CN109409227A - A kind of finger vena plot quality appraisal procedure and its device based on multichannel CNN - Google Patents
A kind of finger vena plot quality appraisal procedure and its device based on multichannel CNN Download PDFInfo
- Publication number
- CN109409227A CN109409227A CN201811113707.8A CN201811113707A CN109409227A CN 109409227 A CN109409227 A CN 109409227A CN 201811113707 A CN201811113707 A CN 201811113707A CN 109409227 A CN109409227 A CN 109409227A
- Authority
- CN
- China
- Prior art keywords
- feature vector
- cnn
- class
- finger vena
- gray level
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/28—Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- General Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Human Computer Interaction (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a kind of finger vena plot quality appraisal procedures and its device based on multichannel CNN.The gray level image of input is subjected to binarization operation and obtains binary image, gray level image and binary image are sent in corresponding CNN network simultaneously and carry out feature extraction, it the feature vector that obtains will be extracted carries out fusion and obtain fusion feature vector, it is classified to preset mass candidates class after calculating by full articulamentum fusion feature vector, the result according to affiliated candidate class as assessment.Multi-channel synchronous feature extraction is realized, quick and precisely the quality of finger vena figure is assessed.
Description
Technical field
The present invention relates to field of biological recognition, especially a kind of finger vena plot quality assessment side based on multichannel CNN
Method and its device.
Background technique
Currently, finger vena identification is as emerging biometrics identification technology, it is the important composition of the following identification technology
Part.And in collection process, since acquisition equipment and the situation for being collected individual are different, the finger vena acquired every time
It is low that the quality of figure also has height to have, and low-quality finger vena figure is usually relatively fuzzyyer, if by low-quality finger vena caption volume
Into database, it will seriously affect feature extraction and matching when later period application, cause the recognition performance of system poor, so needing
Low-quality finger vena figure is rejected in collection process, the finger vena figure of high quality is registered in database, therefore
How to carry out assessment to the quality of finger vena figure is committed step.
It is substantially the figure using computer technology to finger vena figure for the quality evaluation of finger vena figure
Feature extracts, and can be divided into the Feature Parameter Fusion side of manual designs in conventional method according to the difference of feature extraction mode
Method, vein point number statistical method.Although these methods, which can lead to, realizes quality evaluation, often very when encountering complicated image
Hardly possible by manual designs go out effectively and robust feature extracting method, vein point number statistical method need to finger vena figure into
A large amount of image procossing early period of row, process is complicated and takes a long time.It is most of using individually for binary picture in the prior art
Picture or grayscale image are identified, but grayscale image generally comprises more noise, and binary image is easy during binaryzation
Information is lost, therefore the characteristic pattern accuracy obtained is lower.
Summary of the invention
It can be right by CNN (convolutional neural networks) to solve the above problems, the purpose of the present invention is to provide a kind of methods
The two-value of finger vena figure is merged after drawing the feature extraction with grayscale image, carries out matter to finger vena figure using fusion feature
Amount assessment.Multichannel extracts, and improves the accuracy of characteristic pattern.
Technical solution used by the present invention solves the problems, such as it is: a kind of finger vena plot quality based on multichannel CNN
Appraisal procedure, comprising the following steps:
The gray level image for reading input carries out binarization operation to the gray level image, obtains binary image；
The binary image and gray level image are respectively sent to progress feature in the convolutional layer of corresponding CNN network to mention
It takes, obtains binaryzation feature vector and gray feature vector；
By the binaryzation feature vector and gray feature vector fused in tandem, fusion feature vector is obtained, and be sent to
In the full articulamentum of CNN network, class probability vector is obtained by flexible max calculation；
Preset mass candidates class is read, the class probability vector is classified in corresponding mass candidates class, is completed
Quality evaluation.
Further, it is described to the gray level image carry out binarization operation include: to gray level image execute gray scale normalization,
Based on direction paddy shape detection enhancing and local adaptive threshold fuzziness.
Further, the convolutional layer of the CNN network includes gray scale training convolutional layer and binaryzation training convolutional layer；It is described
The convolutional layer of CNN network includes 4 layers of convolutional network, and the convolutional network includes convolutional layer, pond layer and random deactivating layer.
Further, the convolution kernel of the convolutional layer is 3 × 3, and convolution step-length is 1；The template of the pond layer is 2 × 2, step
A length of 2.
Further, the full articulamentum includes the first full articulamentum and the second full articulamentum, the first full articulamentum packet
512 neurons are included, the second full articulamentum includes 2 neurons.
Further, the mass candidates class includes high quality class and low quality class.
A kind of finger vena plot quality assessment device based on multichannel CNN, including following device:
Binary image acquisition device carries out binaryzation behaviour to the gray level image for reading the gray level image of input
Make, obtains binary image；
Feature vector acquisition device, for the binary image and gray level image to be respectively sent to corresponding CNN net
Feature extraction is carried out in the convolutional layer of network, obtains binaryzation feature vector and gray feature vector；
Fusion feature vector acquisition device is used for the binaryzation feature vector and gray feature vector fused in tandem,
Obtain fusion feature vector, and be sent in the full articulamentum of CNN network, by flexible max calculation obtain class probability to
Amount；
The class probability vector is classified to corresponding by quality assessment device for reading preset mass candidates class
In mass candidates class, quality evaluation is completed.
Further, the binary image acquisition device further includes following device:
Binarization operation device, for executing gray scale normalization, based on the detection enhancing drawn game of direction paddy shape to gray level image
Portion's adaptive threshold fuzziness.
A kind of finger vena plot quality assessment device based on multichannel CNN, comprising:
At least one processor；And
The memory being connect at least one described processor communication；
Wherein, the memory is stored with the instruction that can be executed by least one described processor, and described instruction is described
At least one processor executes, so that at least one described processor is able to carry out one kind as described above and is based on multichannel
The finger vena plot quality appraisal procedure of CNN.
A kind of non-transitorycomputer readable storage medium, the computer-readable recording medium storage have computer that can hold
Row instruction, the computer executable instructions are for making computer execute a kind of hand based on multichannel CNN as described above
Refer to vein plot quality appraisal procedure.
The beneficial effects of the present invention are: the present invention uses a kind of finger vena plot quality assessment side based on multichannel CNN
Method and its device.Binary image is obtained according to the gray level image of input, and the two is respectively sent to corresponding CNN network simultaneously
Feature extraction is carried out, then the feature vector obtained is subjected to fusion and obtains fusion feature vector, it is special to fusion using full articulamentum
Vector Processing is levied, and then completes the classification of mass candidates class, the quality evaluation according to locating classification as input picture.It compares
The method that the prior art only extracts gray level image or binary image is played, method of the invention can be effectively by gray level image and two-value
Change image to be merged, solves the problems, such as that extracted picture noise is excessive in the prior art and information is lost, realize matter
Measure the Accuracy and high efficiency of assessment.
Detailed description of the invention
The invention will be further described with example with reference to the accompanying drawing.
Fig. 1 is a kind of method flow diagram of the finger vena plot quality appraisal procedure based on multichannel CNN of the present invention；
Fig. 2 is a kind of volume of the first embodiment of the finger vena plot quality appraisal procedure based on multichannel CNN of the present invention
Product schematic network structure；
Fig. 3 is a kind of CNN network diagram of the finger vena plot quality appraisal procedure based on multichannel CNN of the present invention；
Fig. 4 is a kind of detailed step figure of the finger vena plot quality appraisal procedure based on multichannel CNN of the present invention；
Fig. 5 is a kind of volume of the second embodiment of the finger vena plot quality appraisal procedure based on multichannel CNN of the present invention
Product schematic network structure；
Fig. 6 is a kind of volume of the 3rd embodiment of the finger vena plot quality appraisal procedure based on multichannel CNN of the present invention
Product schematic network structure.
Drawing reference numeral explanation:
1. binary image；2. gray level image；3. binaryzation convolution trains layer；4. gray scale convolution trains layer；5. full connection
Layer.
Specific embodiment
Referring to Fig.1, a kind of finger vena plot quality appraisal procedure based on multichannel CNN of the invention, including following step
It is rapid:
The gray level image for reading input carries out binarization operation to the gray level image, obtains binary image；
The binary image and gray level image are respectively sent to progress feature in the convolutional layer of corresponding CNN network to mention
It takes, obtains binaryzation feature vector and gray feature vector；
By the binaryzation feature vector and gray feature vector fused in tandem, fusion feature vector is obtained, and be sent to
In the full articulamentum of CNN network, class probability vector is obtained by flexible max calculation；
Preset mass candidates class is read, the class probability vector is classified in corresponding mass candidates class, is completed
Quality evaluation.
Wherein, after carrying out binarization operation to the gray level image, also retain the gray level image for subsequent operation.
Preferably, the CNN network is preparatory trained depth convolutional neural networks.
Wherein, when classifying to the class probability vector, using biggish one of generic probability as selected
Classification.
Further, it is described to the gray level image carry out binarization operation include: to gray level image execute gray scale normalization,
Based on direction paddy shape detection enhancing and local adaptive threshold fuzziness.
Wherein, when detecting input gray level image, gray value of image is normalized to [0,255], normalization formula is
Wherein, carrying out enhancing by the way of based on the detection enhancing of direction paddy shape can preferably be partitioned into veinprint
Come, while the operation based on the detection enhancing of direction paddy shape only carries out integer calculations, to compared with other increasings using floating type operation
Time consumed by strong method is shorter, is conducive to accelerate recognition speed.
Wherein, the local auto-adaptive Threshold segmentation determines each picture according to the distribution of enhanced image peripheral pixel
The segmentation threshold of element uses local auto-adaptive Threshold segmentation to enhanced image since the figure of finger vena figure is irregular
Be conducive to the image for dividing brightness or contrast distribution unevenness.
Referring to figs. 2 and 3, further, the convolutional layer of the CNN network includes gray scale training convolutional layer 4 and binaryzation instruction
Practice convolutional layer 3；The convolutional layer of the CNN network include 4 layers of convolutional network, the convolutional network include convolutional layer, pond layer and
Random deactivating layer.
Wherein, every layer of convolutional network is by being all the linear function of upper layer output, therefore can exist between each parameter mutual
Interdependent relationship, it is easy to cause hierarchy of 00operation more deeper more complicated.In order to improve the efficiency of operation, transported in each layer of convolutional network
Activation primitive is introduced after having calculated to be activated, and is made the output 0 of partial nerve member, is thinned out network, reduces between parameter
Relation of interdependence.
Preferably, preceding 3 layers of activation primitive uses Relu activation primitive, and the 4th layer uses softmax activation primitive.
Preferably, the random inactivation probability of the random deactivating layer of preceding level 2 volume product network is 0.3, rear level 2 volume product network with
The random inactivation probability of machine deactivating layer is 0.5.
Further, the convolution kernel of the convolutional layer is 3 × 3, and convolution step-length is 1；The template of the pond layer is 2 × 2, step
A length of 2.
Wherein, after 4 layers of convolutional network, the characteristic dimension of the binaryzation feature vector and gray feature vector is
512 dimensions, therefore the dimension of resulting fusion feature vector is 1024 dimensions.
Further, the full articulamentum includes the first full articulamentum and the second full articulamentum, the first full articulamentum packet
512 neurons are included, the second full articulamentum includes 2 neurons.
Wherein, it since the dimension of the fusion feature vector is 1024 dimensions, is tieed up by residue 512 after the first full articulamentum,
It is tieed up by residue 2 after the second full articulamentum, by being classified as high quality class and low quality class to what image carried out in this present embodiment,
Therefore the second full articulamentum selects 2 neurons to realize classification.
Further, the mass candidates class includes high quality class and low quality class.
Wherein, the classification standard of the mass candidates class is as obtained by training in advance.
With reference to Fig. 4, disposed of in its entirety step of the present invention is illustrated by way of example below:
Step 101, the gray level image for reading input carries out binarization operation to the gray level image, obtains binary picture
Picture；
Step 102, gray level image is sent to the gray scale training convolutional layer of CNN network；Binary image is sent to CNN
The binaryzation training convolutional layer of network；
Step 103, gray level image is trained in gray scale training convolutional layer, obtains the gray feature vector of 512 dimensions；
Binary image is trained in binaryzation training convolutional layer, obtains the binaryzation feature vector of 512 dimensions；
Step 104, gray feature vector sum binaryzation feature vector is merged, obtains the fusion feature vector of 1024 dimensions；
Step 105, the fusion feature vector is sent to full articulamentum, by the flexible most matter of fundamental importance in full articulamentum
It calculates, obtains class probability vector；
Step 106, preset mass candidates class is read, by class probability vector point into the highest candidate class of probability；
Step 107, if the class probability vector is low quality classification, low quality hand is set by the grayscale image of input
Refer to vein figure；If the class probability vector is high quality classification, set the grayscale image of input to do quality finger vena
Figure.
A kind of finger vena plot quality assessment device based on multichannel CNN, including following device:
Binary image acquisition device carries out binaryzation behaviour to the gray level image for reading the gray level image of input
Make, obtains binary image；
Feature vector acquisition device, for the binary image and gray level image to be respectively sent to corresponding CNN net
Feature extraction is carried out in the convolutional layer of network, obtains binaryzation feature vector and gray feature vector；
Fusion feature vector acquisition device is used for the binaryzation feature vector and gray feature vector fused in tandem,
Obtain fusion feature vector, and be sent in the full articulamentum of CNN network, by flexible max calculation obtain class probability to
Amount；
The class probability vector is classified to corresponding by quality assessment device for reading preset mass candidates class
In mass candidates class, quality evaluation is completed.
Further, the binary image acquisition device further includes following device:
Binarization operation device, for executing gray scale normalization, based on the detection enhancing drawn game of direction paddy shape to gray level image
Portion's adaptive threshold fuzziness.
A kind of finger vena plot quality assessment device based on multichannel CNN, comprising:
At least one processor；And
The memory being connect at least one described processor communication；
Wherein, the memory is stored with the instruction that can be executed by least one described processor, and described instruction is described
At least one processor executes, so that at least one described processor is able to carry out one kind as described above and is based on multichannel
The finger vena plot quality appraisal procedure of CNN.
A kind of non-transitorycomputer readable storage medium, the computer-readable recording medium storage have computer that can hold
Row instruction, the computer executable instructions are for making computer execute a kind of hand based on multichannel CNN as described above
Refer to vein plot quality appraisal procedure.
With reference to Fig. 5, a kind of finger vena plot quality appraisal procedure based on multichannel CNN, basic procedure and first is in fact
Apply that example is essentially identical, there is following difference: the convolutional layer of the CNN network includes 3 layers of convolutional network, wherein first 2 layers of activation letter
Number uses Relu activation primitive, and the 3rd layer uses softmax activation primitive；The random mistake of the random deactivating layer of preceding level 2 volume product network
Probability living is that the random inactivation probability of the random deactivating layer of the 0.3, the 3rd layer of convolutional network is 0.5.
With reference to Fig. 6, a kind of finger vena plot quality appraisal procedure based on multichannel CNN, basic procedure and first is in fact
Apply that example is essentially identical, there is following difference: the convolutional layer of the CNN network includes 5 layers of convolutional network, wherein first 4 layers of activation letter
Number uses Relu activation primitive, and the 5th layer uses softmax activation primitive；The random mistake of the random deactivating layer of preceding level 2 volume product network
Probability living is 0.3, and the random inactivation probability of the random deactivating layer of rear 3 layers of convolutional network is 0.5.
Through the above description of the embodiments, those of ordinary skill in the art can be understood that each embodiment
The mode of general hardware platform can be added to realize by software, naturally it is also possible to pass through hardware.Those of ordinary skill in the art can
With understand all or part of the process realized in above-described embodiment method be can be instructed by computer program it is relevant hard
Part is completed, and the program can be stored in a computer-readable storage medium, the program is when being executed, it may include as above
State the process of the embodiment of each method.Wherein, the storage medium can be magnetic disk, CD, read-only memory (Read-
Only Memory, ROM) or random access memory (Random Access Memory, RAM) etc..
The above, only presently preferred embodiments of the present invention, the invention is not limited to above embodiment, as long as
It reaches technical effect of the invention with identical means, all should belong to protection scope of the present invention.
Claims (10)
1. a kind of finger vena plot quality appraisal procedure based on multichannel CNN, which comprises the following steps:
The gray level image for reading input carries out binarization operation to the gray level image, obtains binary image；
The binary image and gray level image are respectively sent to carry out feature extraction in the convolutional layer of corresponding CNN network,
Obtain binaryzation feature vector and gray feature vector；
By the binaryzation feature vector and gray feature vector fused in tandem, fusion feature vector is obtained, and be sent to CNN net
In the full articulamentum of network, class probability vector is obtained by flexible max calculation；
Preset mass candidates class is read, the class probability vector is classified in corresponding mass candidates class, completes quality
Assessment.
2. a kind of finger vena plot quality appraisal procedure based on multichannel CNN according to claim 1, feature exist
In described includes: to execute gray scale normalization, based on direction paddy shape to gray level image to gray level image progress binarization operation
Detection enhancing and local adaptive threshold fuzziness.
3. a kind of finger vena plot quality appraisal procedure based on multichannel CNN according to claim 1, feature exist
In: the convolutional layer of the CNN network includes gray scale training convolutional layer and binaryzation training convolutional layer；The convolution of the CNN network
Layer includes 4 layers of convolutional network, and the convolutional network includes convolutional layer, pond layer and random deactivating layer.
4. a kind of finger vena plot quality appraisal procedure based on multichannel CNN according to claim 3, feature exist
In: the convolution kernel of the convolutional layer is 3 × 3, and convolution step-length is 1；The template of the pond layer is 2 × 2, step-length 2.
5. a kind of finger vena plot quality appraisal procedure based on multichannel CNN according to claim 1, feature exist
In: the full articulamentum includes the first full articulamentum and the second full articulamentum, and the first full articulamentum includes 512 nerves
Member, the second full articulamentum include 2 neurons.
6. a kind of finger vena plot quality appraisal procedure based on multichannel CNN according to claim 1, feature exist
In: the mass candidates class includes high quality class and low quality class.
7. a kind of finger vena plot quality based on multichannel CNN assesses device, which is characterized in that including following device:
Binary image acquisition device carries out binarization operation to the gray level image, obtains for reading the gray level image of input
Binary image out；
Feature vector acquisition device, for the binary image and gray level image to be respectively sent to corresponding CNN network
Feature extraction is carried out in convolutional layer, obtains binaryzation feature vector and gray feature vector；
Fusion feature vector acquisition device, for obtaining the binaryzation feature vector and gray feature vector fused in tandem
Fusion feature vector, and be sent in the full articulamentum of CNN network, class probability vector is obtained by flexible max calculation；
The class probability vector is classified to corresponding quality for reading preset mass candidates class by quality assessment device
In candidate class, quality evaluation is completed.
8. a kind of finger vena plot quality based on multichannel CNN according to claim 7 assesses device, feature exists
In the binary image acquisition device further includes following device: binarization operation device, for executing gray scale to gray level image
Normalization detects enhancing and local adaptive threshold fuzziness based on direction paddy shape.
9. a kind of finger vena plot quality based on multichannel CNN assesses device characterized by comprising
At least one processor；And
The memory being connect at least one described processor communication；
Wherein, the memory be stored with can by least one described processor execute instruction, described instruction by it is described at least
One processor executes, so that at least one described processor is able to carry out as the method according to claim 1 to 6.
10. a kind of non-transitorycomputer readable storage medium, it is characterised in that: the computer-readable recording medium storage has
Computer executable instructions, the computer executable instructions are for executing computer as described in claim any one of 1-6
Method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811113707.8A CN109409227A (en) | 2018-09-25 | 2018-09-25 | A kind of finger vena plot quality appraisal procedure and its device based on multichannel CNN |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811113707.8A CN109409227A (en) | 2018-09-25 | 2018-09-25 | A kind of finger vena plot quality appraisal procedure and its device based on multichannel CNN |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109409227A true CN109409227A (en) | 2019-03-01 |
Family
ID=65465088
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811113707.8A Pending CN109409227A (en) | 2018-09-25 | 2018-09-25 | A kind of finger vena plot quality appraisal procedure and its device based on multichannel CNN |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109409227A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110084238A (en) * | 2019-04-09 | 2019-08-02 | 五邑大学 | Finger vena image segmentation method, device and storage medium based on LadderNet network |
| CN110251137A (en) * | 2019-06-05 | 2019-09-20 | 长沙湖湘医疗器械有限公司 | A kind of sleep detection method for noninvasive ventilator and the ventilator using this method |
| CN110292379A (en) * | 2019-07-02 | 2019-10-01 | 重庆大学 | The recognizer of epilepsy non-invasive diagnosis based on convolutional neural networks |
| CN110738141A (en) * | 2019-09-26 | 2020-01-31 | 五邑大学 | vein identification method, device, equipment and storage medium |
| WO2020199498A1 (en) * | 2019-04-03 | 2020-10-08 | 平安科技（深圳）有限公司 | Palmar digital vein comparison method and device, computer apparatus, and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8872909B2 (en) * | 2010-06-10 | 2014-10-28 | The Hong Kong Polytechnic University | Method and apparatus for personal identification using finger imaging |
| CN106326886A (en) * | 2016-11-07 | 2017-01-11 | 重庆工商大学 | Finger-vein image quality evaluation method and system based on convolutional neural network |
| CN106529468A (en) * | 2016-11-07 | 2017-03-22 | 重庆工商大学 | Finger vein identification method and system based on convolutional neural network |
| KR102160184B1 (en) * | 2017-06-02 | 2020-09-28 | 동국대학교 산학협력단 | Finger vein recognition device and recognition method using convolutional neural network |
-
2018
- 2018-09-25 CN CN201811113707.8A patent/CN109409227A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8872909B2 (en) * | 2010-06-10 | 2014-10-28 | The Hong Kong Polytechnic University | Method and apparatus for personal identification using finger imaging |
| CN106326886A (en) * | 2016-11-07 | 2017-01-11 | 重庆工商大学 | Finger-vein image quality evaluation method and system based on convolutional neural network |
| CN106529468A (en) * | 2016-11-07 | 2017-03-22 | 重庆工商大学 | Finger vein identification method and system based on convolutional neural network |
| KR102160184B1 (en) * | 2017-06-02 | 2020-09-28 | 동국대학교 산학협력단 | Finger vein recognition device and recognition method using convolutional neural network |
Non-Patent Citations (1)
| Title |
|---|
| 胡晶晶: "手指经脉图像质量评估算法综述", 《企业技术开发》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020199498A1 (en) * | 2019-04-03 | 2020-10-08 | 平安科技（深圳）有限公司 | Palmar digital vein comparison method and device, computer apparatus, and storage medium |
| JP2021533493A (en) * | 2019-04-03 | 2021-12-02 | 平安科技(深▲せん▼)有限公司Ping An Technology (Shenzhen) Co., Ltd. | Finger vein matching method, device, computer equipment, and storage medium |
| US11893773B2 (en) | 2019-04-03 | 2024-02-06 | Ping An Technology (Shenzhen) Co., Ltd. | Finger vein comparison method, computer equipment, and storage medium |
| CN110084238A (en) * | 2019-04-09 | 2019-08-02 | 五邑大学 | Finger vena image segmentation method, device and storage medium based on LadderNet network |
| CN110084238B (en) * | 2019-04-09 | 2023-01-03 | 五邑大学 | Finger vein image segmentation method and device based on LadderNet network and storage medium |
| CN110251137A (en) * | 2019-06-05 | 2019-09-20 | 长沙湖湘医疗器械有限公司 | A kind of sleep detection method for noninvasive ventilator and the ventilator using this method |
| CN110292379A (en) * | 2019-07-02 | 2019-10-01 | 重庆大学 | The recognizer of epilepsy non-invasive diagnosis based on convolutional neural networks |
| CN110738141A (en) * | 2019-09-26 | 2020-01-31 | 五邑大学 | vein identification method, device, equipment and storage medium |
| WO2021056974A1 (en) * | 2019-09-26 | 2021-04-01 | 五邑大学 | Vein recognition method and apparatus, device, and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109409227A (en) | A kind of finger vena plot quality appraisal procedure and its device based on multichannel CNN | |
| WO2018072233A1 (en) | Method and system for vehicle tag detection and recognition based on selective search algorithm | |
| CN103996018B (en) | Face identification method based on 4DLBP | |
| CN107977671A (en) | A kind of tongue picture sorting technique based on multitask convolutional neural networks | |
| CN107103317A (en) | Fuzzy license plate image recognition algorithm based on image co-registration and blind deconvolution | |
| CN104809481A (en) | Natural scene text detection method based on adaptive color clustering | |
| CN105574063A (en) | Image retrieval method based on visual saliency | |
| Burie et al. | ICFHR2016 competition on the analysis of handwritten text in images of balinese palm leaf manuscripts | |
| CN110555382A (en) | Finger vein identification method based on deep learning and Wasserstein distance measurement | |
| CN109086794B (en) | Driving behavior pattern recognition method based on T-LDA topic model | |
| CN111008647B (en) | Sample extraction and image classification method based on void convolution and residual linkage | |
| CN112949464B (en) | Face changing counterfeiting detection method, system and equipment based on three-dimensional shape of human face | |
| CN109086772A (en) | A kind of recognition methods and system distorting adhesion character picture validation code | |
| CN111428701B (en) | Small-area fingerprint image feature extraction method, system, terminal and storage medium | |
| CN110874835B (en) | Crop leaf disease resistance identification method and system, electronic equipment and storage medium | |
| CN114693616A (en) | Rice disease detection method, equipment and medium based on improved target detection model and convolutional neural network | |
| CN106650696A (en) | Handwritten electrical element identification method based on singular value decomposition | |
| CN115170548A (en) | Leather defect automatic detection method and device based on unsupervised learning | |
| CN114897825A (en) | Solid wood floor sorting method and system based on computer vision | |
| CN107357834A (en) | Image retrieval method based on visual saliency fusion | |
| CN108108700B (en) | Pig feature region identification method based on chord axis transformation | |
| CN114092814A (en) | Unmanned plane navel orange tree image target identification and statistics method based on deep learning | |
| CN109741351A (en) | A kind of classification responsive type edge detection method based on deep learning | |
| CN112446417B (en) | Spindle-shaped fruit image segmentation method and system based on multilayer superpixel segmentation | |
| Rotem et al. | Combining region and edge cues for image segmentation in a probabilistic gaussian mixture framework |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190301 |