方法判断所述指定投影图案与所述签章图案是否相同;若所述指定投影图案与所述签章图案相同,则判定所述用户的身份验证通过。
- 根据权利要求9所述的计算机设备,其中,所述手写签名位于指定图片中,所述采用预设的文字识别技术识别所述手写签名从而获取签名文本的步骤,包括:采集所述指定图片中的像素点的RGB颜色模型中的R颜色通道的数值、G颜色通道的数值和B颜色通道的数值,并根据公式:F1=MIN{ROUND[(a1R+a2G+a3B)/L,0],A},获取参考数值F1,其中MIN为最小值函数,ROUND为四舍五入函数,a1、a2、a3和L均为预设参数,a1、a2、a3均为大于0且小于L的正数,L为大于0的整数,A为预设的取值在范围(0,255)之内阈值参数,R、G、B分别为所述指定图片中的指定像素点的RGB颜色模型中的R颜色通道的数值、G颜色通道的数值和B颜色通道的数值;获取所述参考数值F1的值不等于A的像素点,记为手写签名像素点,并将所述手写签名像素点构成的图形记为手写签名图形;提取所述手写签名图形的文字特征,并输入预设的支持向量机中进行分类,从而获得识别而得的手写文字文本和印刷体文字文本。
- 根据权利要求9所述的计算机设备,其中,所述若所述签名文本与预设的姓名相同,则提取所述手写签名的字迹特征,将所述字迹特征输入基于神经网络模型训练完成的字迹识别模型中进行计算,从而得到所述字迹识别模型输出的识别结果,其中所述字迹识别模型基于预先采集的手写文字,以及与所述预先采集的手写文字对应的书写者组成的样本数据训练而成的步骤之前,包括:调用预先采集的样本数据,并分成训练集和测试集;其中,所述样本数据包括预先采集的手写文字,以及与所述预先采集的手写文字对应的书写者;将训练集的样本数据输入到预设的神经网络模型中进行训练,从而得到初始字迹识别模型,其中,训练的过程中采用随机梯度下降法;利用测试集的样本数据验证所述初始字迹识别模型;若所述初始字迹识别模型验证通过,则将所述初始字迹识别模型记为所述字迹识别模型。
- 根据权利要求9所述的计算机设备,其中,所述手写签名位于指定图片中,所述提取所述手写签名的字迹特征的步骤,包括:获取所述手写签名对应的像素点,并记为手写签名像素点;获取所述手写签名像素点的颜色数值,并将颜色数值处于同一预设范围内的相邻像素点记为细节单位,并将所述细节单位的颜色数值记为所述相邻像素点的颜色数值的平均值;获取相邻的所述细节单位的颜色数值变化趋势,将所述细节单位、所述细节单位的颜色数值和所述颜色数值变化趋势作为所述手写签名的字迹特征,并提取所述字迹特征。
- 根据权利要求9所述的计算机设备,其中,所述获取用户输入的手写签名与签章图案的步骤,包括:获取用户输入的手写签名、签章图案和签章图案的生成时间;所述获取预存的虚拟立体***在指定投影方向的指定投影图案的步骤,包括:根据预设的签名与虚拟立体***的对应关系,调取所述手写签名对应的指定虚拟立体***;以所述指定虚拟立体***的正面中心为原点,根据预设的时间与空间坐标点的对应关系,获取与所述签章图案的生成时间对应的指定坐标点;将所述指定坐标点指向所述原点的方向记为指定投影方向,并从所述指定投影方向对所述指定虚拟立体***进行投影,从而得到指定投影图案。
- 根据权利要求13所述的计算机设备,其中,所述以所述指定虚拟立体***的正面中心为原点,根据预设的时间与空间坐标点的对应关系,获取与所述签章图案的生成时间对应的指定坐标点的步骤,包括:以所述指定虚拟立体***的正面中心为原点,所述原点与所述正面中的预设点的连线作为x轴,所述正面中与所述x轴垂直的且过原点的直线作为y轴,所述正面的过所述原点的垂线为z轴,从而建立平面直角坐标系;获取当前时间,并根据公式:x=k1×M+b1;y=k2×D+b2;z=k3×T+b3,获取指定坐标点(x,y,z),其中所述签章图案的生成时间为签章年份的第M月中的第D天中的第T个小时,其中k1、k2、k3、b1、b2和b3均为预设的参数。
- 根据权利要求9所述的计算机设备,其中,所述利用预设的图像相似判断方法判断所述指定投影图案与所述签章图案是否相同的步骤,包括:分别对所述指定投影图案与所述签章图案进行灰度化处理,得到第一灰度图片和第二灰度图片;计算灰度图片的第m列或者第m行的所有像素点的灰度值的平均值Am,以及计算灰度图片中所有像素点的灰度值的平均值B;
- 一种计算机可读存储介质,其中,所述计算机可读存储介质上存储有计算机程序,该计算机程序被处理器执行时实现基于***和签名的身份验证方法,其中,所述基于***和签名的身份验证方法包括以下步骤:获取用户输入的手写签名与签章图案,其中所述签章图案是预存的虚拟立体***的投影图案;采用预设的文字识别技术识别所述手写签名从而获取签名文本,并判断所述签名文本是否与预设的姓名相同;若所述签名文本与预设的姓名相同,则提取所述手写签名的字迹特征,将所述字迹特征输入基于神经网络模型训练完成的字迹识别模型中进行计算,从而得到所述字迹识别模型输出的识别结果,其中所述字迹识别模型基于预先采集的手写文字,以及与所述预先采集的手写文字对应的书写者组成的样本数据训练而成;判断所述识别结果是否与所述签名文本相同;若所述识别结果与所述签名文本相同,则获取预存的虚拟立体***在指定投影方向的指定投影图案,并利用预设的图像相似判断方法判断所述指定投影图案与所述签章图案是否相同;若所述指定投影图案与所述签章图案相同,则判定所述用户的身份验证通过。
- 根据权利要求16所述的计算机可读存储介质,其中,所述手写签名位于指定图片中,所述采用预设的文字识别技术识别所述手写签名从而获取签名文本的步骤,包括:采集所述指定图片中的像素点的RGB颜色模型中的R颜色通道的数值、G颜色通道的数值和B颜色通道的数值,并根据公式:F1=MIN{ROUND[(a1R+a2G+a3B)/L,0],A},获取参考数值F1,其中MIN为最小值函数,ROUND为四舍五入函数,a1、a2、a3和L均为预设参数,a1、a2、a3均为大于0且小于L的正数,L为大于0的整数,A为预设的取值在范围(0,255)之内阈值参数,R、G、B分别为所述指定图片中的指定像素点的RGB颜色模型中的R颜色通道的数值、G颜色通道的数值和B颜色通道的数值;获取所述参考数值F1的值不等于A的像素点,记为手写签名像素点,并将所述手写签名像素点构成的图形记为手写签名图形;提取所述手写签名图形的文字特征,并输入预设的支持向量机中进行分类,从而获得识别而得的手写文字文本和印刷体文字文本。
- 根据权利要求16所述的计算机可读存储介质,其中,所述若所述签名文本与预设的姓名相同,则提取所述手写签名的字迹特征,将所述字迹特征输入基于神经网络模型训练完成的字迹识别模型中进行计算,从而得到所述字迹识别模型输出的识别结果,其中所述字迹识别模型基于预先采集的手写文字,以及与所述预先采集的手写文字对应的书写者组成的样本数据训练而成的步骤之前,包括:调用预先采集的样本数据,并分成训练集和测试集;其中,所述样本数据包括预先采集的手写文字,以及与所述预先采集的手写文字对应的书写者;将训练集的样本数据输入到预设的神经网络模型中进行训练,从而得到初始字迹识别模型,其中,训练的过程中采用随机梯度下降法;利用测试集的样本数据验证所述初始字迹识别模型;若所述初始字迹识别模型验证通过,则将所述初始字迹识别模型记为所述字迹识别模型。
- 根据权利要求16所述的计算机可读存储介质,其中,所述手写签名位于指定图片中,所述提取所述手写签名的字迹特征的步骤,包括:获取所述手写签名对应的像素点,并记为手写签名像素点;获取所述手写签名像素点的颜色数值,并将颜色数值处于同一预设范围内的相邻像素点记为细节单位,并将所述细节单位的颜色数值记为所述相邻像素点的颜色数值的平均值;获取相邻的所述细节单位的颜色数值变化趋势,将所述细节单位、所述细节单位的颜色数值和所述颜色数值变化趋势作为所述手写签名的字迹特征,并提取所述字迹特征。
- 根据权利要求16所述的计算机可读存储介质,其中,所述获取用户输入的手写签名与签章图案的步骤,包括:获取用户输入的手写签名、签章图案和签章图案的生成时间;所述获取预存的虚拟立体***在指定投影方向的指定投影图案的步骤,包括:根据预设的签名与虚拟立体***的对应关系,调取所述手写签名对应的指定虚拟立体***;以所述指定虚拟立体***的正面中心为原点,根据预设的时间与空间坐标点的对应关系,获取与所述签章图案的生成时间对应的指定坐标点;将所述指定坐标点指向所述原点的方向记为指定投影方向,并从所述指定投影方向对所述指定虚拟立体***进行投影,从而得到指定投影图案。
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