CN109145834A - A kind of fingerprint recognition neural network based and verification method - Google Patents
A kind of fingerprint recognition neural network based and verification method Download PDFInfo
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- CN109145834A CN109145834A CN201810984751.XA CN201810984751A CN109145834A CN 109145834 A CN109145834 A CN 109145834A CN 201810984751 A CN201810984751 A CN 201810984751A CN 109145834 A CN109145834 A CN 109145834A
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- 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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Abstract
The invention discloses a kind of fingerprint recognition neural network based and verification methods, belong to fingerprint recognition field, the present invention identifies fingerprint using convolutional neural networks, it is verified by the way of random generation verifying fingerprint in the case where paying scene, it is not limited by fingerprint number not only, the safety of payment can greatly be improved, guarantees that user is in the payment environment of safety.
Description
Technical field
The present invention relates to fingerprint recognition fields, and in particular to a kind of fingerprint recognition neural network based and verification method.
Background technique
Fingerprint recognition refers to by comparing the details of different fingerprints and is identified that fingerprint identification technology is related to figure
As numerous subjects such as processing, pattern-recognition, computer vision, mathematical morphology, wavelet analysis.Due to everyone fingerprint not
Together, be exactly same people ten refer between, fingerprint also has significant difference, therefore fingerprint can be used for identity authentication.
Identity authentication is carried out using fingerprint, is related in numerous industries.In field of mobile phones, fingerprint is commonly used to as solution
Lock, payment, the verification tool downloaded.It is paid the bill using fingerprint, it is convenient due to operating, the favor of many people is obtained, therefore past
It is past to pay the bill in this way, however in some environments, due to the habit or inertial thinking of people, in output payment gold
After volume, also confirmation is not just paid by fingerprint repeatedly, so as to cause paying by mistake of fact.
Meanwhile the typing number of current mobile fingerprint is limited, generally five, can not the more fingerprints of typing, in benefit
When the operation such as being unlocked, pay the bill, downloading with fingerprint, the fingerprint of any one typing of input can be completed to verify.
It is verified using fingerprint, is not considered whether the people being verified is me, therefore many people can be by the finger of kith and kin
Line carries out typing simultaneously, when being paid, is logged the permission for having payment per capita of fingerprint, though it is kith and kin,
There can be payment risk.
Summary of the invention
It is an object of the invention to: a kind of fingerprint recognition neural network based and verification method are provided, solved at present
High, the limited problem of fingerprint typing number using risk when fingerprint progress mobile-phone payment.
The technical solution adopted by the invention is as follows:
A kind of fingerprint identification method neural network based, comprising the following steps:
Step 1: obtaining fingerprint, obtain fingerprint training sample；
Step 2: fingerprint training sample input convolutional neural networks model being trained, the convolution after being trained
Neural network model；
Step 3: obtaining fingerprint to be identified, the convolutional neural networks model after the fingerprint input training to be identified obtains
To recognition result.
Further, fingerprint is obtained in the step 1 is cellie's fingerprint, including cellie's both hands fingerprint
Intermediate region and fringe region；
The fingerprint training sample includes: cellie's fingerprint, and the non-cellie being built in mobile phone refers to
Line packet.
Further, training in the step 2 method particularly includes:
Step 21: cellie's fingerprint is inputted into convolutional neural networks model；
Step 22: issuing classification judgement request to user；
Step 23: user is according to the classification for judging the request selecting fingerprint training sample；
Step 24: the classification adjusting parameter that convolutional neural networks model is selected according to user；
Step 25: the random selection non-mobile phone of N group inputs the progress of convolutional neural networks model using the fingerprint in fingerprint packet
Training；
Step 26: repeat step 21-25, when convolutional neural networks model output recognition result accuracy be greater than threshold value,
Complete training.
Further, the output classification of the convolutional neural networks model includes: left hand thumb, left index finger, in left hand
Refer to, the left hand third finger, left hand little finger of toe, hand thumb, right hand index finger, right hand middle finger, right ring finger, right hand little finger of toe, non-use
Person.
A kind of fingerprint authentication method neural network based, comprising the following steps:
Step 1: judging the usage scenario of fingerprint, if non-pay scene, then use single fingerprint authentication method, otherwise jump
Go to step 2；
Step 2: generating the combination fingerprint authentication data of 2~4 fingerprints at random, and user is prompted to input corresponding verifying
Fingerprint；
Step 3: user inputs fingerprint according to prompt, obtains the fingerprint, and is sent to the convolutional Neural net after training
Network is identified, obtains recognition result；
Step 4: judging whether recognition result is consistent with verify data, if being consistent, paid, otherwise prompt payment is lost
It loses.
A kind of fingerprint recognition neural network based and verifying device, including
Fingerprint obtains module: for obtaining fingerprint；
Identification module: it is trained and identifies for built-in convolutional neural networks model；
Cue module: for issuing judgement request to user in cognitive phase, in Qualify Phase prompt user's input
Verify fingerprint；
Selecting module: for selecting the classification of fingerprint；
Judgment module: for judging the usage scenario of fingerprint；
Matching module: whether it is consistent for match cognization result with verification result；
Storage module: for storing non-cellie's fingerprint packet and user's finger print information.
In conclusion by adopting the above-described technical solution, the beneficial effects of the present invention are:
1. being trained and being identified using convolutional neural networks, accuracy of identification is high, is not limited by fingerprint quantity；
2. being verified by the way of combination fingerprint is randomly generated in the payment stage, can effectively improving the peace of payment
Quan Xing not only avoids maloperation, additionally it is possible to prevent other people from paying using fingerprint.
3. using method disclosed by the invention, it can effectively improve the precision of fingerprint recognition and be paid using fingerprint
The safety of (or sensitive operation) provides comparatively safe and privacy environment for user.
Detailed description of the invention
Examples of the present invention will be described by way of reference to the accompanying drawings, in which:
Fig. 1 is the flow chart of cognitive phase of the present invention；
Fig. 2 is the flow chart of Qualify Phase of the present invention；
Specific embodiment
Below in conjunction with attached drawing in the embodiment of the present invention, technical solution in the embodiment of the present invention carries out clear, complete
Ground description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Usually exist
The component of the embodiment of the present invention described and illustrated in attached drawing can be arranged and be designed with a variety of different configurations herein.Cause
This, is not intended to limit claimed invention to the detailed description of the embodiment of the present invention provided in the accompanying drawings below
Range, but it is merely representative of selected embodiment of the invention.Based on the embodiment of the present invention, those skilled in the art are not doing
Every other embodiment obtained under the premise of creative work out, shall fall within the protection scope of the present invention.
It elaborates below with reference to Fig. 1-2 to the present invention.
A kind of fingerprint identification method neural network based, comprising the following steps:
Step 1: obtaining fingerprint, obtain fingerprint training sample；
Step 2: fingerprint training sample input convolutional neural networks model being trained, the convolution after being trained
Neural network model；
Step 3: obtaining fingerprint to be identified, the convolutional neural networks model after the fingerprint input training to be identified obtains
To recognition result.
Further, fingerprint is obtained in the step 1 is cellie's fingerprint, including cellie's both hands fingerprint
Intermediate region and fringe region；
The fingerprint training sample includes: cellie's fingerprint, and the non-cellie being built in mobile phone refers to
Line packet.
Further, training in the step 2 method particularly includes:
Step 21: cellie's fingerprint is inputted into convolutional neural networks model；
Step 22: issuing classification judgement request to user；
Step 23: user is according to the classification for judging the request selecting fingerprint training sample；
Step 24: the classification adjusting parameter that convolutional neural networks model is selected according to user；
Step 25: the random selection non-mobile phone of N group inputs the progress of convolutional neural networks model using the fingerprint in fingerprint packet
Training；
Step 26: repeat step 21-25, when convolutional neural networks model output recognition result accuracy be greater than threshold value,
Complete training.
Further, the output classification of the convolutional neural networks model includes: left hand thumb, left index finger, in left hand
Refer to, the left hand third finger, left hand little finger of toe, hand thumb, right hand index finger, right hand middle finger, right ring finger, right hand little finger of toe, non-use
Person.
A kind of fingerprint authentication method neural network based, comprising the following steps:
Step 1: judging the usage scenario of fingerprint, if non-pay scene, then use single fingerprint authentication method, otherwise jump
Go to step 2；
Step 2: generating the combination fingerprint authentication data of 2~4 fingerprints at random, and user is prompted to input corresponding verifying
Fingerprint；
Step 3: user inputs fingerprint according to prompt, obtains the fingerprint, and is sent to the convolutional Neural net after training
Network is identified, obtains recognition result；
Step 4: judging whether recognition result is consistent with verify data, if being consistent, paid, otherwise prompt payment is lost
It loses.
A kind of fingerprint recognition neural network based and verifying device, including
Fingerprint obtains module: for obtaining fingerprint；
Identification module: it is trained and identifies for built-in convolutional neural networks model；
Cue module: for issuing judgement request to user in cognitive phase, in Qualify Phase prompt user's input
Verify fingerprint；
Selecting module: for selecting the classification of fingerprint；
Judgment module: for judging the usage scenario of fingerprint；
Matching module: whether it is consistent for match cognization result with verification result；
Storage module: for storing non-cellie's fingerprint packet and user's finger print information.
Specific embodiment 1
The present embodiment is used to specifically disclose a kind of fingerprint identification method neural network based,
Method includes the following steps:
Step 1: user obtains module using fingerprint and obtains cellie's fingerprint, use after waking up fingerprint function
Person's fingerprint includes left hand thumb, left index finger, left hand middle finger, the left hand third finger, left hand little finger of toe, hand thumb, right hand index finger, the right side
Hand middle finger, right ring finger, the centre of right hand little finger of toe and fringe region fingerprint, fingerprint of every acquisition once trained；
Non- cellie's fingerprint packet built in user's fingerprint combination mobile phone obtains fingerprint training sample；
Step 2: fingerprint training sample input convolutional neural networks model being trained, the convolution after being trained
Neural network model；
Step 21: user inputs the fingerprint of any finger arbitrary region, and fingerprint obtains after module obtains fingerprint and inputs volume
Product neural network model completes primary training,
Step 22: cue module issues classification judgement request, the i.e. class of the fingerprint of the last input of inquiry to user
Type；
Step 23: user is according to the classification for judging the request selecting fingerprint training sample；
Step 24: classification that convolutional neural networks model is selected according to user and the result actually identified compare, and adjust
Entire volume accumulates neural network parameter；
Step 25: since user's fingerprint is limited, the training sample that can be provided is also limited, therefore interior in storage module
It is equipped with non-cellie's fingerprint packet, is trained once when using user's fingerprint, identification module is transferred non-mobile phone and used
Data in person's fingerprint packet, the random selection non-mobile phone of N group are carried out using the fingerprint in fingerprint packet, input convolutional neural networks model
Training, correct recognition result are non-cellie；
Step 26: repeat step 21-25, when convolutional neural networks model output recognition result accuracy be greater than threshold value,
Complete training.
Step 3: obtaining fingerprint to be identified, the convolutional neural networks model after the fingerprint input training to be identified obtains
To recognition result.
Specific embodiment 2
The present embodiment specifically discloses a kind of fingerprint authentication method neural network based,
The following steps are included:
Step 1: judging that the usage scenario of fingerprint, fingerprint usage scenario include the field of the needs authorizations such as unlock, payment, downloading
Scape, judgment module judge whether the scene is payment scene (wechat payment, Alipay payment etc.), if non-pay scene, then
Using single fingerprint authentication method, that is, a fingerprint is inputted, if the result identified is left hand thumb, in left index finger, left hand
It is finger, the left hand third finger, any in left hand little finger of toe, hand thumb, right hand index finger, right hand middle finger, right ring finger, right hand little finger of toe
One kind can authorize；Otherwise jump procedure 2；
Step 2: generating the combination fingerprint authentication data of 2~4 fingerprints, such as the fingerprint sequentially input at random are as follows: left hand
Middle finger, right hand index finger, cue module prompt user to input corresponding verifying fingerprint；
Step 3: user inputs fingerprint according to prompt, that is, sequentially inputs the fingerprint of left hand middle finger and right hand index finger, per defeated
Enter once, identification module is once judged；
Step 4: judging that recognition result is consistent with verify data, then paid, otherwise prompt payment failure.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.It should also be noted that similar label and letter exist
Similar terms are indicated in following attached drawing, therefore, once being defined in a certain Xiang Yi attached drawing, are then not required in subsequent attached drawing
It is further defined and explained.
Claims (6)
1. a kind of fingerprint identification method neural network based, which comprises the following steps:
Step 1: obtaining fingerprint, obtain fingerprint training sample；
Step 2: fingerprint training sample input convolutional neural networks model being trained, the convolutional Neural after being trained
Network model；
Step 3: obtaining fingerprint to be identified, the convolutional neural networks model after the fingerprint input training to be identified is known
Other result.
2. a kind of fingerprint identification method neural network based according to claim 1, it is characterised in that: the step 1
Middle acquisition fingerprint is cellie's fingerprint, intermediate region and fringe region including cellie's both hands fingerprint；
The fingerprint training sample includes: cellie's fingerprint, and the non-cellie's fingerprint packet being built in mobile phone.
3. a kind of fingerprint identification method neural network based according to claim 1, it is characterised in that: the step 2
Middle training method particularly includes:
Step 21: cellie's fingerprint input convolutional neural networks model being trained, obtains recognition result；
Step 22: issuing the secondary fingerprint classification judgement request to user；
Step 23: user is according to the classification for judging the request selecting fingerprint training sample；
Step 24: the classification and recognition result adjusting parameter that convolutional neural networks model is selected according to user；
Step 25: the random selection non-mobile phone of N group is inputted convolutional neural networks model and is instructed using the fingerprint in fingerprint packet
Practice；
Step 26: repeat step 21-25, when convolutional neural networks model output recognition result accuracy be greater than threshold value, i.e., it is complete
At training.
4. a kind of fingerprint identification method neural network based according to claim 3, it is characterised in that: the convolution mind
Output classification through network model includes: left hand thumb, left index finger, left hand middle finger, the left hand third finger, left hand little finger of toe, the right hand
Thumb, right hand index finger, right hand middle finger, right ring finger, right hand little finger of toe, non-user.
5. a kind of fingerprint authentication method neural network based, it is characterised in that: the following steps are included:
Step 1: judging the usage scenario of fingerprint, if non-pay scene, then use single fingerprint authentication method, otherwise jump step
Rapid 2；
Step 2: generating the combination fingerprint authentication data of 2~4 fingerprints at random, and the corresponding verifying of user's input is prompted to refer to
Line；
Step 3: user according to prompt input fingerprint, obtain the fingerprint, and be sent to training after convolutional neural networks into
Row identification, obtains recognition result；
Step 4: judging whether recognition result is consistent with verify data, if being consistent, paid, otherwise prompt payment failure.
6. a kind of fingerprint recognition neural network based and verifying device, it is characterised in that: including
Fingerprint obtains module: for obtaining fingerprint；
Identification module: it is trained and identifies for built-in convolutional neural networks model；
Cue module: for issuing judgement request to user in cognitive phase, in Qualify Phase, prompt user inputs verifying
Fingerprint；
Selecting module: for selecting the classification of fingerprint；
Judgment module: for judging the usage scenario of fingerprint；
Matching module: whether it is consistent for match cognization result with verification result；
Storage module: for storing non-cellie's fingerprint packet and user's finger print information.
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| CN109921886A (en) * | 2019-01-28 | 2019-06-21 | 东南大学 | A kind of low power consuming devices radio-frequency fingerprint recognition methods of robust |
| CN112437926A (en) * | 2019-06-18 | 2021-03-02 | 神经技术Uab公司 | Fast robust friction ridge imprint detail extraction using feed-forward convolutional neural networks |
| CN112989307A (en) * | 2021-04-21 | 2021-06-18 | 北京金和网络股份有限公司 | Service information processing method, device and terminal |
| CN112989888A (en) * | 2019-12-17 | 2021-06-18 | 华为技术有限公司 | Fingerprint anti-counterfeiting method and electronic equipment |
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| CN112989307A (en) * | 2021-04-21 | 2021-06-18 | 北京金和网络股份有限公司 | Service information processing method, device and terminal |
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