CN110942536A - Fingerprint identification unlocking system - Google Patents
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- CN110942536A CN110942536A CN201911122268.1A CN201911122268A CN110942536A CN 110942536 A CN110942536 A CN 110942536A CN 201911122268 A CN201911122268 A CN 201911122268A CN 110942536 A CN110942536 A CN 110942536A
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C9/00—Individual registration on entry or exit
- G07C9/00174—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys
- G07C9/00563—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys using personal physical data of the operator, e.g. finger prints, retinal images, voicepatterns
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/253—Fusion techniques of extracted features
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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
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
- G06V10/751—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
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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
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/1347—Preprocessing; Feature extraction
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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
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/1365—Matching; Classification
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C9/00—Individual registration on entry or exit
- G07C9/00174—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys
- G07C9/00571—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys operated by interacting with a central unit
Abstract
The invention discloses a fingerprint identification unlocking system, which comprises: lock body and install lock box and the alarm on the lock body, lock box internally mounted has eigenvector treater, first memory, registration fingerprint treater, second memory, discernment fingerprint treater, fingerprint matcher, controller and wireless communication module. The first Hash template and the second Hash template obtained by the invention have better withdrawability and no relevance, so that the safety is better, and the matching operation is carried out under the condition of an encryption domain, so that the original template information cannot be leaked even if the templates are lost, and the safety of unlocking by using fingerprints is improved.
Description
Technical Field
The invention belongs to the technical field of fingerprint identification, and particularly relates to a fingerprint identification unlocking system.
Background
With the rapid development of social urbanization, large and medium-sized cities in China are more and more, and therefore, a safety management system of a building becomes more and more important. One of the most important devices today for building security is the lockset. The traditional mechanical lock has simple structure and low safety performance, and the key is easy to copy, so that the stolen events are frequent, and the traditional mechanical lock is difficult to effectively protect the daily life of people. On the other hand, with the continuous development of science and technology, the electronic coded lock with the anti-theft alarm function gradually replaces the traditional mechanical lock to enter people's life, and can overcome the defects of small code amount, poor safety performance and the like of the mechanical lock. However, most of the existing electronic coded locks adopt a keyboard input mode, and the peep-proof performance and the confidentiality are poor; many other electronic smart locks, such as IC radio frequency card smart locks and fingerprint identification smart locks, have been continuously available at home and abroad and come into the field of vision of people.
The fingerprint identification technology is one of identification technologies which are most widely applied and have the lowest price in the biological identification technology, and when the fingerprint identification technology is applied to an electronic intelligent door lock, the fingerprint identification technology has the advantages of low cost, high safety and convenience in use.
However, the wide application of unlocking by using the fingerprint features also brings personal privacy disclosure and other security concerns, so how to improve the security of the fingerprint features becomes a problem to be solved urgently.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a fingerprint identification unlocking system. The technical problem to be solved by the invention is realized by the following technical scheme:
a fingerprint identification unlocking system, comprising: the lock comprises a lock body, a lock box and an alarm, wherein the lock box and the alarm are arranged on the lock body, a characteristic vector processor, a first memory, a registered fingerprint processor, a second memory, an identified fingerprint processor, a fingerprint matcher, a controller and a wireless communication module are arranged in the lock box,
the feature vector processor is used for obtaining a clustering center set according to first fusion feature vectors of the minutiae to be trained, wherein the clustering center set comprises a plurality of first fusion feature vectors;
a first memory coupled to the feature vector memory for storing the cluster center set;
the registered fingerprint processor is coupled with the first memory and used for acquiring a plurality of minutiae to be registered of the fingerprint to be registered and obtaining a first hash template according to the clustering center set and the second fusion characteristic vector of the minutiae to be registered;
a second memory coupled to the enrolled fingerprint processor for storing the first hash template;
the fingerprint identification processor is coupled with the first memory and used for acquiring a plurality of minutiae to be identified of the fingerprint to be identified and obtaining a second hash template according to the clustering center set and the third fusion feature vector of the minutiae to be identified;
the fingerprint matcher is respectively connected with the second memory and the fingerprint identification processor and is used for obtaining a matching result by using an encryption domain matching formula based on the first hash template and the second hash template;
and the controller is coupled with the fingerprint matcher and the lock body and is used for receiving the matching result, judging whether the matching result is successfully identified or failed to identify, and controlling the lock body to be opened if the matching result is successfully identified.
In one embodiment of the invention, the first memory comprises a ROM memory or a SDRAM memory.
In one embodiment of the invention, the second memory comprises a ROM memory or a SDRAM memory.
In one embodiment of the invention, the lock further comprises an alarm, the alarm is mounted on the lock body and coupled with the controller, and the controller is used for controlling the alarm to give an alarm according to the matching result when the identification fails.
In one embodiment of the invention, the alarm comprises a buzzer or an LED light.
In an embodiment of the present invention, the mobile phone further includes a wireless communication module, which is installed inside the lock box and is wirelessly connected to the controller, and is configured to transmit the matching result to a mobile phone terminal.
In one embodiment of the invention, the wireless communication module comprises bluetooth, wife or GPRS.
In one embodiment of the invention, the feature vector processor comprises:
the acquisition module is used for acquiring a plurality of minutiae points to be trained;
the first processing module is connected with the acquisition module and used for processing the minutiae to be trained and pixel points in a first region corresponding to the minutiae to be trained according to a Gaussian function to obtain a first constant-length real number vector of the minutiae to be trained;
the second processing module is connected with the acquisition module and used for obtaining a second fixed-length real number vector of the minutiae to be trained according to the minutiae to be trained and the gray levels of pixel points in a second region corresponding to the minutiae to be trained;
the first fusion characteristic processing module is connected with the first processing module and the second processing module and is used for performing dimensionality reduction processing on the first fixed-length real number vector and the second fixed-length real number vector respectively by using PCA (principal component analysis), and then cascading the first fixed-length real number vector and the second fixed-length real number vector into a first fusion characteristic vector;
and the clustering module is connected with the first fusion characteristic processing module and used for clustering the first fusion characteristic vector by using a k-means algorithm to obtain a clustering center set.
In one embodiment of the invention, the enrolled fingerprint processor comprises:
the second fusion feature processing module is used for acquiring a second fusion feature vector of the detail node to be registered;
the first bit vector processing module is connected with the second fusion feature processing module and used for obtaining a first bit vector according to the Euclidean distance between the second fusion feature vector and the first fusion feature vector in the cluster center set;
and the first hash template processing module is connected with the first bit vector processing module and used for randomly generating m groups of first permutation seeds according to a locality sensitive hash algorithm, randomly permutating the first bit vectors by using the m groups of first permutation seeds to obtain m first permutation bit vectors, and then obtaining the first hash template according to the first permutation bit vectors.
In one embodiment of the invention, an identifying fingerprint processor comprises:
the third fusion feature processing module is used for acquiring a third fusion feature vector of the minutiae to be identified;
the second bit vector processing module is connected with the third fusion characteristic processing module and used for obtaining a second bit vector according to the Euclidean distance between the third fusion characteristic vector and the first fusion characteristic vector in the cluster center set;
and the second hash template processing module is connected with the second bit vector processing module and used for randomly generating m groups of second permutation seeds according to a locality sensitive hash algorithm, randomly permutating the second bit vectors by using the m groups of second permutation seeds to obtain m second permutation bit vectors, and then obtaining the second hash template according to the second permutation bit vectors.
The invention has the beneficial effects that:
according to the invention, the clustering center set comprising the fusion characteristic vector is obtained through the minutiae to be trained, then the first Hash template is obtained through the minutiae to be registered and the clustering center set, the second Hash template is obtained according to the minutiae to be identified and the clustering center set, and finally the first Hash template and the second Hash template are matched according to the encryption domain matching formula.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Drawings
Fig. 1 is a schematic view of a fingerprint identification unlocking system provided by an embodiment of the invention;
fig. 2 is a schematic diagram of a fingerprint template protection method based on locality sensitive hashing according to an embodiment of the present invention;
FIG. 3 is a diagram of a feature vector processor according to an embodiment of the present invention;
FIG. 4 is a diagram of an enrolled fingerprint processor provided in an embodiment of the invention;
fig. 5 is a schematic diagram of a fingerprint identification processor according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.
Example one
Referring to fig. 1, fig. 1 is a schematic diagram of a fingerprint identification unlocking system according to an embodiment of the present invention, and fig. 2 is a schematic diagram of a fingerprint template protection method based on locality sensitive hashing according to an embodiment of the present invention. The embodiment provides a fingerprint identification unlocking system, which comprises a lock body, a lock box and an alarm, wherein the lock box and the alarm are installed on the lock body;
the feature vector processor is used for obtaining a clustering center set according to the first fusion feature vectors of the minutiae to be trained, wherein the clustering center set comprises a plurality of first fusion feature vectors;
the first memory is coupled with the feature vector memory and used for storing the clustering center set;
the registered fingerprint processor is coupled with the first memory and used for acquiring a plurality of minutiae to be registered of the fingerprint to be registered and obtaining a first hash template according to the clustering center set and the second fusion characteristic vector of the minutiae to be registered;
the second memory is coupled with the registered fingerprint processor and used for storing the first hash template;
the fingerprint identification processor is coupled with the first memory and used for acquiring a plurality of minutiae to be identified of the fingerprint to be identified and obtaining a second hash template according to the clustering center set and the third fusion feature vector of the minutiae to be identified;
the fingerprint matcher is respectively connected with the second memory and the fingerprint identification processor and is used for obtaining a matching result by using an encryption domain matching formula based on the first hash template and the second hash template;
and the controller is coupled with the fingerprint matcher and the lock body and is used for receiving the matching result, judging whether the matching result is successfully identified or unsuccessfully identified, and controlling the lock body to be opened if the matching result is successfully identified.
That is to say, the lock body of this embodiment can be unlocked by fingerprint identification, and is used for locking devices requiring locking of lock bodies, such as doors, windows, and the like, and a lock box is installed on the lock body, the lock box has an inner cavity, and a feature vector processor, a first memory, a registered fingerprint processor, a second memory, an identified fingerprint processor, a fingerprint matcher, and a controller are all arranged in the inner cavity of the lock box, in this embodiment, a cluster center set is obtained by the feature vector processor according to a first fusion feature vector of a minutia to be trained for training, and the obtained cluster center set is stored in the first memory, so that a subsequent registered fingerprint processor and an identified fingerprint processor can obtain relevant data of the cluster center set in time, and the registered minutia to be registered is collected by the registered fingerprint processor, and a first hash template is obtained according to the second fusion feature vector of the cluster center set and the minutia to be registered, then the obtained first hash template is stored in a second memory so as to be used when being matched with a to-be-identified template, when unlocking is needed through fingerprint identification, the fingerprint identification processor acquires a fingerprint to be identified, and a second hash template is obtained according to a third fusion feature vector of a to-be-identified minutia of the fingerprint to be identified, then a fingerprint matcher can be used for matching the first hash template and the second hash template and transmitting a matching result to a controller, the controller can control whether a lock body is opened or not according to the identification result, if the matching is successful, the lock body is opened, if the matching is failed, the lock body is not opened, therefore, the first hash template and the second hash template obtained in the embodiment have better withdrawability and no relevance, and meanwhile, because the matching operation is carried out under the condition of an encryption domain, even if the templates are lost, the original template information cannot be leaked, thereby improving the safety when unlocking by using the fingerprint.
Preferably, the first memory comprises a ROM memory or a SDRAM memory.
Preferably, the second memory comprises a ROM memory or a SDRAM memory.
In addition, in order to further improve the security, the fingerprint identification unlocking system of the embodiment may further include an alarm, the alarm is installed on the lock body and coupled with the controller, and when the identification result obtained by the controller is an identification failure, the controller may control the alarm to give an alarm to remind an owner of the fingerprint identification unlocking system that a stranger may be in a situation of unlocking the lock body, so that the security of the fingerprint identification unlocking system of the embodiment is further improved.
Preferably, the alarm comprises a buzzer or an LED light.
In addition, in order that the owner of fingerprint identification system of unblanking in time obtains the state that fingerprint identification system of unblanking, the fingerprint identification system of unblanking of this embodiment can also include wireless communication module, this wireless communication module install in the inside of locking the box and with controller wireless connection, the controller will match the result and transmit to wireless communication module, wireless communication module alright with in time providing to cell-phone terminal with the matching result, so that the owner of fingerprint identification system of unblanking in time obtains the state that fingerprint identification system of unblanking.
Preferably, the wireless communication module comprises bluetooth, wife or GPRS.
In one embodiment, referring to fig. 3, the feature vector processor includes an acquisition module, a first processing module, a second processing module, a first fused feature processing module and a clustering module, the acquisition module is respectively connected to the first processing module and the second processing module, the first processing module and the second processing module are both connected to the first fused feature processing module, and the first fused feature processing module is connected to the clustering module.
In one embodiment, the acquisition module is used for acquiring a plurality of minutiae points to be trained.
The minutiae to be trained in this embodiment may be combined by collecting a plurality of fingerprint images and acquiring a plurality of minutiae from each fingerprint image, and the minutiae to be trained may include end points and branch points of fingerprint lines.
Further, firstly, a plurality of first fingerprint images to be trained are obtained, then fingerprint enhancement and thinning processing can be carried out on the first fingerprint images to be trained to obtain second fingerprint images to be trained, and then a plurality of minutiae points to be trained on the second fingerprint images to be trained are extracted.
In this embodiment, the first fingerprint image to be trained is used to extract minutiae points to be trained, and in order to improve the quality of the fingerprint image and extract minutiae points more accurately, the embodiment performs preprocessing on the first fingerprint image to be trained so as to perform preprocessing on the second fingerprint image to be trained, where the preprocessing may include enhancement processing and refinement processing, and then extracts minutiae points to be trained for training through the second fingerprint image to be trained.
In one embodiment, the first processing module is configured to process, according to a gaussian function, the minutiae to be trained and pixel points in a first region corresponding to the minutiae to be trained to obtain a first constant-length real number vector of the minutiae to be trained.
The first processing module of this embodiment processes the minutiae to be trained and the pixel points in the first region obtained based on the minutiae to be trained through the gaussian function, thereby obtaining a first fixed-length real number vector of the minutiae to be trained, where the first fixed-length real number vector reflects the position characteristics of the minutiae to be trained, and therefore the fusion feature vector obtained through the first fixed-length real number vector can embody the position characteristics of the minutiae to be trained.
Further, the first processing module may include a first region establishing module, a first gaussian function value calculating module, and a first constant real number vector generating module, which are connected in sequence.
Specifically, the first area establishing module is used for establishing the first area by taking the minutiae points to be trained as base points.
That is to say, in order to better reflect the characteristics of each minutia point to be trained, when each minutia point to be trained is processed, a first region is selected in a certain shape with the minutia point to be trained as a base point, so that the first region can include the minutia point to be trained and pixels around the minutia point. The first area is not limited in this embodiment, and the first area may be, for example, a circle, a square, or the like. To better illustrate the first region, the embodiment illustrates the first region as a circle, for example, a minutiae point { x ] to be trainedr,yr,θrUsing radius r as circle centermMaking a circle, the number of the pixel points in the circle is
The first Gaussian function value calculation module is used for obtaining the distance between the remaining minutiae to be trained except the base point in the first region and each pixel point in the first region according to the polar coordinates of the minutiae to be trained and the polar coordinates of each pixel point in the first region, and obtaining a first Gaussian function value by using a Gaussian function based on the distance between the minutiae to be trained and each pixel point in the first region.
That is to say, in this embodiment, firstly, a to-be-trained minutiae is subjected to polar coordinate conversion to obtain a polar coordinate of the to-be-trained minutiae, and a pixel point in a first region is subjected to polar coordinate conversion to obtain a polar coordinate of each pixel point, and then, a distance between the to-be-trained minutiae and each pixel point in the first region is calculated by using the polar coordinates of the to-be-trained minutiae except for the base point in the first region and each pixel point in the first region, and the obtained distance is substituted into a gaussian function to obtain a first gaussian function value, where an expression of the gaussian function is:
ξ is the distance between the detail point to be trained and the pixel point in the first region, σSIs the standard deviation.
Specifically, the first definite-length real number vector generation module is configured to obtain a first contribution value of each pixel point in the first region according to the first gaussian function value, and obtain a first definite-length real number vector according to the first contribution value.
That is, first, the first gaussian function value obtained by each pixel point in the first region is recorded as the first contribution value of the pixel point, that is, the first contribution value is obtained
Wherein G (d (m)t,px,y) Is a gaussian function, ξ ═ d (m)t,px,y)，
And combining all the first contribution values into a first constant-length real number vector of the minutiae to be trained according to a set sequence after traversing all the pixel points in the first region according to the set sequence, and correspondingly obtaining the first constant-length real number vector of each minutia to be trained in the first region by the above method, wherein the set sequence can be set according to actual requirements, for example, the set sequence can be from left to right and from top to bottom.
In one embodiment, the second processing module is configured to obtain a second fixed-length real number vector of the minutiae to be trained according to the minutiae to be trained and the gray levels of the pixels in the second region corresponding to the minutiae to be trained.
In the second processing of this embodiment, a second fixed-length real number vector of the minutiae to be trained is obtained by a difference between the gray level of the minutiae to be trained and the gray level of the pixel point in the second region obtained based on the minutiae to be trained, and the second fixed-length real number vector reflects the gray level characteristics of the minutiae to be trained, so that the fusion feature vector obtained by the second fixed-length real number vector can embody the gray level characteristics of the minutiae to be trained.
Further, the second processing module may include a second region establishing module, a first texture feature value calculating module, and a first constant-length real number vector generating module, which are connected in sequence.
Specifically, the second area establishing module is used for establishing the second area by taking the minutiae points to be trained as base points.
That is to say, in order to better reflect the characteristics of each minutia point to be trained, when each minutia point to be trained is processed, a second region is selected in a certain shape with the minutia point to be trained as a base point, so that the second region can include the minutia point to be trained and the pixel points around the minutia point. The second area is not limited in this embodiment, and the second area may be, for example, a circle, a square, or the like. To better illustrate the second region, the embodiment illustrates the second region as a circle, for example, a minutiae point { x ] to be trainedr,yr,θrUsing radius r as circle centertMaking a circle, the number of the pixel points in the circle is
Specifically, the first texture feature value calculation module is configured to obtain a first texture feature value according to a difference between a gray value of a minutia point to be trained and a gray value of a pixel point in the second region;
that is to say, the difference between the gray value of the minutiae to be trained and the gray value of the pixel points in the second region is calculated, and the difference is recorded as a first texture feature value.
Specifically, the first fixed-length real number vector generation module is configured to obtain a second fixed-length real number vector according to the first texture feature value.
That is to say, after traversing all the pixel points in the second region according to the set order, assembling all the first texture feature values into a second fixed-length real number vector of the minutiae to be trained according to the set order, and correspondingly obtaining the second fixed-length real number vector of each minutia to be trained in the second region by the above method, where the set order may be set according to actual requirements, for example, the set order may be from left to right, or from top to bottom.
In one embodiment, the first fused feature processing module is configured to perform dimensionality reduction on the first fixed-length real number vector and the second fixed-length real number vector respectively by using PCA, and then concatenate the vectors into the first fused feature vector.
That is to say, a PCA (principal component analysis) method is used to perform dimensionality reduction on a first fixed-length real number vector and a second fixed-length real number vector of a minutia point to be trained respectively, and the first fixed-length real number vector and the second fixed-length real number vector after dimensionality reduction are cascaded, wherein a vector obtained after the cascading is a first fusion feature vector of the minutia point to be trained.
In one embodiment, the clustering module is configured to perform clustering processing on the first fused feature vectors by using a k-means algorithm to obtain a cluster center set, where the cluster center set includes a plurality of first fused feature vectors.
That is, in this embodiment, all the first fused feature vectors used for training are clustered, for example, a certain number is set, all the first fused feature vectors meeting the number after the clustering is completed are collected as a cluster center set, for example, the cluster number is set to 4000, and then the first fused feature vectors meeting the clustering condition are clustered.
In one embodiment, referring to fig. 4, the enrolled fingerprint processor may include a second fused feature vector processing module, a first bit vector processing module, and a first hash template processing module, which are connected in sequence.
In one embodiment, the second fused feature processing module is configured to obtain a second fused feature vector of the minutiae to be registered.
That is to say, the registered fingerprint is a fingerprint that needs to be registered in actual use, the minutiae points to be registered are minutiae points contained in the registered fingerprint, each minutia point to be registered may include an end point and a branch point of a fingerprint line, and the second fused feature vector reflects the position and the grayscale features of the minutiae points to be registered.
Further, the second fused feature vector generation module may include a to-be-registered minutia acquisition module, a third fixed-length real number vector generation module, a fourth fixed-length real number vector generation module, and a first fusion module, where the to-be-registered minutia acquisition module is connected to the third fixed-length real number vector generation module and the fourth fixed-length real number vector generation module, respectively, and the third fixed-length real number vector generation module and the fourth fixed-length real number vector generation module are both connected to the first fusion module.
Specifically, the minutiae to be registered acquisition module is configured to acquire a plurality of minutiae to be registered of the fingerprint to be registered.
Specifically, the third fixed-length real number vector generation module is configured to process, according to the gaussian function, the minutiae to be registered and the pixels in the third area corresponding to the minutiae to be registered to obtain a third fixed-length real number vector of the minutiae to be registered.
Further, the third fixed-length real number vector generation module is specifically configured to construct a third region with the minutiae point to be registered as a base point; obtaining a second Gaussian function value according to the polar coordinates of the detail node to be registered and the polar coordinates of each pixel point in the third area, and obtaining a second contribution value of each pixel point in the third area according to the second Gaussian function value; and obtaining a third fixed-length real number vector of the detail node to be registered according to the second contribution value of each pixel point in the third area.
In order to better reflect the characteristics of each minutia point to be registered, when each minutia point to be registered is processed, a third region is selected in a certain shape with the minutia point to be registered as a base point, so that the third region can include the minutia point to be registered and pixels around the minutia point to be registered. The third area is not limited in this embodiment, and the third area may be, for example, a circle, a square, or the like.
Then, according to the polar coordinates of the detail node to be registered and the polar coordinates of each pixel point in the third area, the distance between the remaining detail nodes to be registered except the base point in the third area and each pixel point in the third area is obtained; and then, based on the distance between the detail point to be registered and each pixel point in the third area, obtaining a second Gaussian function value by using the Gaussian function, and recording the second Gaussian function value obtained by each pixel point in the third area as a second contribution value of the pixel point.
That is to say, firstly, the polar coordinate of the minutiae to be registered is obtained through polar coordinate conversion, the polar coordinate of each pixel point is obtained through polar coordinate conversion of the pixel points in the third region, then the distance between the minutiae to be registered and each pixel point in the third region is calculated through the polar coordinates of the remaining minutiae to be registered except the base point in the third region and the polar coordinates of each pixel point in the third region, the obtained distance is substituted into the gaussian function, so that a second gaussian function value is obtained, and the second gaussian function value obtained by each pixel point in the third region is recorded as a second contribution value of the pixel point.
And finally, after traversing all the pixel points in the third region according to the set sequence, combining the second contribution values of all the pixel points in the third region into a third fixed-length real number vector of the minutiae to be registered according to the set sequence, and correspondingly obtaining the third fixed-length real number vector of each minutia to be registered in the third region by the above method, wherein the set sequence can be set according to actual requirements, for example, the set sequence can be from left to right, and from top to bottom.
Specifically, the fourth fixed-length real number vector generation module is configured to obtain a fourth fixed-length real number vector of the minutiae to be registered according to the minutiae to be registered and the gray levels of the pixel points in the fourth region corresponding to the minutiae to be registered.
Further, the fourth fixed-length real number vector generation module is specifically configured to construct a fourth region with the minutiae point to be registered as a base point; obtaining a second texture characteristic value according to the difference value between the gray value of the detail point to be registered and the gray value of the pixel point in the fourth area; and obtaining a fourth fixed-length real number vector according to the second texture characteristic value of the pixel point in the fourth region.
In order to better reflect the characteristics of each minutia point to be registered, when each minutia point to be registered is processed, a fourth region is selected in a certain shape by taking the minutia point to be registered as a base point, so that the fourth region can include the minutia point to be registered and pixels around the minutia point to be registered. The fourth area is not limited in this embodiment, and the fourth area may be, for example, a circle, a square, or the like.
And then, calculating the difference value between the gray value of the minutiae to be registered and the gray value of the pixel points in the fourth region, and recording the difference value as a second texture characteristic value of the pixel points in the fourth region.
And finally, after traversing all the pixel points in the fourth region according to the set sequence, combining all the second texture characteristic values according to the set sequence to form a fourth fixed-length real number vector of the minutiae to be registered, and correspondingly obtaining the fourth fixed-length real number vector of each minutia to be registered in the fourth region by the above method, wherein the set sequence can be set according to actual requirements, for example, the set sequence can be from left to right and from top to bottom.
Specifically, the first fusion module is configured to perform dimensionality reduction on the third fixed-length real number vector and the fourth fixed-length real number vector respectively by using PCA, and then cascade the third fixed-length real number vector and the fourth fixed-length real number vector into a second fusion feature vector.
Namely, the third fixed-length real number vector and the fourth fixed-length real number vector of the minutiae to be registered are subjected to dimensionality reduction by using a PCA method, the third fixed-length real number vector and the fourth fixed-length real number vector after dimensionality reduction are cascaded, and a vector obtained after the cascade is a second fusion feature vector of the minutiae to be registered.
In one embodiment, the first bit vector processing module is configured to obtain the first bit vector according to the euclidean distance between the second fused feature vector and the first fused feature vector in the cluster center set.
Firstly initializing a vector, wherein the length of the vector is equal to the number of first fusion feature vectors contained in a cluster center set, then calculating the Euclidean distance between the obtained second fusion feature vectors of the minutiae to be registered and each first fusion feature vector in the cluster center set, correspondingly obtaining the first fusion feature vector with the minimum Euclidean distance in each second fusion feature vector, allocating the corresponding position in the initialization vector as 1, allocating the rest positions as 0, and after traversing all the minutiae to be registered, obtaining the first bit vector of the fingerprint to be registered.
In an embodiment, the first hash template processing module is configured to randomly generate m sets of first permutation seeds according to a locality sensitive hash algorithm, perform random permutation on the first bit vector by using the m sets of first permutation seeds to obtain m first permutation bit vectors, and then obtain the first hash template according to the first permutation bit vector.
That is, the hash code value is initialized first, each element is initialized to 0, and then m sets of first permutation seeds for performing position permutation on the obtained first bit vector are randomly generated.
Then, the first bit vector is subjected to position permutation on the first bit vector according to a randomly generated first permutation seed, and a first permutation bit vector is obtained correspondingly, then m groups of first permutation seeds are subjected to random permutation on the first bit vector, so that m first permutation bit vectors can be obtained correspondingly, for example, the first bit vector is [00110], the first permutation seeds are [13245] and [43215], and the correspondingly obtained first permutation bit vectors are [01010] and [11000] respectively.
And then extracting the first w elements in the first replacement bit vector, extracting the position of the first successful clustering in the first w elements, recording the first index value of the position of the successful clustering, performing modulus processing on the first index value, and finally obtaining a first hash template according to the first index value after the modulus processing.
Firstly, extracting the first w elements in each first permutation bit vector, for example, the first permutation bit vector contains 4000 elements, and w is 200; then, determining the successful position of the first clustering in the first w elements, namely the position of the first element being 1, and then recording the first index value t of the successful position of the clusteringiThe first index value is a numerical value corresponding to the position of the first element being 1, for example, w is 5, if the first 5 elements are 01000, the first index value is 2, and if the first 5 elements are 00001, the first index value is 5; then m first permuted bit vectors correspond to m first index values. Then, for the index value tiPerforming a modulo operation (mod) to obtain a first hash template te＝{ti e|i＝1,2,…,m}。
In one embodiment, referring to fig. 5, the fingerprinting fingerprint processor may include a third fused feature processing module, a second bit vector processing module and a second hash template processing module connected in sequence.
In one embodiment, the third fused feature processing module is configured to obtain a third fused feature vector of the minutiae to be identified.
That is to say, the fingerprint to be recognized is a fingerprint that needs to be recognized and authenticated in actual use, the minutiae points to be recognized are minutiae points contained in the fingerprint to be recognized, each minutia point to be recognized may include an end point and a bifurcation point of a fingerprint line, and the third fused feature vector reflects the position and the grayscale features of the minutiae points to be recognized.
Further, the third fused feature processing module may include a to-be-identified minutia obtaining module, a fifth fixed-length real number vector generating module, a sixth fixed-length real number vector generating module, and a second fused module, where the to-be-identified minutia obtaining module is connected to the fifth fixed-length real number vector generating module and the sixth fixed-length real number vector generating module, respectively, and the fifth fixed-length real number vector generating module and the sixth fixed-length real number vector generating module are both connected to the second fused module.
Specifically, the minutiae to be identified acquiring module is used for acquiring a plurality of minutiae to be identified of the fingerprint to be identified.
Specifically, the fifth fixed-length real number vector generation module is configured to process the minutiae to be identified and the pixel points in the fifth region corresponding to the minutiae to be identified according to a gaussian function to obtain a fifth fixed-length real number vector of the minutiae to be identified.
Further, the fifth fixed-length real number vector generation module is specifically configured to construct a fifth region with the minutiae point to be identified as a base point; obtaining a third Gaussian function value according to the polar coordinates of the detail point to be identified and the polar coordinates of each pixel point in the fifth area, and obtaining a third contribution value of each pixel point in the fifth area according to the third Gaussian function value; and then, obtaining a fifth fixed-length real number vector of the minutiae to be identified according to the third contribution value of each pixel point in the fifth region.
In order to better reflect the characteristics of each minutia point to be identified, when each minutia point to be identified is processed, a fifth region is selected in a certain shape by taking the minutia point to be identified as a base point, so that the fifth region can include the minutia point to be identified and pixels around the minutia point. The fifth area is not limited in this embodiment, and the fifth area may be, for example, a circle, a square, or the like.
Then, according to the polar coordinates of the minutiae to be identified and the polar coordinates of each pixel point in the fifth region, the distance between the remaining minutiae to be identified except the base point in the fifth region and each pixel point in the fifth region is obtained; and then, based on the distance between the detail point to be identified and each pixel point in the fifth region, obtaining a third Gaussian function value by using the Gaussian function, and then marking the third Gaussian function value obtained by each pixel point in the fifth region as a third contribution value of the pixel point.
That is to say, firstly, the polar coordinate of the minutiae to be recognized is obtained through polar coordinate conversion of the minutiae to be recognized, the polar coordinate of each pixel point is obtained through polar coordinate conversion of the pixel points in the fifth region, then the distance between the minutiae to be recognized and each pixel point in the fifth region is calculated through the polar coordinates of the remaining minutiae to be recognized except the base point in the fifth region and the polar coordinates of each pixel point in the fifth region, the obtained distance is substituted into the gaussian function, a third gaussian function value is obtained, and the third gaussian function value obtained by each pixel point in the fifth region is marked as a third contribution value of the pixel point.
And finally, after traversing all the pixel points in the fifth region according to a set sequence, combining the third contribution values of all the pixel points in the fifth region into a fifth fixed-length real number vector of the minutiae to be identified according to the set sequence, and correspondingly obtaining the fifth fixed-length real number vector of each minutia to be identified in the fifth region by the above method, wherein the set sequence can be set according to actual requirements, for example, the set sequence can be from left to right and from top to bottom.
Specifically, the sixth fixed-length real number vector generation module is configured to obtain a sixth fixed-length real number vector of the minutiae to be identified according to the minutiae to be identified and the gray levels of the pixels in the sixth region corresponding to the minutiae to be identified.
Further, the sixth fixed-length real number vector generation module is specifically configured to construct a sixth region with the minutiae point to be identified as a base point; obtaining a third texture characteristic value according to the difference value between the gray value of the to-be-identified minutia and the gray value of the pixel point in the sixth area; and obtaining a sixth fixed-length real number vector according to a third texture characteristic value of the pixel points in the sixth area.
In order to better reflect the characteristics of each minutia point to be identified, when each minutia point to be identified is processed, a sixth area is selected in a certain shape by taking the minutia point to be identified as a base point, so that the sixth area can include the minutia point to be identified and pixels around the minutia point. The sixth area is not limited in this embodiment, and may be, for example, a circle, a square, or the like.
And then, calculating the difference value between the gray value of the minutiae to be identified and the gray value of the pixel points in the sixth area, and recording the difference value as a third texture characteristic value of the pixel points in the sixth area.
And finally, after traversing all the pixel points in the sixth area according to the set sequence, combining all the third texture characteristic values according to the set sequence to form a sixth fixed-length real number vector of the minutiae to be identified, and correspondingly obtaining the sixth fixed-length real number vector of each minutia to be identified in the sixth area in the above manner, wherein the set sequence can be set according to actual requirements, for example, the set sequence can be from left to right and from top to bottom.
Specifically, the second fusion module is configured to perform dimensionality reduction on the fifth fixed-length real number vector and the sixth fixed-length real number vector respectively by using PCA, and then cascade the vectors into a third fusion feature vector.
Namely, the PCA method is utilized to perform dimensionality reduction on the fifth fixed-length real number vector and the sixth fixed-length real number vector of the minutiae to be registered respectively, the fifth fixed-length real number vector and the sixth fixed-length real number vector after dimensionality reduction are cascaded, and a vector obtained after cascading is the third fusion feature vector of the minutiae to be identified.
In one embodiment, the second bit vector processing module is configured to obtain the second bit vector according to the euclidean distance between the third fused feature vector and the first fused feature vector in the cluster center set.
That is, a vector is initialized, the length of the vector is equal to the number of first fusion feature vectors contained in a cluster center set, then the Euclidean distance between the obtained third fusion feature vector of the minutiae to be identified and each first fusion feature vector in the cluster center set is calculated, the first fusion feature vector with the minimum Euclidean distance in each third fusion feature vector is correspondingly obtained, the corresponding position in the initialized vector is distributed to be 1, the rest positions are distributed to be 0, and after all minutiae to be identified are traversed, the second bit vector of the fingerprint to be identified can be obtained.
In an embodiment, the second hash template processing module is configured to randomly generate m sets of second permutation seeds according to a locality sensitive hash algorithm, perform random permutation on the second bit vectors by using the m sets of second permutation seeds to obtain m second permutation bit vectors, and then obtain the second hash template according to the second permutation bit vectors.
That is, the hash code value is initialized first, each element is initialized to 0, and then m sets of second permutation seeds for performing position permutation on the obtained second bit vector are randomly generated.
And then, performing position permutation on the second bit vector according to a second permutation seed generated randomly by the second bit vector and correspondingly obtaining a second permutation bit vector, and performing random permutation on the second bit vector by the m groups of second permutation seeds to correspondingly obtain m second permutation bit vectors.
And then extracting the first w elements in the second replacement bit vector, extracting the position where the first clustering of the first w elements succeeds, recording a second index value of the position where the clustering succeeds, performing modulo processing on the second index value, and obtaining a second hash template according to the second index value after the modulo processing.
Firstly, extracting the first w elements in each second permutation bit vector; then, determining the successful position of the first clustering in the first w elements, namely the position of the first element being 1, and then recording the second index value t of the successful position of the clusteringjThe second index value is the first one is 1The m second permutation bit vectors are corresponding to the m second index values. Then, for the second index value tjPerforming a modulo operation (mod) to obtain a second hash template tq＝{tj q|j＝1,2,...,m}。
In a specific embodiment, the fingerprint matcher is specifically configured to obtain the matching result by using an encryption domain matching formula based on the first hash template and the second hash template, where the encryption domain matching formula is:
wherein, S (t)e,tq) To match the score, QeqMatching a vector for index values, which is composed of 0 and 1, and has a length equal to both the first hash template and the second hash template, and recording a position in the first hash template where the first index value is the same as the second index value in the second hash template as 1 and recording the rest positions as 0, for example, the first hash template is [135425 ]]The second hash template is [136435 ]]Then Q iseqIs [110101 ]]，|Qeq|＝4，BeIs the matching vector corresponding to the first hash template, BqFor the matching vector corresponding to the second hash template, BeAnd BqAre all binary matrices, Be、BqLength and Q ofeqEqual and initialized to zero matrix, teIn a position other than 0 is in BeThe corresponding position is denoted as 1, teIn the position of 0 is in BeThe corresponding position is noted as 0, tqIn a position other than 0 is in BqThe corresponding position is denoted as 1, tqIn the position of 0 is in BqThe corresponding position is noted as 0, e.g., the first hash template is [135425 ]]Then B iseIs [111111]The second hash template is [136435 ]]Then B isqIs [111111]Then | Be∩Bq6, final S (t)e,tq)＝4/6＝0.67。
In this embodiment, a threshold may be set when the resulting S (t) ise,tq) Is greater than the threshold valueIf the value is smaller than the threshold, the identification is considered to be successful, and if the value is smaller than the threshold, the identification is considered to be failed.
The fingerprint template protection method based on locality sensitive hashing, provided by the invention, maps original fingerprint features to an index value space which is not associated with original fingerprint information, ensures the irreversibility of the whole protection template, simultaneously, the modulus taking operation adopted by the method further enhances the safety intensity, the matching operation is carried out in an encryption domain, even if the template is lost, the original template information cannot be leaked, and the method has better safety.
The invention takes the randomly generated permutation seed as the user password, when the registered template is lost, the new permutation seed can be randomly replaced, and the new template can be issued. This makes the system based on the invention have better withdrawability and no relevance.
The invention designs a transformation method based on an index with the first 1 in a replacement bit vector based on a fingerprint unlocking system of locality sensitive hashing, and by optimizing the number of hash functions and related parameters, the matching performance loss before and after transformation is small (in the test of a public library FVC 2002DB1, the error rate of the system and the like is only 0.05 percent before and after characteristic transformation), and the invention has no special limitation on the type of biological characteristics and can be expanded to the template protection of other biological characteristics.
The fingerprint features extracted by the invention are alignment-free minutiae local features which have rotational translation invariance and can effectively avoid deformation damage and minutiae loss errors caused by scars, dust, fingerprint dryness and wetness degrees and different acquisition instrument environments. Meanwhile, the characteristics are finally stored in a fixed-length ordered bit vector form, so that the matching speed is high, and the storage consumption is low.
The method can effectively protect the original fingerprint information from being illegally stolen, can promote the safe development of the information industry, and has important market value.
In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples described in this specification can be combined and combined by those skilled in the art.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. A fingerprint identification unlocking system is characterized by comprising a lock body and a lock box arranged on the lock body, wherein a characteristic vector processor, a first memory, a registered fingerprint processor, a second memory, an identification fingerprint processor, a fingerprint matcher and a controller are arranged in the lock box,
the feature vector processor is used for obtaining a clustering center set according to first fusion feature vectors of the minutiae to be trained, wherein the clustering center set comprises a plurality of first fusion feature vectors;
a first memory coupled to the feature vector memory for storing the cluster center set;
the registered fingerprint processor is coupled with the first memory and used for acquiring a plurality of minutiae to be registered of the fingerprint to be registered and obtaining a first hash template according to the clustering center set and the second fusion characteristic vector of the minutiae to be registered;
a second memory coupled to the enrolled fingerprint processor for storing the first hash template;
the fingerprint identification processor is coupled with the first memory and used for acquiring a plurality of minutiae to be identified of the fingerprint to be identified and obtaining a second hash template according to the clustering center set and the third fusion feature vector of the minutiae to be identified;
the fingerprint matcher is respectively connected with the second memory and the fingerprint identification processor and is used for obtaining a matching result by using an encryption domain matching formula based on the first hash template and the second hash template;
and the controller is coupled with the fingerprint matcher and the lock body and is used for receiving the matching result, judging whether the matching result is successfully identified or failed to identify, and controlling the lock body to be opened if the matching result is successfully identified.
2. The fingerprint unlocking system of claim 1, wherein the first memory includes a ROM memory or a SDRAM memory.
3. The fingerprint unlocking system of claim 1, wherein the second memory includes a ROM memory or a SDRAM memory.
4. The fingerprint identification unlocking system of claim 1, further comprising an alarm, wherein the alarm is mounted on the lock body and coupled with the controller, and is used for controlling the alarm to give an alarm according to the matching result when the identification fails.
5. The fingerprint identification unlocking system of claim 4, wherein the alarm comprises a buzzer or an LED lamp.
6. The fingerprint identification unlocking system of claim 1, further comprising a wireless communication module, wherein the wireless communication module is installed inside the lock box and is in wireless connection with the controller, and is used for transmitting the matching result to a mobile phone terminal.
7. The fingerprint identification unlocking system of claim 6, wherein the wireless communication module comprises Bluetooth, wife or GPRS.
8. The fingerprint unlocking system of claim 1, wherein the feature vector processor comprises:
the acquisition module is used for acquiring a plurality of minutiae points to be trained;
the first processing module is connected with the acquisition module and used for processing the minutiae to be trained and pixel points in a first region corresponding to the minutiae to be trained according to a Gaussian function to obtain a first constant-length real number vector of the minutiae to be trained;
the second processing module is connected with the acquisition module and used for obtaining a second fixed-length real number vector of the minutiae to be trained according to the minutiae to be trained and the gray levels of pixel points in a second region corresponding to the minutiae to be trained;
the first fusion characteristic processing module is connected with the first processing module and the second processing module and is used for performing dimensionality reduction processing on the first fixed-length real number vector and the second fixed-length real number vector respectively by using PCA (principal component analysis), and then cascading the first fixed-length real number vector and the second fixed-length real number vector into a first fusion characteristic vector;
and the clustering module is connected with the first fusion characteristic processing module and used for clustering the first fusion characteristic vector by using a k-means algorithm to obtain a clustering center set.
9. The fingerprint identification unlocking system of claim 1, wherein the registered fingerprint processor comprises:
the second fusion feature processing module is used for acquiring a second fusion feature vector of the detail node to be registered;
the first bit vector processing module is connected with the second fusion feature processing module and used for obtaining a first bit vector according to the Euclidean distance between the second fusion feature vector and the first fusion feature vector in the cluster center set;
and the first hash template processing module is connected with the first bit vector processing module and used for randomly generating m groups of first permutation seeds according to a locality sensitive hash algorithm, randomly permutating the first bit vectors by using the m groups of first permutation seeds to obtain m first permutation bit vectors, and then obtaining the first hash template according to the first permutation bit vectors.
10. The fingerprint identification unlocking system of claim 1, wherein the identification fingerprint processor comprises:
the third fusion feature processing module is used for acquiring a third fusion feature vector of the minutiae to be identified;
the second bit vector processing module is connected with the third fusion characteristic processing module and used for obtaining a second bit vector according to the Euclidean distance between the third fusion characteristic vector and the first fusion characteristic vector in the cluster center set;
and the second hash template processing module is connected with the second bit vector processing module and used for randomly generating m groups of second permutation seeds according to a locality sensitive hash algorithm, randomly permutating the second bit vectors by using the m groups of second permutation seeds to obtain m second permutation bit vectors, and then obtaining the second hash template according to the second permutation bit vectors.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070005589A1 (en) * | 2005-07-01 | 2007-01-04 | Sreenivas Gollapudi | Method and apparatus for document clustering and document sketching |
| US20070130188A1 (en) * | 2005-12-07 | 2007-06-07 | Moon Hwa S | Data hashing method, data processing method, and data processing system using similarity-based hashing algorithm |
| CN107392121A (en) * | 2017-07-06 | 2017-11-24 | 同济大学 | A kind of adaptive device identification method and system based on fingerprint recognition |
| CN109766850A (en) * | 2019-01-15 | 2019-05-17 | 西安电子科技大学 | Fingerprint image matching method based on Fusion Features |
-
2019
- 2019-11-15 CN CN201911122268.1A patent/CN110942536B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070005589A1 (en) * | 2005-07-01 | 2007-01-04 | Sreenivas Gollapudi | Method and apparatus for document clustering and document sketching |
| US20070130188A1 (en) * | 2005-12-07 | 2007-06-07 | Moon Hwa S | Data hashing method, data processing method, and data processing system using similarity-based hashing algorithm |
| CN107392121A (en) * | 2017-07-06 | 2017-11-24 | 同济大学 | A kind of adaptive device identification method and system based on fingerprint recognition |
| CN109766850A (en) * | 2019-01-15 | 2019-05-17 | 西安电子科技大学 | Fingerprint image matching method based on Fusion Features |
Non-Patent Citations (2)
| Title |
|---|
| PANG LIAO-JUN: "identity Authentication Based on Fuzzy Vault", 《JOURNAL OF WUHAN UNIVERSITY OF TECHNOLOGY》 * |
| 庞辽军等: "一种基于扩散方程的指纹方向场提取方法", 《清华大学学报》 * |
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