CN112861649B - Fingerprint signature generation method and device, electronic equipment and computer storage medium - Google Patents
Fingerprint signature generation method and device, electronic equipment and computer storage medium Download PDFInfo
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- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
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Abstract
The invention relates to a data processing technology, and discloses a fingerprint signature generation method, which comprises the following steps: acquiring a fingerprint image of a user, and extracting a fingerprint area from the fingerprint image to obtain the fingerprint area; performing texture track analysis on the fingerprint area to obtain texture track data; acquiring fingerprint pressing data of a user, and performing pressure data analysis on the fingerprint pressing data to obtain pressure quantized data; acquiring an electronic signature of a user, and performing signature recognition on the electronic signature to obtain signature data; and carrying out equidistant data fusion on the texture track data, the pressure quantization data and the signature data to obtain the fingerprint signature of the user. The invention also provides a fingerprint signature generating device, equipment and a computer readable storage medium. In addition, the invention also relates to a blockchain technology, and the electronic signature can be stored in a blockchain node. The invention can improve the security of the electronic signature.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a fingerprint signature generating method, a fingerprint signature generating device, an electronic device, and a computer readable storage medium.
Background
In daily life, many people are subjected to the situation that signature is needed, for example, handwriting signature is performed in a contract file, electronic signature is performed in an email, and each transaction is authorized by signature.
With the development of networks, electronic signatures have gradually replaced handwritten signatures, becoming the primary signature method. However, in the case of electronic signature, the pen force application and writing habit of fine strokes of a signer cannot be reflected due to the difference of the performance of the devices used in the signing, so that the identity of the signer cannot be identified, the security of the electronic signature is not high, and the legal effect of the electronic signature cannot be ensured.
Disclosure of Invention
The invention provides a fingerprint signature generation method, a fingerprint signature generation device, electronic equipment and a computer readable storage medium, and mainly aims to improve the security of an electronic signature.
In order to achieve the above object, the present invention provides a fingerprint signature generation method, including:
Acquiring a fingerprint image of a user, and extracting a fingerprint area from the fingerprint image to obtain the fingerprint area;
performing texture track analysis on the fingerprint region to obtain texture track data;
Acquiring fingerprint pressing data of a user, and performing pressure data analysis on the fingerprint pressing data to obtain pressure quantized data;
Acquiring an electronic signature of a user, and performing signature recognition on the electronic signature to obtain signature data;
and carrying out equidistant data fusion on the texture track data, the pressure quantized data and the signature data to obtain the fingerprint signature of the user.
Optionally, the fingerprint region extracting of the fingerprint image includes:
downsampling the fingerprint image to obtain a global feature map;
up-sampling the global feature map by a first threshold multiple to obtain an intermediate feature map;
Up-sampling the intermediate feature image by a second threshold multiple to obtain a fingerprint restoration image;
Calculating target class probability of each pixel point in the fingerprint restoration image belonging to a preset target class by using a first activation function;
and dividing the fingerprint restored image according to the target class probability to obtain a fingerprint area.
Optionally, before the analyzing the texture trajectory of the fingerprint region, the method further includes:
And carrying out gray pixel conversion and contrast stretching treatment on the fingerprint region.
Optionally, the performing texture trajectory analysis on the fingerprint area to obtain texture trajectory data includes:
Calculating a gray frequency field of the fingerprint region;
calculating the sum of tangential pixels and the sum of normal pixels of each pixel point in the fingerprint area;
and integrating the gray frequency field, the sum of the tangential pixels and the sum of the normal pixels to obtain texture track data.
Optionally, the performing texture trajectory analysis on the fingerprint area to obtain texture trajectory data includes:
Calculating a gray frequency field of the fingerprint region;
calculating the sum of tangential pixels and the sum of normal pixels of each pixel point in the fingerprint area;
and integrating the gray frequency field, the sum of the tangential pixels and the sum of the normal pixels to obtain texture track data.
Optionally, the calculating the gray frequency field of the fingerprint region includes:
the gray frequency field f of the fingerprint region is calculated using the following calculation formula:
v (x) is the total vertical gray level change amount of any two pixel points on the fingerprint area; x 1 and x 2 are respectively the transverse coordinate values of any two different pixel points on the fingerprint area; h (x) represents a gray function in a vertical direction of the fingerprint region; a m is the average amplitude of the fingerprint waveform between any two different pixels on the fingerprint area.
Optionally, the fusing the texture track data, the pressure quantized data and the signature data at equal intervals to obtain a fingerprint signature of the user includes:
equidistant splitting is carried out on the texture track data according to a first preset length, so that first split data are obtained;
equidistant splitting is carried out on the pressure quantized data according to a second preset length, so that second split data are obtained;
equidistant splitting is carried out on the signature data according to a third preset length, and third split data are obtained;
and performing interpenetration combination on the first split data, the second split data and the third split data to obtain the fingerprint signature of the user.
Optionally, the performing pressure data analysis on the fingerprint pressing data to obtain pressure quantized data includes:
the instantaneous pressure value Z and the pressure gradient field T (x, y) of the fingerprint compression data are calculated using the following calculation formula:
Z＝|T(x,y)|
wherein Z is the instantaneous pressure value of the pixel point (x, y); a pressure gradient field for the T (x, y) pixel points (x, y); g x (x, y) is the partial derivative of the instantaneous pressure value Z at point (x, y) with respect to x G y (x, y) is the partial derivative of the instantaneous pressure value Z at point (x, y) with respect to yΘ (x, y) is the direction of application of the instantaneous pressure value Z;
The instantaneous pressure value Z and the pressure gradient field |t (x, y) | are determined as pressure quantized data.
In order to solve the above-mentioned problem, the present invention also provides a fingerprint signature generating apparatus, the apparatus comprising:
The region extraction module is used for acquiring a fingerprint image of a user, and extracting a fingerprint region from the fingerprint image to obtain a fingerprint region;
The track analysis module is used for carrying out texture track analysis on the fingerprint area to obtain texture track data;
The pressure analysis module is used for acquiring fingerprint pressing data of a user, and performing pressure data analysis on the fingerprint pressing data to obtain pressure quantized data;
The signature recognition module is used for acquiring an electronic signature of a user, and carrying out signature recognition on the electronic signature to obtain signature data;
And the data fusion module is used for carrying out equidistant data fusion on the texture track data, the pressure quantization data and the signature data to obtain a fingerprint signature of the user.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
at least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the fingerprint signature generation method described above.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium including a storage data area storing created data and a storage program area storing a computer program; wherein the computer program when executed by a processor implements the fingerprint signature generation method described above.
According to the embodiment of the invention, the fingerprint region is extracted from the fingerprint image, so that the analysis of the region without fingerprint information in the fingerprint image can be avoided, and the efficiency of extracting the texture track data from the fingerprint image is improved; the fingerprint pressing data is subjected to pressure data analysis to obtain pressure quantized data, signature recognition is carried out on the user electronic signature to obtain signature data, texture track data, the pressure quantized data and the signature data are fused into fingerprint signatures at equal intervals, the fused fingerprint signatures comprise more accurate pen transporting force and fine strokes, the matching rate of the fingerprint signatures and templates prestored in a database is higher, and therefore the accuracy and safety of the fingerprint signatures are improved. Therefore, the fingerprint signature generation method, the fingerprint signature generation device and the computer readable storage medium can improve the security of the electronic signature.
Drawings
Fig. 1 is a flowchart of a fingerprint signature generating method according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a fingerprint signature generating apparatus according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of an electronic device for implementing a fingerprint signature generating method according to an embodiment of the present invention;
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a fingerprint signature generation method. The execution subject of the fingerprint signature generation method includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the fingerprint signature generation method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Referring to fig. 1, a flowchart of a fingerprint signature generating method according to an embodiment of the present invention is shown. In this embodiment, the fingerprint signature generation method includes:
S1, acquiring a fingerprint image of a user, and extracting a fingerprint area from the fingerprint image to obtain the fingerprint area.
In the embodiment of the invention, the fingerprint image is an image containing the fingerprint of the user, for example, an image containing an electronic photograph of the fingerprint of the user or a written text with the fingerprint of the user, etc.
In detail, the embodiment of the invention can take a picture through a mobile phone or any equipment with a camera shooting function to acquire the fingerprint image of the user.
In practical application, since the acquired fingerprint image of the user may contain a large amount of useless information, for example, only one tenth of the area in a large fingerprint image contains fingerprint information of the user, if the acquired fingerprint image is directly analyzed, a large amount of calculation resources are occupied, and efficiency of extracting the fingerprint information is reduced.
In detail, the fingerprint region extraction of the fingerprint image includes:
downsampling the fingerprint image to obtain a global feature map;
up-sampling the global feature map by a first threshold multiple to obtain an intermediate feature map;
Up-sampling the intermediate feature image by a second threshold multiple to obtain a fingerprint restoration image;
Calculating target class probability of each pixel point in the fingerprint restoration image belonging to a preset target class by using a first activation function;
and dividing the fingerprint restored image according to the target class probability to obtain a fingerprint area.
Specifically, the segmentation processing includes classifying the pixel points in the fingerprint restoration image, the preset target category includes a plurality of preset categories, and the segmentation processing is performed on the fingerprint restoration image according to the preset target category probability to obtain a fingerprint area, namely, the preset category with the maximum target category probability of the target pixel point in the fingerprint restoration image is determined as the pixel category of the target pixel point.
For example, the preset target category includes a category a, a category B and a category C, the probability that the target pixel point in the fingerprint restoration image is the category a in the preset target category is 20%, the probability that the target pixel point in the fingerprint restoration image is the category B in the preset target category is 70%, and the probability that the target pixel point in the fingerprint restoration image is the category C in the preset target category is 40%, then the target pixel point in the fingerprint restoration image is determined to be the category B, when all the pixel points in the fingerprint restoration image complete the segmentation operation, the region where the pixel points in the fingerprint restoration image are classified as the pixels of the fingerprint category is determined to be the fingerprint region.
In the embodiment of the invention, the global feature map is subjected to up-sampling of the first threshold multiple to obtain the intermediate feature map, and then the intermediate feature map is subjected to up-sampling of the second threshold multiple to obtain the fingerprint restoration image, so that the loss of image features in the fingerprint restoration image caused by the excessive up-sampling multiple when the global feature map is directly up-sampled to the fingerprint restoration image is avoided, and the integrity of feature information in the fingerprint restoration image is improved.
S2, performing texture track analysis on the fingerprint area to obtain texture track data.
In an embodiment of the present invention, before the analyzing the texture trace of the fingerprint area, the method may further include:
And carrying out gray pixel conversion and contrast stretching treatment on the fingerprint region.
In detail, the gray pixel conversion is to input all pixel points in the fingerprint area into a gray value conversion formula to perform gray value conversion, so as to generate the gray image.
The gray value conversion formula is as follows:
Gary＝0.30*R+0.59*G+0.11*B
Wherein R, G and B are three components of the pixels in the fingerprint region, and Gary is a gray image obtained by performing gray pixel conversion on the fingerprint region.
Further, the contrast stretching process includes:
traversing and counting gray probability density of each pixel point in the fingerprint area after gray pixel conversion;
And stretching and transforming the gray probability density of each pixel point by using a preset gray transformation function to obtain a fingerprint area after the contrast stretching treatment.
Specifically, the embodiment of the invention can utilize the gray density function pre-compiled in MATLAB to count the gray probability density of each pixel point in the fingerprint area after gray pixel conversion.
In detail, the embodiment of the invention can carry out contrast stretching treatment on the fingerprint region by using the following stretching transformation function:
Db＝f(Da)＝a*Da+b
Wherein a is a preset linear slope, D a is a gray value of the fingerprint region before the contrast stretching, D b is a gray value of the fingerprint region after the contrast stretching, and b is an intercept of D b on the Y axis.
Because the fingerprint region obtained by directly extracting the fingerprint region from the acquired fingerprint image has the conditions of dark image, unclear image and the like, the method is unfavorable for analyzing the data contained in the fingerprint region, and therefore, the embodiment of the invention carries out gray pixel conversion and contrast stretching treatment on the fingerprint region.
In detail, the performing texture trajectory analysis on the fingerprint region to obtain texture trajectory data includes:
Calculating a gray frequency field of the fingerprint region;
calculating the sum of tangential pixels and the sum of normal pixels of each pixel point in the fingerprint area;
and integrating the gray frequency field, the sum of the tangential pixels and the sum of the normal pixels to obtain texture track data.
Specifically, the embodiment of the invention calculates the gray frequency field f of the fingerprint region by using the following calculation formula:
v (x) is the total vertical gray level change amount of any two pixel points on the fingerprint area; x 1 and x 2 are respectively the transverse coordinate values of any two different pixel points on the fingerprint area; h (x) represents a gray function in a vertical direction of the fingerprint region; a m is the average amplitude of the fingerprint waveform between any two different pixels on the fingerprint area.
And S3, acquiring fingerprint pressing data of a user, and performing pressure data analysis on the fingerprint pressing data to obtain pressure quantized data.
In the embodiment of the invention, any pressure sensor function device can be used for acquiring fingerprint pressing data of a user, wherein the fingerprint pressing data refers to pressure data generated when the user presses the pressure sensor function device, and the fingerprint pressing data include, but are not limited to, the pressure change trend, the pressure application direction and the like.
The step of analyzing the pressure data of the fingerprint pressing data to obtain pressure quantized data comprises the following steps:
the instantaneous pressure value Z and the pressure gradient field T (x, y) of the fingerprint compression data are calculated using the following calculation formula:
Z＝|T(x,y)|
wherein Z is the instantaneous pressure value of the pixel point (x, y); a pressure gradient field for the T (x, y) pixel points (x, y); g x (x, y) is the partial derivative of the instantaneous pressure value Z at point (x, y) with respect to x G y (x, y) is the partial derivative of the instantaneous pressure value Z at point (x, y) with respect to yΘ (x, y) is the direction of application of the instantaneous pressure value Z;
The instantaneous pressure value Z and the pressure gradient field |t (x, y) | are determined as pressure quantized data.
In detail, the application direction θ (x, y) of the pressure is calculated using the following calculation direction formula:
Wherein V x (x, y) is the pressure applied in the transverse axis direction; v y (x, y) is the pressure applied in the direction of the longitudinal axis; θ (x, y) is the pressure direction field function; w is an initial direction parameter; g x (x, y) is the partial derivative of the instantaneous pressure function Z at point (x, y) with respect to x G y (x, y) is the partial derivative of the instantaneous pressure function Z at point (x, y) with respect to y/>I is a horizontal axis error preset for the point (x, y), and j is a vertical axis error preset for the point (x, y).
S4, acquiring an electronic signature of the user, and performing signature recognition on the electronic signature to obtain signature data.
In the embodiment of the invention, the python statement with the data grabbing function can be utilized to acquire the electronic signature of the user from the blockchain used for storing the electronic signature of the user. The security of the electronic signature can be improved by utilizing the confidentiality of the blockchain to the data, and meanwhile, the efficiency of acquiring the electronic signature of the user can be improved by utilizing the high throughput of the blockchain to the data.
In the embodiment of the invention, the electronic signature is identified by utilizing an OCR (Optical Character Recognition ) model to obtain signature data, and the electronic signature can be any electronic form of user signature, such as an electronic seal, an electronic image with the user signature, and the like.
In detail, the OCR model adopts a Bi-LSTM-CRF structure, and comprises the following steps:
a word/word vector layer, configured to convert words and characters in a text contained in the electronic signature into a word/word vector;
The Bi-LSTM layer is used for dividing the character/word vector, coding the divided character/word vector to obtain a coding representation of the character/word vector, and marking the divided character/word vector by using the coding representation to obtain a key value and a result value;
and the CRF layer is used for splicing the key values and the result values of the same type into signature data.
The word/word vector layer converts words and characters in texts contained in the electronic signature into word/word vectors by using trained word vectors as initialization parameters, wherein the trained word vectors are a set of preset standard conversion rules.
Since more texts are possibly contained in the electronic signature, sentences in the texts are possibly longer, if only character conversion is performed, text stiction can possibly occur, and subsequent text error correction is not facilitated.
Preferably, the Bi-LSTM layer can divide the word/word vector by adopting java language and encode the divided word/word vector, wherein the encoded representation comprises six label types of Key-B, value-B, key-I, value-I, other-B and Other-I. Wherein Key is a Key Value, value is a result Value, and Other is Other values.
In the embodiment of the invention, the CRF layer is utilized to splice Key values and result values of the same type, such as Key-B, key-I or Value-B, value-I. And after all the key values and the result values are spliced, the signature data can be obtained, wherein the signature data is computer data in the form of IO data stream.
And S5, carrying out equidistant data fusion on the texture track data, the pressure quantized data and the signature data to obtain a fingerprint signature of the user.
In the embodiment of the present invention, the performing equidistant data fusion on the texture track data, the pressure quantization data and the signature data to obtain a fingerprint signature of a user includes:
equidistant splitting is carried out on the texture track data according to a first preset length, so that first split data are obtained;
equidistant splitting is carried out on the pressure quantized data according to a second preset length, so that second split data are obtained;
equidistant splitting is carried out on the signature data according to a third preset length, and third split data are obtained;
and performing interpenetration combination on the first split data, the second split data and the third split data to obtain the fingerprint signature of the user.
In detail, equidistant splitting refers to splitting a piece of data into multiple pieces of data according to a preset length, for example, data & including data: 123456, when the preset length is "2", the data & is split into 12, 34 and 56.
In the embodiment of the present invention, the first preset length, the second preset length and the third preset length may be the same or different.
Specifically, the first split data, the second split data and the third split data are alternately combined according to a preset sequence, for example, the first split data are ab, cd and ef, the second split data are gh, ij and kl, and the third split data are mn, op and qr; when the preset sequence is 'first split data-second split data-third split data', the first split data, the second split data and the third split data are alternately combined according to the preset sequence to be: ab+gh+mn+cd+ij+op+ef+kl+qr.
In the embodiment of the invention, the texture track data, the pressure quantized data and the signature data are subjected to equidistant data fusion to generate the fingerprint signature of the user, and the user identity can be authenticated by the texture track data, the pressure quantized data and the signature data of the user fingerprint, so that the safety of user identity authentication by using the user signature is improved.
According to the embodiment of the invention, the fingerprint region is extracted from the fingerprint image, so that the analysis of the region without fingerprint information in the fingerprint image can be avoided, and the efficiency of extracting the texture track data from the fingerprint image is improved; the fingerprint pressing data is subjected to pressure data analysis to obtain pressure quantized data, signature recognition is carried out on the user electronic signature to obtain signature data, texture track data, the pressure quantized data and the signature data are fused into fingerprint signatures at equal intervals, the fused fingerprint signatures comprise more accurate pen transporting force and fine strokes, the matching rate of the fingerprint signatures and templates prestored in a database is higher, and therefore the accuracy and safety of the fingerprint signatures are improved. Therefore, the fingerprint signature generation method provided by the invention can improve the security of the electronic signature.
Fig. 2 is a schematic block diagram of the fingerprint signature generating apparatus according to the present invention.
The fingerprint signature generating apparatus 100 of the present invention may be mounted in an electronic device. The fingerprint signature generating apparatus may include a region extraction module 101, a trace analysis module 102, a pressure analysis module 103, a signature recognition module 104, and a data fusion module 105 according to the implemented functions. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
The region extraction module 101 is configured to obtain a fingerprint image of a user, and perform fingerprint region extraction on the fingerprint image to obtain a fingerprint region;
The track analysis module 102 is configured to perform texture track analysis on the fingerprint area to obtain texture track data;
The pressure analysis module 103 is configured to obtain fingerprint pressing data of a user, perform pressure data analysis on the fingerprint pressing data, and obtain pressure quantized data;
The signature recognition module 104 is configured to obtain an electronic signature of a user, and perform signature recognition on the electronic signature to obtain signature data;
the data fusion module 105 is configured to perform equidistant data fusion on the texture track data, the pressure quantized data and the signature data, so as to obtain a fingerprint signature of the user.
In detail, each module in the fingerprint signature generating apparatus 100, when executed by a processor of an electronic device, may implement a fingerprint signature generating method comprising the following operation steps:
Step one, the region extraction module 101 obtains a fingerprint image of a user, and performs fingerprint region extraction on the fingerprint image to obtain a fingerprint region.
In the embodiment of the invention, the fingerprint image is an image containing the fingerprint of the user, for example, an image containing an electronic photograph of the fingerprint of the user or a written text with the fingerprint of the user, etc.
In detail, the region extraction module 101 according to the embodiment of the present invention may take a photograph through a mobile phone or any device with a camera function to obtain a fingerprint image of a user.
In practical applications, since the acquired fingerprint image of the user may contain a large amount of useless information, for example, only one tenth of the area in a large fingerprint image contains fingerprint information of the user, if the acquired fingerprint image is directly analyzed, a large amount of calculation resources are occupied, and efficiency of extracting the fingerprint information is reduced, so that the area extracting module 101 of the embodiment of the present invention uses the convolutional neural network with the feature extracting function to extract the fingerprint image, so as to reduce the size of the fingerprint image and improve efficiency of acquiring the fingerprint information from the fingerprint image, where the fingerprint area is an image area containing the fingerprint information in the fingerprint image.
In detail, the region extraction module 101 performs fingerprint region extraction on the fingerprint image by:
downsampling the fingerprint image to obtain a global feature map;
up-sampling the global feature map by a first threshold multiple to obtain an intermediate feature map;
Up-sampling the intermediate feature image by a second threshold multiple to obtain a fingerprint restoration image;
Calculating target class probability of each pixel point in the fingerprint restoration image belonging to a preset target class by using a first activation function;
and dividing the fingerprint restored image according to the target class probability to obtain a fingerprint area.
Specifically, the segmentation processing includes classifying the pixel points in the fingerprint restoration image, the preset target category includes a plurality of preset categories, and the segmentation processing is performed on the fingerprint restoration image according to the preset target category probability to obtain a fingerprint area, namely, the preset category with the maximum target category probability of the target pixel point in the fingerprint restoration image is determined as the pixel category of the target pixel point.
For example, the preset target category includes a category a, a category B and a category C, the probability that the target pixel point in the fingerprint restoration image is the category a in the preset target category is 20%, the probability that the target pixel point in the fingerprint restoration image is the category B in the preset target category is 70%, and the probability that the target pixel point in the fingerprint restoration image is the category C in the preset target category is 40%, then the target pixel point in the fingerprint restoration image is determined to be the category B, when all the pixel points in the fingerprint restoration image complete the segmentation operation, the region where the pixel points in the fingerprint restoration image are classified as the pixels of the fingerprint category is determined to be the fingerprint region.
In the embodiment of the invention, the global feature map is subjected to up-sampling of the first threshold multiple to obtain the intermediate feature map, and then the intermediate feature map is subjected to up-sampling of the second threshold multiple to obtain the fingerprint restoration image, so that the loss of image features in the fingerprint restoration image caused by the excessive up-sampling multiple when the global feature map is directly up-sampled to the fingerprint restoration image is avoided, and the integrity of feature information in the fingerprint restoration image is improved.
And step two, the track analysis module 102 performs texture track analysis on the fingerprint area to obtain texture track data.
In the embodiment of the present invention, the fingerprint signature generating device 100 is further configured to:
And carrying out gray pixel conversion and contrast stretching treatment on the fingerprint region.
In detail, the gray pixel conversion is to input all pixel points in the fingerprint area into a gray value conversion formula to perform gray value conversion, so as to generate the gray image.
The gray value conversion formula is as follows:
Gary＝0.30*R+0.59*G+0.11*B
Wherein R, G and B are three components of the pixels in the fingerprint region, and Gary is a gray image obtained by performing gray pixel conversion on the fingerprint region.
Further, the contrast stretching process includes:
traversing and counting gray probability density of each pixel point in the fingerprint area after gray pixel conversion;
And stretching and transforming the gray probability density of each pixel point by using a preset gray transformation function to obtain a fingerprint area after the contrast stretching treatment.
Specifically, the trajectory analysis module 102 may use a pre-compiled gray density function in MATLAB to count the gray probability density of each pixel in the fingerprint region after gray pixel conversion.
In detail, the trajectory analysis module 102 performs a contrast stretching process on the fingerprint region using the following stretching transformation function:
Db＝f(Da)＝a*Da+b
Wherein a is a preset linear slope, D a is a gray value of the fingerprint region before the contrast stretching, D b is a gray value of the fingerprint region after the contrast stretching, and b is an intercept of D b on the Y axis.
Because the fingerprint region obtained by directly extracting the fingerprint region from the acquired fingerprint image has dark images, unclear images and the like, the subsequent analysis of the data contained in the fingerprint region is not facilitated, and therefore, the trace analysis module 102 in the embodiment of the invention performs gray pixel conversion and contrast stretching treatment on the fingerprint region. In the embodiment of the invention, before the texture trajectory analysis is performed on the fingerprint region, the trajectory analysis module 102 performs gray pixel conversion and contrast stretching on the fingerprint region, so that the texture trajectory characteristics of the fingerprint in the fingerprint region can be highlighted, and the accuracy of obtaining the texture trajectory data by performing the texture trajectory analysis on the fingerprint region is improved.
In detail, the trace analysis module 102 performs texture trace analysis on the fingerprint region to obtain texture trace data by adopting the following operations:
Calculating a gray frequency field of the fingerprint region;
calculating the sum of tangential pixels and the sum of normal pixels of each pixel point in the fingerprint area;
and integrating the gray frequency field, the sum of the tangential pixels and the sum of the normal pixels to obtain texture track data.
Specifically, the trajectory analysis module 102 according to the embodiment of the present invention calculates the gray frequency field f of the fingerprint region using the following calculation formula:
v (x) is the total vertical gray level change amount of any two pixel points on the fingerprint area; x 1 and x 2 are respectively the transverse coordinate values of any two different pixel points on the fingerprint area; h (x) represents a gray function in a vertical direction of the fingerprint region; a m is the average amplitude of the fingerprint waveform between any two different pixels on the fingerprint area.
And thirdly, the pressure analysis module 103 acquires fingerprint pressing data of a user, and performs pressure data analysis on the fingerprint pressing data to obtain pressure quantized data.
In an embodiment of the present invention, the pressure analysis module 103 may acquire fingerprint pressing data of a user by using any pressure sensor function device, where the fingerprint pressing data refers to pressure data generated when the user presses the pressure sensor function device, and the fingerprint pressing data includes, but is not limited to, a pressure magnitude, a pressure change trend, and/or a pressure application direction.
In detail, the pressure analysis module 103 performs pressure data analysis on the fingerprint pressing data by the following operations to obtain pressure quantized data:
the instantaneous pressure value Z and the pressure gradient field T (x, y) of the fingerprint compression data are calculated using the following calculation formula:
Z＝|T(x,y)|
wherein Z is the instantaneous pressure value of the pixel point (x, y); a pressure gradient field for the T (x, y) pixel points (x, y); g x (x, y) is the partial derivative of the instantaneous pressure value Z at point (x, y) with respect to x G y (x, y) is the partial derivative of the instantaneous pressure value Z at point (x, y) with respect to yΘ (x, y) is the direction of application of the instantaneous pressure value Z;
The instantaneous pressure value Z and the pressure gradient field |t (x, y) | are determined as pressure quantized data.
In detail, the pressure analysis module 103 calculates the application direction θ (x, y) of the pressure using the following calculation direction formula:
Wherein V x (x, y) is the pressure applied in the transverse axis direction; v y (x, y) is the pressure applied in the direction of the longitudinal axis; θ (x, y) is the pressure direction field function; w is an initial direction parameter; g x (x, y) is the partial derivative of the instantaneous pressure function Z at point (x, y) with respect to x G y (x, y) is the partial derivative of the instantaneous pressure function Z at point (x, y) with respect to y/>I is a horizontal axis error preset for the point (x, y), and j is a vertical axis error preset for the point (x, y).
And step four, the signature recognition module 104 acquires an electronic signature of the user, and performs signature recognition on the electronic signature to obtain signature data.
In the embodiment of the present invention, the signature recognition module 104 may use the python statement with the data grabbing function to obtain the electronic signature of the user from the blockchain used for storing the electronic signature of the user. The security of the electronic signature can be improved by utilizing the confidentiality of the blockchain to the data, and meanwhile, the efficiency of acquiring the electronic signature of the user can be improved by utilizing the high throughput of the blockchain to the data.
In the embodiment of the present invention, the signature recognition module 104 recognizes the electronic signature by using an OCR (Optical Character Recognition ) model to obtain signature data, where the electronic signature may be any electronic form of user signature, for example, an electronic seal, an electronic image with the user signature, and the like.
In detail, the OCR model adopts a Bi-LSTM-CRF structure, and comprises the following steps:
a word/word vector layer, configured to convert words and characters in a text contained in the electronic signature into a word/word vector;
The Bi-LSTM layer is used for dividing the character/word vector, coding the divided character/word vector to obtain a coding representation of the character/word vector, and marking the divided character/word vector by using the coding representation to obtain a key value and a result value;
and the CRF layer is used for splicing the key values and the result values of the same type into signature data.
The word/word vector layer converts words and characters in texts contained in the electronic signature into word/word vectors by using trained word vectors as initialization parameters, wherein the trained word vectors are a set of preset standard conversion rules.
Since more texts are possibly contained in the electronic signature, sentences in the texts are possibly longer, if only character conversion is performed, text stiction can possibly occur, and subsequent text error correction is not facilitated.
Preferably, the Bi-LSTM layer can divide the word/word vector by adopting java language and encode the divided word/word vector, wherein the encoded representation comprises six label types of Key-B, value-B, key-I, value-I, other-B and Other-I. Wherein Key is a Key Value, value is a result Value, and Other is Other values.
In the embodiment of the invention, the CRF layer is utilized to splice Key values and result values of the same type, such as Key-B, key-I or Value-B, value-I. And after all the key values and the result values are spliced, the signature data can be obtained, wherein the signature data is computer data in the form of IO data stream.
And fifthly, the data fusion module 105 performs equidistant data fusion on the texture track data, the pressure quantized data and the signature data to obtain a fingerprint signature of the user.
In the embodiment of the present invention, the data fusion module 105 performs equidistant data fusion on the texture track data, the pressure quantization data and the signature data to obtain a fingerprint signature of the user by:
equidistant splitting is carried out on the texture track data according to a first preset length, so that first split data are obtained;
equidistant splitting is carried out on the pressure quantized data according to a second preset length, so that second split data are obtained;
equidistant splitting is carried out on the signature data according to a third preset length, and third split data are obtained;
and performing interpenetration combination on the first split data, the second split data and the third split data to obtain the fingerprint signature of the user.
In detail, equidistant splitting refers to splitting a piece of data into multiple pieces of data according to a preset length, for example, data & including data: 123456, when the preset length is "2", the data & is split into 12, 34 and 56.
In the embodiment of the present invention, the first preset length, the second preset length and the third preset length may be the same or different.
Specifically, the data fusion module 105 performs interleaving and combining on the first split data, the second split data and the third split data according to a preset sequence, for example, the first split data is ab, cd and ef, the second split data is gh, ij and kl, and the third split data is mn, op and qr; when the preset sequence is 'first split data-second split data-third split data', the first split data, the second split data and the third split data are alternately combined according to the preset sequence to be: ab+gh+mn+cd+ij+op+ef+kl+qr.
In the embodiment of the present invention, the data fusion module 105 performs equidistant data fusion on the texture track data, the pressure quantization data and the signature data to generate the fingerprint signature of the user, so that the user identity can be authenticated by the texture track data, the pressure quantization data and the signature data of the fingerprint of the user together, and the security of user identity authentication by using the user signature is improved.
According to the embodiment of the invention, the fingerprint region is extracted from the fingerprint image, so that the analysis of the region without fingerprint information in the fingerprint image can be avoided, and the efficiency of extracting the texture track data from the fingerprint image is improved; the fingerprint pressing data is subjected to pressure data analysis to obtain pressure quantized data, signature recognition is carried out on the user electronic signature to obtain signature data, texture track data, the pressure quantized data and the signature data are fused into fingerprint signatures at equal intervals, the fused fingerprint signatures comprise more accurate pen transporting force and fine strokes, the matching rate of the fingerprint signatures and templates prestored in a database is higher, and therefore the accuracy and safety of the fingerprint signatures are improved. Therefore, the fingerprint signature generating device provided by the invention can improve the security of the electronic signature.
Fig. 3 is a schematic structural diagram of an electronic device for implementing the fingerprint signature generation method according to the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program, such as a fingerprint signature generation program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of the fingerprint signature generation program 12, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects respective parts of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device 1 and processes data by running or executing programs or modules (for example, executing a fingerprint signature generation program or the like) stored in the memory 11, and calling data stored in the memory 11.
The bus may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 3 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The fingerprint signature generation program 12 stored in the memory 11 in the electronic device 1 is a combination of a plurality of computer programs, which when run in the processor 10, can implement:
Acquiring a fingerprint image of a user, and extracting a fingerprint area from the fingerprint image to obtain the fingerprint area;
performing texture track analysis on the fingerprint region to obtain texture track data;
Acquiring fingerprint pressing data of a user, and performing pressure data analysis on the fingerprint pressing data to obtain pressure quantized data;
Acquiring an electronic signature of a user, and performing signature recognition on the electronic signature to obtain signature data;
and carrying out equidistant data fusion on the texture track data, the pressure quantized data and the signature data to obtain the fingerprint signature of the user.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
Further, the computer-usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any accompanying diagram representation in the claims should not be considered as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (7)
1. A method of fingerprint signature generation, the method comprising:
Acquiring a fingerprint image of a user, downsampling the fingerprint image to obtain a global feature image, upsampling the global feature image by a first threshold multiple to obtain an intermediate feature image, upsampling the intermediate feature image by a second threshold multiple to obtain a fingerprint restoration image, calculating target class probability that each pixel point in the fingerprint restoration image belongs to a preset target class by using a first activation function, and dividing the fingerprint restoration image according to the target class probability to obtain a fingerprint region;
Inputting all pixel points in the fingerprint area into a preset gray value conversion formula to perform gray pixel conversion to generate a gray image, traversing and counting gray probability density of each pixel point in the fingerprint area after gray pixel conversion, performing stretching conversion treatment on the gray probability density of each pixel point by using a preset gray conversion function to obtain a fingerprint area after contrast stretching treatment, and performing texture track analysis on the fingerprint area after contrast stretching treatment to obtain texture track data;
Acquiring fingerprint pressing data of a user, and performing pressure data analysis on the fingerprint pressing data to obtain pressure quantized data;
Acquiring an electronic signature of a user, converting characters and words in a text contained in the electronic signature into word/word vectors, dividing the word/word vectors, encoding the divided word/word vectors to obtain encoding characterization of the word/word vectors, marking the divided word/word vectors by using the encoding characterization to obtain key values and result values, and splicing the key values and the result values of the same type into signature data;
Equidistant splitting is carried out on the texture track data according to a first preset length to obtain first split data, equidistant splitting is carried out on the pressure quantized data according to a second preset length to obtain second split data, equidistant splitting is carried out on the signature data according to a third preset length to obtain third split data, and the first split data, the second split data and the third split data are combined in a penetrating mode to obtain the fingerprint signature of the user.
2. The fingerprint signature generation method as recited in claim 1, wherein the performing texture trajectory analysis on the fingerprint region to obtain texture trajectory data includes:
Calculating a gray frequency field of the fingerprint region;
calculating the sum of tangential pixels and the sum of normal pixels of each pixel point in the fingerprint area;
and integrating the gray frequency field, the sum of the tangential pixels and the sum of the normal pixels to obtain texture track data.
3. The fingerprint signature generation method of claim 2, wherein said calculating a gray scale frequency field of said fingerprint region comprises:
Calculating the gray frequency field of the fingerprint region by using the following calculation formula ：
Wherein,The total vertical gray level change amount of any two pixel points on the fingerprint area is calculated; /(I)And/>Respectively the transverse coordinate values of any two different pixel points on the fingerprint area; /(I)A gray function representing a vertical direction of the fingerprint region; /(I)Is the average amplitude of the fingerprint waveform between any two different pixel points on the fingerprint area.
4. A fingerprint signature generation method according to any one of claims 1 to 3 wherein said performing pressure data analysis on said fingerprint press data to obtain pressure quantified data comprises:
calculating the instantaneous pressure value of the fingerprint pressing data by using the following calculation formula And pressure gradient field/>：
Wherein,Is pixel/>Is determined by the instantaneous pressure value of (a); /(I)Pixel dot/>Is a pressure gradient field of (2); /(I)For instantaneous pressure value/>At the point/>Pair/>Partial derivative/>；/>For instantaneous pressure value/>At the point/>Pair/>Partial derivative/>，For instantaneous pressure value/>Is applied in the direction of application;
Determining the instantaneous pressure value And pressure gradient field/>Data were quantified for pressure.
5. A fingerprint signature generation device, the device comprising:
The device comprises a region extraction module, a first activation function and a second activation function, wherein the region extraction module is used for acquiring a fingerprint image of a user, downsampling the fingerprint image to obtain a global feature image, upsampling the global feature image by a first threshold multiple to obtain an intermediate feature image, upsampling the intermediate feature image by a second threshold multiple to obtain a fingerprint restoration image, calculating target class probability that each pixel point in the fingerprint restoration image belongs to a preset target class by using the first activation function, and dividing the fingerprint restoration image according to the target class probability to obtain a fingerprint region;
The track analysis module is used for inputting all pixel points in the fingerprint area into a preset gray value conversion formula to perform gray pixel conversion, generating a gray image, traversing and counting gray probability density of each pixel point in the fingerprint area after gray pixel conversion, performing stretching conversion on the gray probability density of each pixel point by using a preset gray conversion function to obtain a fingerprint area after contrast stretching, and performing texture track analysis on the fingerprint area after contrast stretching to obtain texture track data;
The pressure analysis module is used for acquiring fingerprint pressing data of a user, and performing pressure data analysis on the fingerprint pressing data to obtain pressure quantized data;
The signature recognition module is used for acquiring an electronic signature of a user, converting characters and words in a text contained in the electronic signature into word/word vectors, dividing the word/word vectors, encoding the divided word/word vectors to obtain encoding characterization of the word/word vectors, marking the divided word/word vectors by using the encoding characterization to obtain key values and result values, and splicing the key values and the result values of the same type into signature data;
The data fusion module is used for carrying out equidistant splitting on the texture track data according to a first preset length to obtain first split data, carrying out equidistant splitting on the pressure quantized data according to a second preset length to obtain second split data, carrying out equidistant splitting on the signature data according to a third preset length to obtain third split data, and carrying out interpenetration combination on the first split data, the second split data and the third split data to obtain the fingerprint signature of the user.
6. An electronic device, the electronic device comprising:
at least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the fingerprint signature generation method of any one of claims 1 to 4.
7. A computer-readable storage medium comprising a storage data area storing created data and a storage program area storing a computer program; a fingerprint signature generation method according to any one of claims 1 to 4, wherein the computer program when executed by a processor.
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