CN109635618A - Visible images vein developing method based on convolutional neural networks - Google Patents
Visible images vein developing method based on convolutional neural networks Download PDFInfo
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Abstract
The invention discloses a kind of visible images vein developing method based on convolutional neural networks, belong to a kind of information Perception and identification technology, by devising design, full convolutional neural networks vein images MODEL C Y-Net to the present invention end to end, near-infrared arm image is pre-processed first, then using pairs of visible light arm image and pretreated NIR image as training sample, the convolutional neural networks of one five layers of training end-to-endly, and the last full articulamentum of network is changed to convolutional layer, form full convolutional neural networks, network is set to learn the mapping relations between visible light-near-infrared image automatically, obtain the mapping graph of target；Finally vein extraction is carried out with vein imaging figure of the methods of the Gabor filtering to network output.Method of the invention is more preferable than the visible light developing method effect based on optics and based on three layer feedforward neural networks, embodies preferable robustness, and demonstrate the validity of the method for the present invention.
Description
Technical field
The invention belongs to a kind of information Perceptions and identification technology, in particular to a kind of visible light based on convolutional neural networks
Image vein developing method.
Background technique
In medical domain, intravenous injection is a kind of common treatment means, but the skin of some patients is difficult to find
Vein, such as subcutaneous fat is thicker or chaeta is more, vein is relatively thin, the colour of skin is deeper, this makes the success rate of venipuncture not high.
Although existing research person develops venous locations detector, traditional hand vein recognition instrument must rely near infrared imaging and set
It is standby, it is at high cost, difficult in maintenance.Visible images vein developing method proposed in recent years based on optics and skin physiology can
To overcome this limitation (Chaoying Tang, Adams Wai Kin Kong, N.C.Uncovering vein patterns
from color skin images for forensic analysis[C]//IEEE International
Conference on Computer Vision and Pattern Recognition, 2011:665-672), but the party
There is noise, algorithm complexity, to the poor robustness of different skin condition in method.Patent of invention: visible light skin image
Vein developing method (application number: 201410734150.5) proposes a kind of visible light imaging based on three layer feedforward neural networks
Method, but its structure is too simple, learning ability is weak, therefore imaging results are also limited to.
Deep learning is a kind of method of automatic learning characteristic, wherein convolutional neural networks (Convolutional
Neural Networks, CNN) pixel can be retained when handling image spatial relation, its structure is to figure
There is height invariance therefore to be very suitable to do the feature of two-dimensional image data for the deformation such as rotation, translation, the scaling of picture
It extracts.
The process that vein is extracted from visible light arm image includes that object detection and lines extract in terms of two.Object inspection
It surveys, i.e., detects arm from input picture；Lines extract, i.e., the visible light and near-infrared image learnt using network model
Mapping relations realized from the arm detected vein image, finally carry out lines extraction again.Traditional object detecting method
It is low-level feature using the feature of design, it is insufficient to the ability to express of target.Deep learning method can be from a large amount of sample
High-level characteristic is arrived in automatic study, and compared to the feature of engineer, the feature learnt is more abundant, ability to express is also stronger.
With the continuous development of deep learning, researcher's discovery carries out target detection using CNN, and accuracy rate can be mentioned substantially
It rises, compared to Conventional visual feature, convolutional neural networks visual signature is more suitable for image detection task.In such as venous blood
Pipe, lane, course line lines extract aspect, traditional algorithm is strong to the identification of feature.And the expression way of feature excessively according to
Rely artificial selection, researcher not can guarantee the accurate selection and expression to feature.The deep layer group of deep learning main analog human brain
Structure is knitted, low-level feature is combined.More abstract effective high-rise expression is formed, therefore mark sheet can be carried out automatically
It reaches.
Superfine finger hand vein recognition algorithm (the finger hand vein recognition research based on deep learning proposed based on deep learning of Wu
[J] computer technology and development, 2018 (2): 200-204), it proposes to improve on the basis of Alex Net deep neural network
Scheme, carrying out classification to finger vein image has better result；Qin Zhi light etc. proposes a kind of based on multi-modal CNN model
((cerebrovascular Study on Extraction Method [J] Journal of UEST of China based on multi-modal convolutional neural networks, 2016,45 (4):
573-581)), brain CT angiographic image is split, and has been experimentally confirmed this model and has been extracted in the cerebrovascular
On validity；The it is proposeds such as Wang Yiding obtained using wavelet decomposition and the method for down-sampling it is multiple dimensioned under hand back vein figure
Picture, then the extraction of characteristics of image is carried out with centrosymmetric local binary patterns, it is successively trained using limitation Boltzmann machine
And discrimination (the hand based on deep learning and multiple dimensioned coded combination of vein is improved by the method for multiple dimensioned coded combination
Dorsal vein identifies [J] North China University of Tech journal, 2015,27 (3): 6-13)；Zhang Guiying etc. uses two kinds of nets based on CNN
Network structure automatically learns and extracts the feature of optic disk in retinal images, realizes the automatic detection of optic disk, substantially increases view
Disk detection accuracy rate (optic disk of Zhang Guiying, the Zhang Xianjie based on deep learning detects the Guizhou [J] college of education journal automatically,
2017,33(3):27-32)。
Convolutional neural networks can extract high-level characteristic, and feature extraction, feature selecting and tagsort are merged same
Training, non local on the whole to optimize in model, is enhanced the separability of feature.CNN is mentioned applied to vein
Other methods can effectively be overcome the shortcomings of by taking.But there are no the skills that CNN is used for the imaging of visible light vein in the prior art
Art.
Summary of the invention
The present invention is directed to traditional vein and images the dependence near infrared gear and obtained in the prior art using optical means
To image there are obvious noise, imaging results are limited, and the defect of poor robustness proposes a kind of based on the visible of deep learning
CNN is used for the technology of visible light vein imaging, solved by light image vein developing method by devising a kind of CNN model
Problems of the prior art.
The present invention is implemented as follows:
The invention discloses a kind of visible images vein developing method based on convolutional neural networks, specific steps are such as
Under:
Step 1: near-infrared arm vein image preprocessing；
Step 2: designing end as training sample based on the pretreated image of step 1 and visible light arm image
Full convolutional neural networks vein to end images MODEL C Y-Net；The network is using visible light arm image as input, using five
Layer convolutional layer is filtered operation to image；
Step 3: to the parameter optimization strategy in CY-Net network model: using gradient optimization algorithm and cost
Function optimization algorithm；
Step 4: finally carrying out vein extraction to the vein imaging figure of network output.
Further, the adaptive histogram equalization method near-infrared arm of contrast-limited is utilized in the step one
Image is pre-processed.
Further, the step two specifically:
2.1, the convolutional neural networks that the network overall structure of design is one five layers are followed by one behind each convolutional layer
Layer PReLU activation primitive layer, common activation primitive Sigmoid and Tanh is saturation nonlinearity function.In recent years, most nerves
Network all uses unsaturated ReLU (Rectified Linear Unit, non-liner revision unit) or its deformation PReLU
The function of (Parametric Rectified Linear Unit) as active coating；Its mathematical expression difference is as follows:
F (x)=max (0, x) (1)
Wherein, x is function input, and f (x) is function output, and a is slope；
One layer of PReLU activation primitive layer is followed by behind each convolutional layer of present networks, increase network non-linear factor and
The learning ability of model, it, which increases a small amount of calculation amount than ReLU activation primitive, realizes higher accuracy rate, believes negative axis
Breath will not all be lost, and avoid " dying ReLU ".
2.2, Dropout layers are arranged after convolutional layer four, the characteristic pattern finally obtained with 3 × 3 convolution kernel to convolutional layer 5
It carries out convolution and obtains network output image, calculate the cost function of the near-infrared image of output image and input, passed using reversed
The gradient descent method optimization that iterates is broadcast to update network parameter and minimize cost function, make e-learning to visible images with
Mapping relations between near-infrared image；
Dropout layers be a kind of generalization ability for avoiding occurring in network training over-fitting and improving network plan
Slightly, network complexity is reduced by giving up the output of hidden layer neuron at random.The neuron being rejected both be not involved in front of to
Communication process is also not involved in back-propagation process, can reduce the coupling between neuron in this way, make network have more generalization ability,
It is more robust.When training data is less, it is easy to happen over-fitting, Dropout strategy can be used.
2.3, using pretreated image as supervision message, one five layers of full convolutional Neural net is trained end-to-endly
Network, direct prediction label figure；The target of end-to-end study is directly learnt by depth network to (defeated from the primitive form of data
Enter) to the mapping relations between the label (output) of data, reduce artificial pretreatment and post-processing and complicated based on artificial
The rule of design or the feature of selection increase the whole compatible degree of network.
It is specially input with visible images, corresponding near-infrared image is that target exports, trained one five layers complete
Convolutional neural networks；Network output is subjected to backpropagation as error at a distance from target output, is optimized and revised in network
The parameters such as weight, bias term, reduce error constantly, until network output is exported close to target.
Further, first and third layer of convolution kernel size is designed as 5, remaining is 3.Convolution operation usually has and makes
Useful information enhancing, the effect for reducing noise are practice have shown that lesser convolution kernel preferably extracts the feature in image.
Further, cost function optimization algorithm is specially the model for using L2 regularization to carry out constrained parameters in the step three
Number, i.e., with the difference of predicted value and actual value square half to being averaged after the summation of m sample come measure algorithm operating condition:
In formula, W is network weight, and b is network bias term, and m is number of samples,It is network to the mesh of i-th sample
Mark output, y(i)It is network to the reality output of i-th of sample,
Therefore the final goal of Solution To The Network is that the minimum value of cost function, solution procedure are asked for weight W and bias term b
Gaussian initialization is carried out to each parameter；
L2 regularization is substantially linear attenuation to be carried out to the weight of network, therefore L2 regularization is also referred to as weight decaying,
It is the cost function formula (3) in network below along with a regularization term, it may be assumed that
In formula, the subsequent Section 2 of plus sige is regularization term；
L2 regularization term be to all parameter W square summation after be averaged multiplied by λ/2, wherein λ is regularization coefficient,
For weighing specific gravity shared by original cost function J (W, b) and regular terms plus derivation again after regular terms, the more new formula of W
Become:
In formula, Section 2 is the partial derivative for solving parameter, and α indicates learning rate；
So the coefficient of W becomes the value smaller than 1 from 1 in newerIts effect is to reduce W to update weight
Influence, i.e., weight decay.
Further, gradient optimization algorithm carries out parameter update to weight and bias term in the step three, updates
Mode is as follows:
In formula,Indicate the weight parameter connecting between j-th of node of l layer and l+1 i-th of node of layer,
Indicate the bias term of l+1 i-th of node of layer, α indicates learning rate, can be seen that by two parameter more new formulas and solves parameter
Partial derivative is the key that gradient descent method；Cost function is to weightAnd bias termLocal derviation is asked to obtain:
Residual error is to calculate the output node of every layer network the difference of output valve and true value, it is assumed that the output layer of neural network
It is n-thlLayer, then the residual error of output layer is usedIt indicates, l=nl-1,nlThe residual error of i-th of node of -2, K, 2 layer is usedTable
Show, calculation formula is as follows:
In formula,It is got by the weighted average calculating of l+1 node layer residual error, after having two above residual error, then
Cost function J (W, b) can be further calculated to the local derviation of weight and bias term, it may be assumed that
Detailed description of the invention
Fig. 1 is near-infrared arm vein image preprocessing result figure of the present invention；
Fig. 2 is the pretreatment schematic diagram of arm near-infrared image of the present invention；
Fig. 3 is the schematic diagram of CY-Net network structure of the present invention；
Fig. 4 is the image of arm vein post processing of image process of the present invention；
Fig. 5 is to show in the embodiment of the present invention using visible images, based on what optical means and method of the invention obtained
As comparative result figure；
Fig. 6 is the local binarization image after vein of the present invention extracts；
Fig. 7 is network in the embodiment of the present invention to the effect picture of data difference fitting degree；
Fig. 8 is the preferable three groups of images of subjective assessment in the embodiment of the present invention and its result figure after binaryzation；
Fig. 9 is CY-Net model robustness verification result figure under shooting conditions different in the embodiment of the present invention.
Specific embodiment
It is clear to keep the purpose of the present invention, technical solution and effect clearer, referring to the drawings and illustrative example pair
The present invention is further described.It should be understood that specific implementation described herein is not used to limit only to explain the present invention
The fixed present invention.
1. near-infrared image pre-processes
Due to the specific location of near-infrared arm image medium sized vein and unintelligible, utilize the adaptive of contrast-limited
Histogram equalization method (Contrast Limited Adaptive Histogram Equalization, CLAHE) is answered to mention
The contrast of near-infrared image is risen, as shown in Figure 1.1 (a) is arm near-infrared image, and 1 (b) is the adaptive of contrast-limited
The result of histogram equalization method.
The data set of network is made of the synchronous arm image of visible light-near-infrared of 24 people, since sample total is less,
Therefore final to obtain 96 pairs of visible lights-near-infrared arm image using horizontal, vertical mirror method EDS extended data set, at random
Wherein 80% training set of the data as network is chosen, remaining 20% data is as test set.
2. full convolutional neural networks vein images MODEL C Y-Net end to end
The CY-Net network structure that the present invention designs is as shown in Figure 3: the network is adopted using visible light arm image as input
Operation, convolution kernel size and feature map number are filtered to image with five layers of convolutional layer as shown in figure 3, after each convolutional layer
It is followed by one layer of PReLU activation primitive layer, Dropout layers of setting inactivates neuron at random excessively quasi- to avoid network after convolutional layer 4
It closes, convolution finally is carried out to the characteristic pattern that convolutional layer 5 obtains with 3 × 3 convolution kernel and obtains network output image, calculates output figure
As the cost function with the near-infrared image of input, iterating to optimize using back-propagation gradient descent method updates network parameter
Cost function is minimized, makes the e-learning to the mapping relations between visible images and near-infrared image.
First and third layer of convolution kernel size is designed as 5, remaining is 3.Convolutional layer is the core layer in convolutional neural networks,
Convolutional filtering operation is carried out to each channel of input picture, extracts different features.The each convolutional layer of network of the invention
It is followed by one layer of PReLU activation primitive layer below, increases the non-linear factor of network and the learning ability of model, it swashs than ReLU
Function living, which increases a small amount of calculation amount, realizes higher accuracy rate, lose negative axis information will not all, avoids "
dying ReLU".It deforms the function of PReLU (Parametric Rectified Linear Unit) as active coating,
Mathematical expression difference is as follows:
F (x)=max (0, x) (1)
In formula, x is function input, and f (x) is function output, and a is slope.
Dropout layers: the coupling between neuron is reduced by giving up hidden layer neuron at random, to reduce network
Complexity makes network have more generalization ability, more robust.In order to avoid generation over-fitting, the generalization ability of network is improved,
Dropout strategy is used when network design.
Finally the full articulamentum with output connection realizes classification task for extracting high-level characteristic to traditional CNN network.This
Invention is based on the theory of FCN (Fully Convolutional Network), by full connection last in traditional CNN network
Layer has changed convolutional layer into, constitutes full convolutional network, obtains the mapping graph of target finally to realize the imaging of vein image.
The CNN network C Y-Net that the present invention designs is with CLAHE treated near-infrared arm image (Ground truth)
As supervision message, full convolutional neural networks of one five layers of training end-to-endly, direct prediction label figure (Label map),
As shown in Fig. 2, being input with visible images, corresponding near-infrared image is target output, the full convolution of one five layers of training
Neural network.By network output with as error progress backpropagation, optimized and revised at a distance from target output weight in network,
The parameters such as bias term, reduce error constantly, until network output is exported close to target.
3. Network Optimization Strategy
(1) cost function optimizes
The present invention is using L2 regularization come the norm of constrained parameters.Vein visualization role substantially belongs to recurrence task, because
The cost function of this network is defined as in recurrence task common Euclidean distance loss function, that is, uses the difference of predicted value and actual value
Square half to being averaged after the summation of a sample and carry out measure algorithm operating condition:
Therefore the final goal of Solution To The Network is that the minimum value of cost function is sought for weight W and bias term b.Solution procedure
Gaussian initialization is carried out to each parameter.
L2 regularization is substantially linear attenuation to be carried out to the weight of network, therefore L2 regularization is also referred to as weight decaying
(weight decay).It is the cost function formula (3) in network below along with a regularization term, it may be assumed that
L2 regularization term be to all parameter W square summation after be averaged multiplied by λ/2, wherein λ is regularization coefficient,
For weighing specific gravity shared by original cost function J (W, b) and regular terms.In addition derivation again after regular terms, the more new formula of W
Become:
So the coefficient of W becomes the value smaller than 1 from 1 in newerIts effect is to reduce W to update weight
Influence, i.e., weight decay.In a sense, smaller weight indicates that the complexity of network is lower, therefore, pass through constraint
The norm of parameter, regularization can mitigate over-fitting to a certain extent.
(2) gradient descent method
The present invention mainly uses Adam (Adaptive Moment Estimation) optimization algorithm.It is stochastic gradient
The extension form of descent algorithm (Stochastic Gradient Descent, SGD), algorithm fusion AdaGrad and
The advantages of RMSProp algorithm, is based on first moment mean value computation adaptability parameter learning rate, and makes full use of the second moment of gradient equal
Value.According to practical experience, the exponential damping parameter beta1 of single order moments estimation takes 0.9, the exponential damping parameter of second order moments estimation
Beta2 takes 0.999, epsilon parameter to take 10E-8.
Gradient optimization algorithm carries out parameter update to weight and bias term in step 3, and update mode is as follows:
In formula,Indicate the weight parameter connecting between j-th of node of l layer and l+1 i-th of node of layer,
Indicate the bias term of l+1 i-th of node of layer, α indicates learning rate, can be seen that by two parameter more new formulas and solves parameter
Partial derivative is the key that gradient descent method；Cost function is to weightAnd bias termLocal derviation is asked to obtain:
Residual error is to calculate the output node of every layer network the difference of output valve and true value, it is assumed that the output layer of neural network
It is n-thlLayer, then the residual error of output layer is usedIt indicates, l=nl-1,nlThe residual error of i-th of node of -2, K, 2 layer is usedTable
Show, calculation formula is as follows:
In formula,It is got by the weighted average calculating of l+1 node layer residual error, after having two above residual error, then
Cost function J (W, b) can be further calculated to the local derviation of weight and bias term, it may be assumed that
Ubuntu16.04, the Caffe deep learning frame of 64 bit manipulation systems, with NVIDIA are based in the present embodiment
TITAN X (Pascal), the GPU of 8G memory are experimental situation.Vein image is shot using JAI-AD080CL camera, the camera
The spectrum for obtaining visible light and near-infrared can be synchronized.By convolutional neural networks MODEL C Y-Net complete end to end proposed by the present invention
What is proposed in the prior art is compared based on optical developing method, to verify the validity of network.Two methods are obtained
To output image and former near-infrared arm image post-processed.
Firstly, carrying out Gabor filtering to obtained vein image, Gabor filter can automatically extract the quiet of output image
Then based on this arteries and veins information increases venous information using the energy diagram exported after arm shape exposure mask and Gabor filtering
By force, binaryzation is finally carried out to enhanced image using the method for adaptive threshold.To the process of arm NIR post processing of image
As shown in Figure 4.4 (a) be NIR image；4 (b) be Gabor filtering and enhanced result；4 (c) be the result after binaryzation.
It subjective assessment based on the method for the present invention and is analyzed as follows:
As shown in figure 5, Fig. 5 is that the result that the visible images of arm, optical means obtain and CY-Net are obtained respectively
As a result.The imaging of visible light vein image can be achieved in optical means and CY-Net as seen from Figure 5, wherein optical means needle
Each pixel is handled, there are obvious noises, and the method based on deep learning is end-to-end study, and noise is than optics side
Method wants small.Fig. 6 is the local binarization figure of the 9th group of result, further demonstrates the vein extracting method based on deep learning and makes an uproar
Sound is smaller.From the 5th, 7, the 8 group of result of Fig. 5 also it will be evident that the result obtained based on optical method is in dark
Region all pixels gray value is all very low, can not observe venous locations, and the method based on deep learning overcomes this and asks
Topic, embodies preferable robustness.
It objectively evaluating based on the method for the present invention and is analyzed as follows:
In order to further verify the validity of invention, it is objective to be carried out with four common counters in identification field to experimental result
Evaluation: accuracy rate (Accuracy), precision (Precision), also known as precision ratio, recall ratio (Recall) and FM estimate.It is fixed
Justice is as follows respectively:
By definition it is found that accuracy rate and precision ratio measured is accuracy that algorithm extracts vein, recall ratio is then paid close attention to
Be extract result comprehensive, FM, which estimates, carries out overall merit to algorithm performance in conjunction with precision ratio and recall ratio, to three kinds of sides
The bianry image that method post-processes, aforementioned four index value is bigger to illustrate that vein extracts result closer to true vein, effect
Better.Vein extraction optically is carried out with CY-Net respectively on 20 groups of visible light arm images, carries out post-processing it
Afterwards, four evaluation indexes for calculating every group of result, using near-infrared image corresponding with visible images as Calculation Estimation index
True value (Ground truth).It is the accuracy rate and precision ratio evaluation index value of two methods in table 1, is two kinds of sides in table 2
The recall ratio and FM measuring and evaluation index value of method.
Table 1 is Accuracy the and Precision evaluation index value of two methods
Table 2 is Recall the and FM evaluation index value of two methods
Data comparison in table it can be found that in 20 groups of test images, CY-Net have 70% Accuracy value and
Precision value is higher than optical means, and the average value on Accuracy, Precision and FM estimate is all than being based on optics
Method it is high, illustrate that CY-Net can reach effect more better than optical means based on Pixel-level.Network proposed by the present invention is tested
Having demonstrate,proved convolutional neural networks has the ability to the substantive characteristics of image that learns from low volume data.
Fitting degree based on the method for the present invention is analyzed as follows:
Poor fitting refers to that model does not capture the feature of data well, can not good fitting data；Over-fitting refers to
Model is fitted too thorough data, so that the feature of data noise is also learnt to arrive, will lead to can not in test
Data are identified well, and the generalization ability of model is poor.As shown in fig. 7, being from left to right trained poor fitting (a) respectively, preferably quasi-
The case where closing (b) and over-fitting (c).
Wherein Fig. 7 (b) is the visitor by the preferable three groups of results of subjective assessment in measurement same model, different the number of iterations
See what index obtained.Fig. 8 is the preferable three groups of images of subjective assessment, calculates four kinds of evaluation indexes, knot to Fig. 8 (a), (b), (c)
Fruit is shown in Table 3, each evaluation index of Comprehensive Correlation.It is considered that (c) fitting degree in Fig. 8 is preferable, as used in inventive network
Model.
Table 3 is four kinds of evaluation index values of tri- groups of results of Fig. 8
Training starts, and model is often poor fitting, also just there is the space of optimization just because of this, can be by constantly
The hyper parameters such as regularized learning algorithm rate and increase the number of iterations to make the feature of model more fully learning data, makes the expression of model
Ability is stronger.The reason of over-fitting occurs also has very much, usually will appear over-fitting in the case where data set is less, can
To expand data set, therefore the data set of script 4 times are expanded；Meanwhile the number of iterations is not The more the better, sometimes
Big the number of iterations model learning has arrived the details of overabundance of data, effect be not so good as instead the number of iterations it is relatively fewer when model,
Therefore, suitable the number of iterations is selected to can also be improved the expression quality of model；It, can also in order to which over-fitting is alleviated or avoided
The cost function and Dropout strategy that network is defined using regularization method, by changing network structure, reduction neuron
Coupling improve the generalization ability of model.
Robustness verifying based on the method for the present invention is as follows:
Since visible light-near-infrared arm image that network training is concentrated into pair is shot by camera of the same race, illumination condition,
The variation of the factors such as the receptance function of camera is little.Therefore the experiment of this part is mainly verified in different illumination conditions, is clapped
It takes the photograph under environment, and to the robustness of non-arm position network model.As shown in figure 9, CY-Net mould under Fig. 9 difference shooting condition
The verifying of type robustness.Shown in Fig. 10 is different physical feeling CY-Net model robustness verifyings；The left side of every a pair of image is
Visible images, the right are CY-Net vein imaging figure.
Have been verified that model proposed by the present invention has preferable Shandong to illumination variation in subjective assessment and analysis part
Stick.From Fig. 9 and Figure 10 can be seen that the model that the method based on deep learning trains learn automatically to visible light with it is close red
Complicated mapping relations between outer image, it is effective not only for the arm image of training sample, change in external condition or
All there is preferable robustness and generalization ability when image content change itself.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the principle of the present invention, several improvement can also be made, these improvement also should be regarded as of the invention
Protection scope.
Claims (6)
1. a kind of visible images vein developing method based on convolutional neural networks, which is characterized in that steps are as follows:
Step 1: near-infrared arm vein image preprocessing；
Step 2: being designed end-to-end based on the pretreated image of step 1 and visible light arm image as training sample
Full convolutional neural networks vein image MODEL C Y-Net；
Step 3: to the parameter optimization strategy in CY-Net network model: using gradient optimization algorithm and cost function
Optimization algorithm；
Step 4: finally carrying out vein extraction to the vein imaging figure of network output.
2. a kind of visible images vein developing method based on convolutional neural networks according to claim 1, feature
It is, is carried out in advance in the step one using the adaptive histogram equalization method near-infrared arm image of contrast-limited
Processing.
3. a kind of visible images vein developing method based on convolutional neural networks according to claim 1, feature
It is, the step two specifically:
2.1, the convolutional neural networks that the network overall structure of design is one five layers are followed by one layer behind each convolutional layer
PReLU activation primitive layer；Its mathematical expression difference is as follows:
F (x)=max (0, x) (1)
Wherein, x is function input, and f (x) is function output, and a is slope；
2.2, Dropout layers are arranged after convolutional layer four, finally the characteristic pattern that convolutional layer 5 obtains is carried out with 3 × 3 convolution kernel
Convolution obtains network output image, calculates the cost function of the near-infrared image of output image and input, uses backpropagation ladder
The optimization that iterates of degree descent method updates network parameter and minimizes cost function, makes e-learning to visible images and closely red
Mapping relations between outer image；
2.3, it is input with visible images, corresponding near-infrared image is target output, the full convolution mind of one five layers of training
Through network；Network output is subjected to backpropagation as error at a distance from target output, optimizes and revises the parameter in network,
Reduce error constantly, until network output is exported close to target.
4. a kind of visible images vein developing method based on convolutional neural networks according to claim 3, feature
It is, first and third layer of convolution kernel size is designed as 5, remaining is 3.
5. a kind of visible images vein developing method based on convolutional neural networks according to claim 1, feature
It is, cost function optimization algorithm is specially the norm for using L2 regularization to carry out constrained parameters in the step three, i.e., with pre-
The difference of measured value and actual value square half sum to m sample after be averaged and carry out measure algorithm operating condition:
In formula, W is network weight, and b is network bias term, and m is number of samples,It is defeated for target of the network to i-th of sample
Out, y(i)It is network to the reality output of i-th of sample,
Therefore the final goal of Solution To The Network is that the minimum value of cost function is sought for weight W and bias term b, and solution procedure is to every
One parameter carries out Gaussian initialization；
L2 regularization is substantially linear attenuation to be carried out to the weight of network, therefore L2 regularization is also referred to as weight decaying, it is
A regularization term is added below in the cost function formula (3) of network, it may be assumed that
In formula, the subsequent Section 2 of plus sige is regularization term；
L2 regularization term be to all parameter W square summation after be averaged multiplied by λ/2, wherein λ is regularization coefficient, is used to
Weigh specific gravity shared by original cost function J (W, b) and regular terms, in addition derivation again after regular terms, the more new formula of W becomes
Are as follows:
In formula, Section 2 is the partial derivative for solving parameter, and α indicates learning rate；
So the coefficient of W becomes the value smaller than 1 from 1 in newerIts effect is the shadow for reducing W and updating to weight
It rings, i.e., weight decays.
6. a kind of visible images vein developing method based on convolutional neural networks according to claim 1, feature
It is, gradient optimization algorithm carries out parameter update to weight and bias term in the step three, and update mode is as follows:
In formula,Indicate the weight parameter connecting between j-th of node of l layer and l+1 i-th of node of layer,Indicate the
The bias term of l+1 i-th of node of layer, α indicate learning rate, can be seen that the partial derivative for solving parameter by two parameter more new formulas
It is the key that gradient descent method；Cost function is to weightAnd bias termLocal derviation is asked to obtain:
Residual error is to calculate the output node of every layer network the difference of output valve and true value, it is assumed that the output layer of neural network is the
nlLayer, then the residual error of output layer is usedIt indicates, l=nl-1,nlThe residual error of i-th of node of -2, K, 2 layer is usedIt indicates,
Calculation formula is as follows:
In formula,It is got by the calculating of the weighted average of l+1 node layer residual error, it, then can be into after having two above residual error
One step calculates cost function J (W, b) to the local derviation of weight and bias term, it may be assumed that
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