# User Identity Recognition Based on Wireless Sensor Network and Internet Finance Development

Authors: Tianxin Hua; Lingling Zhang
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

With the rapid development of computer network technology, the concept of “Internet +” has become more and more popular in recent years. The combination of the Internet and finance has particularly attracted people’s attention, and the operating modes of many industries have also changed. Since the use of Internet technology can achieve data sharing and information exchange, the “Internet + Finance” model has broken the barriers of information asymmetry in the financial sector in the past and has made great contributions to China’s multiple improvements. The financial market is very important to China’s economic development. The identification of the ID function of the wireless sensor network is susceptible to interference and the identification accuracy is reduced. We propose an adaptive identification feature recognition algorithm based on an improved minimum gray tree. After calculating the similarity, the nearest neighbor matching algorithm is directly used to obtain the minimum matching cost corresponding to the wireless sensor network registration that is regarded as the recognized identity so as to realize the identity function adaptive recognition. In this regard, the simulation results show that the proposed algorithm has high recognition accuracy. With the pace of financial innovation, financial institutions have achieved rapid development on the basis of Internet service platforms. At the same time, as the core of preventing money laundering activities, financial institutions are also facing many issues in identifying “customers” in their work. This article analyzes the main content, implementation effects, and difficulty of customer identification in financial institutions and proposes relevant improvement plans.


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## Body

## 1. Introduction

First, the recognition based on the characteristics of the action requires high-precision recognition of the action. With the increasing popularity of wireless object networks and the continuous development of wearable sensors, the value of building sensor-based human behavior and identity recognition systems is getting higher and higher [1]. With the rapid development of information globalization in today’s society, the digitization and ambiguity of user ID caused by the Internet will lead to serious disclosure of personal information, which will undoubtedly bring new challenges to identification and technology. The security authentication methods currently in use include combined authentication of user names and passwords, authentication of special authentication objects, and so on [2]. These authentication methods are easy to be stolen or imitated due to excessive human factors in the authentication process and cannot achieve high-level and high-precision security prevention and control [3]. Due to the convenience of storage such as password authentication and identification technology, users often use passwords and other reasons are relatively simple. There are illegal uses of general username and password combinations, a Bluetooth database has been established, and general IDs have been collected on a large scale. The combination of authenticated username and password has a serious impact on personal privacy and information security [4]. Although it is difficult to avoid the danger due to objective factors, it can be eliminated by the authentication method using biometric properties. Because it is difficult to change the user’s biometric characteristics, it is more difficult to imitate, so the possibility of theft is very small. It is now widely used in government, military, banks, welfare companies, social security bureaus, e-commerce, and other fields [5]. The Internet finance model has been favored and valued by more and more users and enterprises for its advantages of high efficiency, low threshold, convenience, and low cost [6]. It is booming. Various Internet finance platforms are growing exponentially, giving China’s financial industry a boost [7]. Development has brought new opportunities. However, the development of new things often has two sides, and the rapid development of Internet financial platforms has led to a saturation trend in the market. Therefore, in order to seize the opportunity in the fiercely competitive Internet financial market, major platforms often invest heavily in various lucrative activities to attract users. Summarizing the previous literature discussion and empirical research, we propose specific targeted antifraud prevention and control countermeasures for Internet financial platform users from the three directions of improving the level of Internet financial supervision, using financial technology to combat fraud, and building industry moral order [8]. Internet platforms provide prevention and control suggestions to reduce company losses and provide more ideas and methods for Internet financial platforms to combat fraud [9].


## 2. Related Works

According to the literature, the identification of mobile phone users based on biometrics as well as the identification of the use of passwords and patterns is not appropriate [10]. Therefore, the focus of the research is to be able to simply access and recognize the mobile phone sensor data. In order to solve the problem of low stability of dynamic matching recognition, the literature proposes a combination of the D-S evidence theory and mobile phone 3D gesture sensor data [11]. Related researchers have proposed a method called OpenSame to collect data from 200 experimenters in a total of 389,373 tuples [12]. In order to perform implicit authentication of mobile phone users in a sustainable manner without interrupting the normal use of mobile phones, Lee et al. proposed a system based on multiple sensors [13]. Experimental results show that this method is more efficient, the training time is less than 10 seconds, and the verification time is less than 20 seconds. The literature proposes that a platform that provides loans to applicants through the Internet is called an online lending platform [14]. The literature proposes that traditional financial services rely on Internet technology to operate, which can be understood as “finance + Internet”.


## 3. Research and Implementation of User Identity Recognition System in Wireless Sensor Network

### 3.1. Biometric Technology for Wireless Sensor Network Users

With the rapid development of information globalization in today’s society, electronic equipment has become an indispensable part of scientific research and daily life. These devices provide many convenient functions through network interconnection, but the digitization and invisibility of user ID caused by network information is a serious privacy leakage problem [15]. As a result, the requirements for ID recognition and verification technology have become higher [16]. Among them, the more commonly used methods are security authentication methods including user name and password combined authentication, user-specific object authentication, PIN code authentication, and so on. These authentication methods are easy to be stolen or imitated due to excessive human participation factors and cannot achieve a high degree of security prevention and control. It is very difficult to avoid the dangers brought about by these objective factors [17]. At the same time, some studies have shown that the user’s biometric function is difficult to change, imitation and tampering are not easy, fingerprint recognition, facial recognition, iris recognition, movement feature recognition, etc., are very unlikely to be stolen. Figure 1 shows the application process of high-end biometric technology that combines voice and skull fingerprints.Figure 1
High-end biometric technology using a combination of voice and skull fingerprints.Traditional identification methods usually use identification items (striped codes, QR codes, magnetic cards, keys, etc.) and identification knowledge (passwords, etc.). Because the detection is dependent on foreign objects, if the identification items and identification knowledge are stolen or forgotten, these identities can be easily deceived or replaced by others. Because the biological characteristics are not easy to forget, the anticounterfeiting performance is good, and it is not easy to be tampered with and theft. It can be used at any time by taking advantage of the “portability.” The characteristics of the human body are inherently unique to biology. Compared with traditional methods, biometric technology has improved security, privacy, and convenience because keys cannot be copied, stolen, or forgotten. At present, products related to biometric technology are realized through the latest computing technology, combined with the security surveillance system, to realize automatic ID identification and personal information management. According to the current statistical results of the International Biometric Group (IBG), the existing biometric technology is mainly divided into two applications: physiological characteristics and action characteristics, most of which are identification applications that use the characteristics of human physiology. For example, fingerprint recognition, facial recognition, and iris recognition as well as biometric technology based on user behavior characteristics, have multiple applications such as handwriting recognition, voice recognition, gesture recognition, and walking recognition. The indexes of these technologies are shown in Table1. The biometric method based on action characteristics has the highest safety level and accuracy at the same equipment cost level, high convenience and stability, and low machine cost requirements. Therefore, it has become a new trend in the research field to use the user’s behavior characteristics to authenticate the ID of the biometric technology and solve the traditional identification method in the process of vulnerability authentication.Table 1
Comparison of various biometric technology indicators.
Biometric technologyConvenienceAccuracySecurity levelStabilityIdentify equipment costFingerprint recognitionHigherHighMediumHigherMediumFace recognitionExtremely highHighHighHigherMediumSpeech recognitionHighMediumHigherMediumLowerBehavior feature recognitionHigherHigherHigherHigherMedium


### 3.2. Visual Sensor Network User Identification Algorithm

#### 3.2.1. Self-Adaptive Recognition Algorithm for Identity Features

The target area environment is defined as a rectangle, and the grid lines are divided intom rows and n columns. According to the corresponding matrix area detection requirements, the corresponding area detection matrix refers to the expansion matrix of all sensors. Define the corresponding restriction matrix suitable for the deployment position of the sensor. By analyzing the number and depth of the recognizable features of the face, the correlation level between the faces can be determined. In this paper, the pixel statistics iterative method is used, and the expression (1) is used to obtain the recognizable average offset vector of the face.(1)Skx=∑i=1qExi−x/ksxi∑i=1qExi−x/ksxi−x.After the average offset feature vector of the face is extracted, in order to further improve the effect of identity recognition in the visual sensor network, it is necessary to perform gray-scale processing on the obtained image and apply it to the adaptive recognition of identity features. The specific steps are as follows:(2)gx,y=Tfx,yorD′=TD.Assuming that the gray histogram distribution of the recognized original image is described byP(x), then (2) can be substituted into P(x) and the gray of the original image can be converted using(3)y=gx,y=1c∫x0xPxdx.The original image after gray-scale conversion was assumed to has a gray level ofL and a resolution of M×N. The gray level constant c calculated is as follows:.(4)c=M×NL−1.By substituting (4) into (3) and using (5) to obtain the histogram equalization function, the gray-scale distribution of the image can be made uniform, and the histogram equalization process can be realized.(5)y=INT1c∑x0xPx=INTL−1∑x0xPxM×N.First, a cost function for each gray-level pair corresponding to two images was defined, and the degree of consistency between the gray level at the positiony and the gray level at the same gray level was described. Formula (6) is used to explain that the function is expressed as(6)Cpy=1−α⋅mindiuty,τ1+α⋅mindFany,τ2,dinty=ξ⋅∑δ∈ΩIy+δ−I¯y−I′y+δ−I′¯y2×Cpy∑δ∈ΩIy+δ−I¯y2+∑δ∈ΩI′y+δ−I′¯y2,dsray=∇I′y−∇Iy×Cpy.In this paper,L(x, y) is used to represent the distance between two nodes (pixels) on the minimum gray tree (x and y), that is, the sum of the weights of the edges connected to the pixels. When the distance between two nodes in the minimum gray level difference tree is very close, the similarity of the corresponding two pixels will increase. On the contrary, if the distance between two nodes of the minimum gray level tree is very far, the similarity of the corresponding two pixels is very small. According to the abovementioned principle, formula (7) is used to calculate the similarity between two pixels.(7)Sx,y=Sy,x=exp−Lx,y.Regarding the structure of the minimum gray difference tree, the cost function previously defined for each node (pixel) is expanded to a cost function that converges to the entire treeT(x) represented by(8)CTx=∑v∈FxSx,vCqv.Similarly, this article usesCST(x) to describe the total convergence cost in the subtree ST(x) with pixel x as the root node. At this time, the parent node Q(x) of the node is as follows:(9)CSTx=∑v∈STxSx,vCqv=Cqx+∑v′∈STxSx,v′Cqv″=Cqx+∑v′∈STxSx,v′Sv′,v″Cqv″=Cqx+∑Qv′=xSx,v′∑v′∈STv′Sv′,v″Cqv″=Cqx+∑Qv′=xSx,v′CSTv′.The parent node of the subtree is used to perform line-by-line design for each layer, and formula (10) is used to establish the minimum gray-scale tree model.(10)CSTx=Cqx+∑Qv′=xSx,v′CSTv′.According to the matching cost calculation, the matching cost of the entire recognized image (size H•V) is obtained by adding up and normalizing at the same time, and finally, the matching cost (MC) of the image and the similarity of the two images are obtained.(11)MCI,I′=∑h∑wCTxh⋅w.


#### 3.2.2. Support Vector Machine

The basic idea of a support vector machine (SVM) is to divide the data correctly. Therefore, important parameters such as the penalty coefficient C and the kernel widthγ of the kernel function must be set to appropriate values before applying actual problems. The penalty factor C plays an important role in minimizing fitting errors and balancing the complexity of the model. In the traditional method, these parameters are processed according to the grid search and the fastest descent method. However, these methods can be simply classified as local optimal solutions. In recent years, several meta-heuristic search algorithms based on Biological Hughstick have appeared. It is easier to find the best global solution than the traditional methods mentioned above. Based on the above analysis, an improved gray wolf optimization algorithm is proposed to optimize the selection of SVM model parameters. This method is suitable for the problem of crop lack of diagnosis.In the sample space, the hyperplane of the sample can be divided by a linear equation.(12)wTx+b=0.The support vector is constrained to be correct. That is, the support vector satisfies the following conditions.(13)yiwTxi+b−1=0.Corresponding to the function interval, the geometric interval has better properties. The geometric interval of the sample points is defined as(14)γi=yiwTwxi+bw.The geometric interval of the entire training sample set is defined as the minimum value of the geometric interval of all sample points.(15)γ=mini=1,…,Nγi.This is regarded as the original optimization problem, the Lagrangian pairing is applied, and the Lagrangian multiplier is added under the constraints of the equation. Therefore, the Lagrangian function of this problem can be expressed as follows:(16)Lw,b,α=12w2−∑i=1nαiyiwTxi+b−1.When all constraints are met, there are(17)θw=12w2.In other words, Equation (17) constitutes the basic form of SVM. Therefore, if satisfying all constraints, θ(w) is the same as direct minimization, and the objective function can be expressed as follows:(18)minw,bθw=minw,bmaxαi≥0Lw,b,α=p∗Swap the positions of the minimum and maximum functions to obtain the following information:(19)maxαi≥0minw,bLw,b,α=d∗.The conditions of the Lagrangian multiplier method was satisfied, and the partial derivative function of each component was taken, when the value is 0:(20)∂L∂w=0⇒w=∑i=1nαiyixi,∂L∂b=0⇒∑i=1nαiyi=0.As shown in (21), the parameters w and b were deleted, and only the problem was converted into a variable.(21)maxw,b,αLw,b,α=∑i=1nαi−12∑i=1n∑j=1nαiαjyiyjxixj.


### 3.3. Wireless Sensor Network Data Collection and Processing

#### 3.3.1. Collection of Geometric Features of Human Faces

The core function of the system is the display of sensor information, and it is necessary to design a table for initial storage of data. SQLSERVER was choosen as a stable and reliable high-performance database management system because it has a large-capacity data storage system, a variety of data, and long-term storage time period characteristics. The design of the data table must be reasonable and comprehensive, the image of each person in the neutral facial image was registered to the visual sensor network, and the other three representation variables and three illumination variables were used as the detection image set. The collected functions are shown in Table2.Table 2
Geometric features of standard human faces.
FeatureGeometric characteristicsMeasurementsF1Inside and outside eye point width/cm3.9F2Distance between inner eye points/cm4.2F3Outer eye point interval/cm10.3F4Height of nose/cm1.4F5Nose tip angle of left and right wings116°F6Eye point and nose angle in left and right32°F7Nose volume/cm37.9After obtaining the correct geometric features of the face, it is necessary to collect the geometric features of various facial expressions. The detailed expression conversion is shown in Table3.Table 3
Acquisition of geometric characteristics of the 3D face model.
FeatureSmileFrustratedPoutingBulging△F1/cm0.91.50.20.2△F2/cm0.51.80.30.3△F3/cm3.52.60.40.3△F4/cm0.60.300△F56°5°00△F64°10°2°4°△F725°8°6°5°△F845°26°25°25°△F9/cm342.600Geometric similarity0.580.450.600.55Relevance similarity0.640.710.630.59


#### 3.3.2. Some Features Used in Gait Recognition

Mark Nixon, a professor of electrical engineering and computer science at the University of Southampton in the United Kingdom, found that the way people walk is fundamentally different. There are subtle aspects in muscle strength, flexibility of tendons, length and density of bones, vision, and damage to muscles and bones. Difference: the walking process of a normal adult is based on the regular movement of taking the left foot one step forward and the right foot one step forward, which is called the complete walking cycle. In this chapter, it is divided into several short periods according to the adjacent minimum value on theY axis of the acceleration sensor, so that it can easily determine the walking period corresponding to the data. According to statistics, the average time of a walking cycle is 1 to 1.5 seconds. These are based on complete walking time. The main characteristics considered are shown in Table 4.Table 4
Some features used in gait recognition.
FeatureDescriptionRoot mean squareThe sum of squares in the three directions ofX, Y, and Z is divided by 3, and the square is takenEnergyWork done in the three directions ofX, Y, and ZPercentile valueThe percentage of the number of samples below this sample value in the dataset to the total number of samplesMean absolute deviationThe average of the absolute value of the deviation of each value from its arithmetic meanCadenceThe number of complete gait cycles contained in 1 minute


#### 3.3.3. Synchronization

In theory, when the sampling frequency of different sensors is set to the same, the data needs to be collected at the same time. In other words, the time stamps of the sampling points are exactly the same. However, in actual situations, even if the same smartphone is set to the same sampling frequency, the returned sensor data will have a different sampling time. Therefore, in order to avoid the time difference between the same frame of data intercepted according to the length of different sensors, it is necessary to synchronize the original sensor data. This process is shown in Figure2.Figure 2
Flow chart of sensor data synchronization process.


### 3.4. Simulation Experiment Results and Analysis

#### 3.4.1. Evaluation Index

According to the definition of the object problem, the user identification of the smartphone belongs to multiple classification problems. The model classification performance evaluation index proposed in this paper uses accuracy, F1 value, ROC curve, and AUC. The calculation formulas for the matching rate and reproduction rate of thek category are as follows:(22)Pk=TPkTPk+FPk,Rk=TPkTPk+FNk.Accuracy is the most common evaluation index for classifiers. This is the ratio of the number of samples that are correctly identified (that is, correctly classified) for all sample sizes. For example, there are 1,000 students in junior high school. Obviously, there are two categories of students, boys and girls. Among them, 600 are boys and 400 are girls. If the classifier correctly determines the gender of 800 people (including 500 boys and 300 girls), then, the number of people correctly classified by the classifier will account for 80% of the total. In other words, the accuracy is 80% (800/1000). This paper shows the calculation method in following formula:(23)accuracy=1nsamples∑i=0nsampls−1fy^i=yi.TheF value is the weighted harmonic average of precision and precision. For multiple classification problems, the evaluation indicators of micro-F score and macro-F score are usually used. Because the coincidence rate and the reproduction rate may be mutually exclusive, it is necessary to consider these two issues together. In order to solve this problem, the F value is proposed, and the calculation formula is as follows:(24)Fβ=1+β2×P×Rβ2×P+R.


#### 3.4.2. Detailed Comparison of Recognition Performance

In order to further prove the effectiveness of the algorithm in this paper, on the basis of the abovementioned experiment, the performance of the two algorithms is statistically compared, and the results obtained are shown in Table5.Table 5
Detailed comparison of the performance of the two algorithms.
Variable typeThe recognition accuracy of the algorithm in this paper (%)Recognition accuracy based on palmprint and facial feature algorithm (%)Happy10084Sad9776Surprised9662Light source 19876Light source 29669Light source 39063


#### 3.4.3. Division Ratio Results

Figure3 shows the experimental results of the ShakeLogin dataset under experimental conditions. For the model SSUI proposed in this paper, when the division ratio is 0.8, the accuracy rate reaches the maximum, and then, when the division ratio is increased to 0.9, the accuracy rate drops slightly.Figure 3
Performance graph of the ShakeLogin dataset with different division ratios.The specific experimental results are shown in Figure4. It can be seen from the broken line chart that as the proportion increases, the accuracy of walking and going up and down stairs increases, and the accuracy of going up and down stairs increases by more than 20%. The other three actions have changed, but when the ratio reaches 0.9, and all actions have reached the maximum accuracy.Figure 4
HHAR dataset (all behaviors).


#### 3.4.4. Results of All Behavioral Experiments

The results of this series of experiments are shown in Table6. The time-domain feature weighting parameter in this table changes from 1 to 2/3, 1/2, 1/3, and finally to 0. The frequency domain weighting parameter value ranges from 0 to 1/3, 1/2, 2/3, and finally to 1.Table 6
User identification results of ShakeLogin and HHAR datasets.
ShakeLogin (17 users, shake the phone arbitrarily)Random 80% of the entire data set/remaining85.4687.2592.0781.9875.33HHAR (9 users, 6 behaviors)All actions/all actions92.5697.1292.5697.2394.88All behaviors/walking90.0896.8797.7596.85100.0All actions/stations90.3797.7895.8498.0572.42All behaviors/sit86.1890.5592.6793.8366.05All behaviors/biking90.9285.3291.6288.8386.37All actions/stairs92.7596.9391.3595.1697.59All actions/down stairs88.7393.1895.5994.3997.56Figure5 shows the performance of the SSI model in the Shakelogin dataset and the experimental results of the sample period. As shown in the figure, the sample period is too short (2 seconds), the accuracy is very low, and the obtainable value is less than 60%. The longer the sample time is, the higher the correct rate (2 seconds to 4 seconds, 60% to close to 80%) is.Figure 5
SSUI’s performance in the ShakeLogin dataset varies with sample duration.


## 3.1. Biometric Technology for Wireless Sensor Network Users

With the rapid development of information globalization in today’s society, electronic equipment has become an indispensable part of scientific research and daily life. These devices provide many convenient functions through network interconnection, but the digitization and invisibility of user ID caused by network information is a serious privacy leakage problem [15]. As a result, the requirements for ID recognition and verification technology have become higher [16]. Among them, the more commonly used methods are security authentication methods including user name and password combined authentication, user-specific object authentication, PIN code authentication, and so on. These authentication methods are easy to be stolen or imitated due to excessive human participation factors and cannot achieve a high degree of security prevention and control. It is very difficult to avoid the dangers brought about by these objective factors [17]. At the same time, some studies have shown that the user’s biometric function is difficult to change, imitation and tampering are not easy, fingerprint recognition, facial recognition, iris recognition, movement feature recognition, etc., are very unlikely to be stolen. Figure 1 shows the application process of high-end biometric technology that combines voice and skull fingerprints.Figure 1
High-end biometric technology using a combination of voice and skull fingerprints.Traditional identification methods usually use identification items (striped codes, QR codes, magnetic cards, keys, etc.) and identification knowledge (passwords, etc.). Because the detection is dependent on foreign objects, if the identification items and identification knowledge are stolen or forgotten, these identities can be easily deceived or replaced by others. Because the biological characteristics are not easy to forget, the anticounterfeiting performance is good, and it is not easy to be tampered with and theft. It can be used at any time by taking advantage of the “portability.” The characteristics of the human body are inherently unique to biology. Compared with traditional methods, biometric technology has improved security, privacy, and convenience because keys cannot be copied, stolen, or forgotten. At present, products related to biometric technology are realized through the latest computing technology, combined with the security surveillance system, to realize automatic ID identification and personal information management. According to the current statistical results of the International Biometric Group (IBG), the existing biometric technology is mainly divided into two applications: physiological characteristics and action characteristics, most of which are identification applications that use the characteristics of human physiology. For example, fingerprint recognition, facial recognition, and iris recognition as well as biometric technology based on user behavior characteristics, have multiple applications such as handwriting recognition, voice recognition, gesture recognition, and walking recognition. The indexes of these technologies are shown in Table1. The biometric method based on action characteristics has the highest safety level and accuracy at the same equipment cost level, high convenience and stability, and low machine cost requirements. Therefore, it has become a new trend in the research field to use the user’s behavior characteristics to authenticate the ID of the biometric technology and solve the traditional identification method in the process of vulnerability authentication.Table 1
Comparison of various biometric technology indicators.
Biometric technologyConvenienceAccuracySecurity levelStabilityIdentify equipment costFingerprint recognitionHigherHighMediumHigherMediumFace recognitionExtremely highHighHighHigherMediumSpeech recognitionHighMediumHigherMediumLowerBehavior feature recognitionHigherHigherHigherHigherMedium


## 3.2. Visual Sensor Network User Identification Algorithm

### 3.2.1. Self-Adaptive Recognition Algorithm for Identity Features

The target area environment is defined as a rectangle, and the grid lines are divided intom rows and n columns. According to the corresponding matrix area detection requirements, the corresponding area detection matrix refers to the expansion matrix of all sensors. Define the corresponding restriction matrix suitable for the deployment position of the sensor. By analyzing the number and depth of the recognizable features of the face, the correlation level between the faces can be determined. In this paper, the pixel statistics iterative method is used, and the expression (1) is used to obtain the recognizable average offset vector of the face.(1)Skx=∑i=1qExi−x/ksxi∑i=1qExi−x/ksxi−x.After the average offset feature vector of the face is extracted, in order to further improve the effect of identity recognition in the visual sensor network, it is necessary to perform gray-scale processing on the obtained image and apply it to the adaptive recognition of identity features. The specific steps are as follows:(2)gx,y=Tfx,yorD′=TD.Assuming that the gray histogram distribution of the recognized original image is described byP(x), then (2) can be substituted into P(x) and the gray of the original image can be converted using(3)y=gx,y=1c∫x0xPxdx.The original image after gray-scale conversion was assumed to has a gray level ofL and a resolution of M×N. The gray level constant c calculated is as follows:.(4)c=M×NL−1.By substituting (4) into (3) and using (5) to obtain the histogram equalization function, the gray-scale distribution of the image can be made uniform, and the histogram equalization process can be realized.(5)y=INT1c∑x0xPx=INTL−1∑x0xPxM×N.First, a cost function for each gray-level pair corresponding to two images was defined, and the degree of consistency between the gray level at the positiony and the gray level at the same gray level was described. Formula (6) is used to explain that the function is expressed as(6)Cpy=1−α⋅mindiuty,τ1+α⋅mindFany,τ2,dinty=ξ⋅∑δ∈ΩIy+δ−I¯y−I′y+δ−I′¯y2×Cpy∑δ∈ΩIy+δ−I¯y2+∑δ∈ΩI′y+δ−I′¯y2,dsray=∇I′y−∇Iy×Cpy.In this paper,L(x, y) is used to represent the distance between two nodes (pixels) on the minimum gray tree (x and y), that is, the sum of the weights of the edges connected to the pixels. When the distance between two nodes in the minimum gray level difference tree is very close, the similarity of the corresponding two pixels will increase. On the contrary, if the distance between two nodes of the minimum gray level tree is very far, the similarity of the corresponding two pixels is very small. According to the abovementioned principle, formula (7) is used to calculate the similarity between two pixels.(7)Sx,y=Sy,x=exp−Lx,y.Regarding the structure of the minimum gray difference tree, the cost function previously defined for each node (pixel) is expanded to a cost function that converges to the entire treeT(x) represented by(8)CTx=∑v∈FxSx,vCqv.Similarly, this article usesCST(x) to describe the total convergence cost in the subtree ST(x) with pixel x as the root node. At this time, the parent node Q(x) of the node is as follows:(9)CSTx=∑v∈STxSx,vCqv=Cqx+∑v′∈STxSx,v′Cqv″=Cqx+∑v′∈STxSx,v′Sv′,v″Cqv″=Cqx+∑Qv′=xSx,v′∑v′∈STv′Sv′,v″Cqv″=Cqx+∑Qv′=xSx,v′CSTv′.The parent node of the subtree is used to perform line-by-line design for each layer, and formula (10) is used to establish the minimum gray-scale tree model.(10)CSTx=Cqx+∑Qv′=xSx,v′CSTv′.According to the matching cost calculation, the matching cost of the entire recognized image (size H•V) is obtained by adding up and normalizing at the same time, and finally, the matching cost (MC) of the image and the similarity of the two images are obtained.(11)MCI,I′=∑h∑wCTxh⋅w.


### 3.2.2. Support Vector Machine

The basic idea of a support vector machine (SVM) is to divide the data correctly. Therefore, important parameters such as the penalty coefficient C and the kernel widthγ of the kernel function must be set to appropriate values before applying actual problems. The penalty factor C plays an important role in minimizing fitting errors and balancing the complexity of the model. In the traditional method, these parameters are processed according to the grid search and the fastest descent method. However, these methods can be simply classified as local optimal solutions. In recent years, several meta-heuristic search algorithms based on Biological Hughstick have appeared. It is easier to find the best global solution than the traditional methods mentioned above. Based on the above analysis, an improved gray wolf optimization algorithm is proposed to optimize the selection of SVM model parameters. This method is suitable for the problem of crop lack of diagnosis.In the sample space, the hyperplane of the sample can be divided by a linear equation.(12)wTx+b=0.The support vector is constrained to be correct. That is, the support vector satisfies the following conditions.(13)yiwTxi+b−1=0.Corresponding to the function interval, the geometric interval has better properties. The geometric interval of the sample points is defined as(14)γi=yiwTwxi+bw.The geometric interval of the entire training sample set is defined as the minimum value of the geometric interval of all sample points.(15)γ=mini=1,…,Nγi.This is regarded as the original optimization problem, the Lagrangian pairing is applied, and the Lagrangian multiplier is added under the constraints of the equation. Therefore, the Lagrangian function of this problem can be expressed as follows:(16)Lw,b,α=12w2−∑i=1nαiyiwTxi+b−1.When all constraints are met, there are(17)θw=12w2.In other words, Equation (17) constitutes the basic form of SVM. Therefore, if satisfying all constraints, θ(w) is the same as direct minimization, and the objective function can be expressed as follows:(18)minw,bθw=minw,bmaxαi≥0Lw,b,α=p∗Swap the positions of the minimum and maximum functions to obtain the following information:(19)maxαi≥0minw,bLw,b,α=d∗.The conditions of the Lagrangian multiplier method was satisfied, and the partial derivative function of each component was taken, when the value is 0:(20)∂L∂w=0⇒w=∑i=1nαiyixi,∂L∂b=0⇒∑i=1nαiyi=0.As shown in (21), the parameters w and b were deleted, and only the problem was converted into a variable.(21)maxw,b,αLw,b,α=∑i=1nαi−12∑i=1n∑j=1nαiαjyiyjxixj.


## 3.2.1. Self-Adaptive Recognition Algorithm for Identity Features

The target area environment is defined as a rectangle, and the grid lines are divided intom rows and n columns. According to the corresponding matrix area detection requirements, the corresponding area detection matrix refers to the expansion matrix of all sensors. Define the corresponding restriction matrix suitable for the deployment position of the sensor. By analyzing the number and depth of the recognizable features of the face, the correlation level between the faces can be determined. In this paper, the pixel statistics iterative method is used, and the expression (1) is used to obtain the recognizable average offset vector of the face.(1)Skx=∑i=1qExi−x/ksxi∑i=1qExi−x/ksxi−x.After the average offset feature vector of the face is extracted, in order to further improve the effect of identity recognition in the visual sensor network, it is necessary to perform gray-scale processing on the obtained image and apply it to the adaptive recognition of identity features. The specific steps are as follows:(2)gx,y=Tfx,yorD′=TD.Assuming that the gray histogram distribution of the recognized original image is described byP(x), then (2) can be substituted into P(x) and the gray of the original image can be converted using(3)y=gx,y=1c∫x0xPxdx.The original image after gray-scale conversion was assumed to has a gray level ofL and a resolution of M×N. The gray level constant c calculated is as follows:.(4)c=M×NL−1.By substituting (4) into (3) and using (5) to obtain the histogram equalization function, the gray-scale distribution of the image can be made uniform, and the histogram equalization process can be realized.(5)y=INT1c∑x0xPx=INTL−1∑x0xPxM×N.First, a cost function for each gray-level pair corresponding to two images was defined, and the degree of consistency between the gray level at the positiony and the gray level at the same gray level was described. Formula (6) is used to explain that the function is expressed as(6)Cpy=1−α⋅mindiuty,τ1+α⋅mindFany,τ2,dinty=ξ⋅∑δ∈ΩIy+δ−I¯y−I′y+δ−I′¯y2×Cpy∑δ∈ΩIy+δ−I¯y2+∑δ∈ΩI′y+δ−I′¯y2,dsray=∇I′y−∇Iy×Cpy.In this paper,L(x, y) is used to represent the distance between two nodes (pixels) on the minimum gray tree (x and y), that is, the sum of the weights of the edges connected to the pixels. When the distance between two nodes in the minimum gray level difference tree is very close, the similarity of the corresponding two pixels will increase. On the contrary, if the distance between two nodes of the minimum gray level tree is very far, the similarity of the corresponding two pixels is very small. According to the abovementioned principle, formula (7) is used to calculate the similarity between two pixels.(7)Sx,y=Sy,x=exp−Lx,y.Regarding the structure of the minimum gray difference tree, the cost function previously defined for each node (pixel) is expanded to a cost function that converges to the entire treeT(x) represented by(8)CTx=∑v∈FxSx,vCqv.Similarly, this article usesCST(x) to describe the total convergence cost in the subtree ST(x) with pixel x as the root node. At this time, the parent node Q(x) of the node is as follows:(9)CSTx=∑v∈STxSx,vCqv=Cqx+∑v′∈STxSx,v′Cqv″=Cqx+∑v′∈STxSx,v′Sv′,v″Cqv″=Cqx+∑Qv′=xSx,v′∑v′∈STv′Sv′,v″Cqv″=Cqx+∑Qv′=xSx,v′CSTv′.The parent node of the subtree is used to perform line-by-line design for each layer, and formula (10) is used to establish the minimum gray-scale tree model.(10)CSTx=Cqx+∑Qv′=xSx,v′CSTv′.According to the matching cost calculation, the matching cost of the entire recognized image (size H•V) is obtained by adding up and normalizing at the same time, and finally, the matching cost (MC) of the image and the similarity of the two images are obtained.(11)MCI,I′=∑h∑wCTxh⋅w.


## 3.2.2. Support Vector Machine

The basic idea of a support vector machine (SVM) is to divide the data correctly. Therefore, important parameters such as the penalty coefficient C and the kernel widthγ of the kernel function must be set to appropriate values before applying actual problems. The penalty factor C plays an important role in minimizing fitting errors and balancing the complexity of the model. In the traditional method, these parameters are processed according to the grid search and the fastest descent method. However, these methods can be simply classified as local optimal solutions. In recent years, several meta-heuristic search algorithms based on Biological Hughstick have appeared. It is easier to find the best global solution than the traditional methods mentioned above. Based on the above analysis, an improved gray wolf optimization algorithm is proposed to optimize the selection of SVM model parameters. This method is suitable for the problem of crop lack of diagnosis.In the sample space, the hyperplane of the sample can be divided by a linear equation.(12)wTx+b=0.The support vector is constrained to be correct. That is, the support vector satisfies the following conditions.(13)yiwTxi+b−1=0.Corresponding to the function interval, the geometric interval has better properties. The geometric interval of the sample points is defined as(14)γi=yiwTwxi+bw.The geometric interval of the entire training sample set is defined as the minimum value of the geometric interval of all sample points.(15)γ=mini=1,…,Nγi.This is regarded as the original optimization problem, the Lagrangian pairing is applied, and the Lagrangian multiplier is added under the constraints of the equation. Therefore, the Lagrangian function of this problem can be expressed as follows:(16)Lw,b,α=12w2−∑i=1nαiyiwTxi+b−1.When all constraints are met, there are(17)θw=12w2.In other words, Equation (17) constitutes the basic form of SVM. Therefore, if satisfying all constraints, θ(w) is the same as direct minimization, and the objective function can be expressed as follows:(18)minw,bθw=minw,bmaxαi≥0Lw,b,α=p∗Swap the positions of the minimum and maximum functions to obtain the following information:(19)maxαi≥0minw,bLw,b,α=d∗.The conditions of the Lagrangian multiplier method was satisfied, and the partial derivative function of each component was taken, when the value is 0:(20)∂L∂w=0⇒w=∑i=1nαiyixi,∂L∂b=0⇒∑i=1nαiyi=0.As shown in (21), the parameters w and b were deleted, and only the problem was converted into a variable.(21)maxw,b,αLw,b,α=∑i=1nαi−12∑i=1n∑j=1nαiαjyiyjxixj.


## 3.3. Wireless Sensor Network Data Collection and Processing

### 3.3.1. Collection of Geometric Features of Human Faces

The core function of the system is the display of sensor information, and it is necessary to design a table for initial storage of data. SQLSERVER was choosen as a stable and reliable high-performance database management system because it has a large-capacity data storage system, a variety of data, and long-term storage time period characteristics. The design of the data table must be reasonable and comprehensive, the image of each person in the neutral facial image was registered to the visual sensor network, and the other three representation variables and three illumination variables were used as the detection image set. The collected functions are shown in Table2.Table 2
Geometric features of standard human faces.
FeatureGeometric characteristicsMeasurementsF1Inside and outside eye point width/cm3.9F2Distance between inner eye points/cm4.2F3Outer eye point interval/cm10.3F4Height of nose/cm1.4F5Nose tip angle of left and right wings116°F6Eye point and nose angle in left and right32°F7Nose volume/cm37.9After obtaining the correct geometric features of the face, it is necessary to collect the geometric features of various facial expressions. The detailed expression conversion is shown in Table3.Table 3
Acquisition of geometric characteristics of the 3D face model.
FeatureSmileFrustratedPoutingBulging△F1/cm0.91.50.20.2△F2/cm0.51.80.30.3△F3/cm3.52.60.40.3△F4/cm0.60.300△F56°5°00△F64°10°2°4°△F725°8°6°5°△F845°26°25°25°△F9/cm342.600Geometric similarity0.580.450.600.55Relevance similarity0.640.710.630.59


### 3.3.2. Some Features Used in Gait Recognition

Mark Nixon, a professor of electrical engineering and computer science at the University of Southampton in the United Kingdom, found that the way people walk is fundamentally different. There are subtle aspects in muscle strength, flexibility of tendons, length and density of bones, vision, and damage to muscles and bones. Difference: the walking process of a normal adult is based on the regular movement of taking the left foot one step forward and the right foot one step forward, which is called the complete walking cycle. In this chapter, it is divided into several short periods according to the adjacent minimum value on theY axis of the acceleration sensor, so that it can easily determine the walking period corresponding to the data. According to statistics, the average time of a walking cycle is 1 to 1.5 seconds. These are based on complete walking time. The main characteristics considered are shown in Table 4.Table 4
Some features used in gait recognition.
FeatureDescriptionRoot mean squareThe sum of squares in the three directions ofX, Y, and Z is divided by 3, and the square is takenEnergyWork done in the three directions ofX, Y, and ZPercentile valueThe percentage of the number of samples below this sample value in the dataset to the total number of samplesMean absolute deviationThe average of the absolute value of the deviation of each value from its arithmetic meanCadenceThe number of complete gait cycles contained in 1 minute


### 3.3.3. Synchronization

In theory, when the sampling frequency of different sensors is set to the same, the data needs to be collected at the same time. In other words, the time stamps of the sampling points are exactly the same. However, in actual situations, even if the same smartphone is set to the same sampling frequency, the returned sensor data will have a different sampling time. Therefore, in order to avoid the time difference between the same frame of data intercepted according to the length of different sensors, it is necessary to synchronize the original sensor data. This process is shown in Figure2.Figure 2
Flow chart of sensor data synchronization process.


## 3.3.1. Collection of Geometric Features of Human Faces

The core function of the system is the display of sensor information, and it is necessary to design a table for initial storage of data. SQLSERVER was choosen as a stable and reliable high-performance database management system because it has a large-capacity data storage system, a variety of data, and long-term storage time period characteristics. The design of the data table must be reasonable and comprehensive, the image of each person in the neutral facial image was registered to the visual sensor network, and the other three representation variables and three illumination variables were used as the detection image set. The collected functions are shown in Table2.Table 2
Geometric features of standard human faces.
FeatureGeometric characteristicsMeasurementsF1Inside and outside eye point width/cm3.9F2Distance between inner eye points/cm4.2F3Outer eye point interval/cm10.3F4Height of nose/cm1.4F5Nose tip angle of left and right wings116°F6Eye point and nose angle in left and right32°F7Nose volume/cm37.9After obtaining the correct geometric features of the face, it is necessary to collect the geometric features of various facial expressions. The detailed expression conversion is shown in Table3.Table 3
Acquisition of geometric characteristics of the 3D face model.
FeatureSmileFrustratedPoutingBulging△F1/cm0.91.50.20.2△F2/cm0.51.80.30.3△F3/cm3.52.60.40.3△F4/cm0.60.300△F56°5°00△F64°10°2°4°△F725°8°6°5°△F845°26°25°25°△F9/cm342.600Geometric similarity0.580.450.600.55Relevance similarity0.640.710.630.59


## 3.3.2. Some Features Used in Gait Recognition

Mark Nixon, a professor of electrical engineering and computer science at the University of Southampton in the United Kingdom, found that the way people walk is fundamentally different. There are subtle aspects in muscle strength, flexibility of tendons, length and density of bones, vision, and damage to muscles and bones. Difference: the walking process of a normal adult is based on the regular movement of taking the left foot one step forward and the right foot one step forward, which is called the complete walking cycle. In this chapter, it is divided into several short periods according to the adjacent minimum value on theY axis of the acceleration sensor, so that it can easily determine the walking period corresponding to the data. According to statistics, the average time of a walking cycle is 1 to 1.5 seconds. These are based on complete walking time. The main characteristics considered are shown in Table 4.Table 4
Some features used in gait recognition.
FeatureDescriptionRoot mean squareThe sum of squares in the three directions ofX, Y, and Z is divided by 3, and the square is takenEnergyWork done in the three directions ofX, Y, and ZPercentile valueThe percentage of the number of samples below this sample value in the dataset to the total number of samplesMean absolute deviationThe average of the absolute value of the deviation of each value from its arithmetic meanCadenceThe number of complete gait cycles contained in 1 minute


## 3.3.3. Synchronization

In theory, when the sampling frequency of different sensors is set to the same, the data needs to be collected at the same time. In other words, the time stamps of the sampling points are exactly the same. However, in actual situations, even if the same smartphone is set to the same sampling frequency, the returned sensor data will have a different sampling time. Therefore, in order to avoid the time difference between the same frame of data intercepted according to the length of different sensors, it is necessary to synchronize the original sensor data. This process is shown in Figure2.Figure 2
Flow chart of sensor data synchronization process.


## 3.4. Simulation Experiment Results and Analysis

### 3.4.1. Evaluation Index

According to the definition of the object problem, the user identification of the smartphone belongs to multiple classification problems. The model classification performance evaluation index proposed in this paper uses accuracy, F1 value, ROC curve, and AUC. The calculation formulas for the matching rate and reproduction rate of thek category are as follows:(22)Pk=TPkTPk+FPk,Rk=TPkTPk+FNk.Accuracy is the most common evaluation index for classifiers. This is the ratio of the number of samples that are correctly identified (that is, correctly classified) for all sample sizes. For example, there are 1,000 students in junior high school. Obviously, there are two categories of students, boys and girls. Among them, 600 are boys and 400 are girls. If the classifier correctly determines the gender of 800 people (including 500 boys and 300 girls), then, the number of people correctly classified by the classifier will account for 80% of the total. In other words, the accuracy is 80% (800/1000). This paper shows the calculation method in following formula:(23)accuracy=1nsamples∑i=0nsampls−1fy^i=yi.TheF value is the weighted harmonic average of precision and precision. For multiple classification problems, the evaluation indicators of micro-F score and macro-F score are usually used. Because the coincidence rate and the reproduction rate may be mutually exclusive, it is necessary to consider these two issues together. In order to solve this problem, the F value is proposed, and the calculation formula is as follows:(24)Fβ=1+β2×P×Rβ2×P+R.


### 3.4.2. Detailed Comparison of Recognition Performance

In order to further prove the effectiveness of the algorithm in this paper, on the basis of the abovementioned experiment, the performance of the two algorithms is statistically compared, and the results obtained are shown in Table5.Table 5
Detailed comparison of the performance of the two algorithms.
Variable typeThe recognition accuracy of the algorithm in this paper (%)Recognition accuracy based on palmprint and facial feature algorithm (%)Happy10084Sad9776Surprised9662Light source 19876Light source 29669Light source 39063


### 3.4.3. Division Ratio Results

Figure3 shows the experimental results of the ShakeLogin dataset under experimental conditions. For the model SSUI proposed in this paper, when the division ratio is 0.8, the accuracy rate reaches the maximum, and then, when the division ratio is increased to 0.9, the accuracy rate drops slightly.Figure 3
Performance graph of the ShakeLogin dataset with different division ratios.The specific experimental results are shown in Figure4. It can be seen from the broken line chart that as the proportion increases, the accuracy of walking and going up and down stairs increases, and the accuracy of going up and down stairs increases by more than 20%. The other three actions have changed, but when the ratio reaches 0.9, and all actions have reached the maximum accuracy.Figure 4
HHAR dataset (all behaviors).


### 3.4.4. Results of All Behavioral Experiments

The results of this series of experiments are shown in Table6. The time-domain feature weighting parameter in this table changes from 1 to 2/3, 1/2, 1/3, and finally to 0. The frequency domain weighting parameter value ranges from 0 to 1/3, 1/2, 2/3, and finally to 1.Table 6
User identification results of ShakeLogin and HHAR datasets.
ShakeLogin (17 users, shake the phone arbitrarily)Random 80% of the entire data set/remaining85.4687.2592.0781.9875.33HHAR (9 users, 6 behaviors)All actions/all actions92.5697.1292.5697.2394.88All behaviors/walking90.0896.8797.7596.85100.0All actions/stations90.3797.7895.8498.0572.42All behaviors/sit86.1890.5592.6793.8366.05All behaviors/biking90.9285.3291.6288.8386.37All actions/stairs92.7596.9391.3595.1697.59All actions/down stairs88.7393.1895.5994.3997.56Figure5 shows the performance of the SSI model in the Shakelogin dataset and the experimental results of the sample period. As shown in the figure, the sample period is too short (2 seconds), the accuracy is very low, and the obtainable value is less than 60%. The longer the sample time is, the higher the correct rate (2 seconds to 4 seconds, 60% to close to 80%) is.Figure 5
SSUI’s performance in the ShakeLogin dataset varies with sample duration.


## 3.4.1. Evaluation Index

According to the definition of the object problem, the user identification of the smartphone belongs to multiple classification problems. The model classification performance evaluation index proposed in this paper uses accuracy, F1 value, ROC curve, and AUC. The calculation formulas for the matching rate and reproduction rate of thek category are as follows:(22)Pk=TPkTPk+FPk,Rk=TPkTPk+FNk.Accuracy is the most common evaluation index for classifiers. This is the ratio of the number of samples that are correctly identified (that is, correctly classified) for all sample sizes. For example, there are 1,000 students in junior high school. Obviously, there are two categories of students, boys and girls. Among them, 600 are boys and 400 are girls. If the classifier correctly determines the gender of 800 people (including 500 boys and 300 girls), then, the number of people correctly classified by the classifier will account for 80% of the total. In other words, the accuracy is 80% (800/1000). This paper shows the calculation method in following formula:(23)accuracy=1nsamples∑i=0nsampls−1fy^i=yi.TheF value is the weighted harmonic average of precision and precision. For multiple classification problems, the evaluation indicators of micro-F score and macro-F score are usually used. Because the coincidence rate and the reproduction rate may be mutually exclusive, it is necessary to consider these two issues together. In order to solve this problem, the F value is proposed, and the calculation formula is as follows:(24)Fβ=1+β2×P×Rβ2×P+R.


## 3.4.2. Detailed Comparison of Recognition Performance

In order to further prove the effectiveness of the algorithm in this paper, on the basis of the abovementioned experiment, the performance of the two algorithms is statistically compared, and the results obtained are shown in Table5.Table 5
Detailed comparison of the performance of the two algorithms.
Variable typeThe recognition accuracy of the algorithm in this paper (%)Recognition accuracy based on palmprint and facial feature algorithm (%)Happy10084Sad9776Surprised9662Light source 19876Light source 29669Light source 39063


## 3.4.3. Division Ratio Results

Figure3 shows the experimental results of the ShakeLogin dataset under experimental conditions. For the model SSUI proposed in this paper, when the division ratio is 0.8, the accuracy rate reaches the maximum, and then, when the division ratio is increased to 0.9, the accuracy rate drops slightly.Figure 3
Performance graph of the ShakeLogin dataset with different division ratios.The specific experimental results are shown in Figure4. It can be seen from the broken line chart that as the proportion increases, the accuracy of walking and going up and down stairs increases, and the accuracy of going up and down stairs increases by more than 20%. The other three actions have changed, but when the ratio reaches 0.9, and all actions have reached the maximum accuracy.Figure 4
HHAR dataset (all behaviors).


## 3.4.4. Results of All Behavioral Experiments

The results of this series of experiments are shown in Table6. The time-domain feature weighting parameter in this table changes from 1 to 2/3, 1/2, 1/3, and finally to 0. The frequency domain weighting parameter value ranges from 0 to 1/3, 1/2, 2/3, and finally to 1.Table 6
User identification results of ShakeLogin and HHAR datasets.
ShakeLogin (17 users, shake the phone arbitrarily)Random 80% of the entire data set/remaining85.4687.2592.0781.9875.33HHAR (9 users, 6 behaviors)All actions/all actions92.5697.1292.5697.2394.88All behaviors/walking90.0896.8797.7596.85100.0All actions/stations90.3797.7895.8498.0572.42All behaviors/sit86.1890.5592.6793.8366.05All behaviors/biking90.9285.3291.6288.8386.37All actions/stairs92.7596.9391.3595.1697.59All actions/down stairs88.7393.1895.5994.3997.56Figure5 shows the performance of the SSI model in the Shakelogin dataset and the experimental results of the sample period. As shown in the figure, the sample period is too short (2 seconds), the accuracy is very low, and the obtainable value is less than 60%. The longer the sample time is, the higher the correct rate (2 seconds to 4 seconds, 60% to close to 80%) is.Figure 5
SSUI’s performance in the ShakeLogin dataset varies with sample duration.


## 4. User Identification Applications and Risk Prevention and Control Strategies in Internet Financial Platforms

### 4.1. Identification of Fraudulent Users in Internet Financial Platforms

The data in this article comes from the user transaction record data of a European online payment platform, which was collected and shared during the research cooperation process of the machine learning team of Worldline and ULB (University Libre de Bruxelles). Because each transaction data contains a category label (that is, whether it is a fraudulent transaction), this is actually a supervised classification machine learning task. This experiment will first perform data preprocessing, that is, subsamples were obtained by random undersampling, and then, feature engineering was performed, including feature scaling, outlier detection, and feature screening. Subsequently, four supervised machine learning algorithms are used to train and test the model, and finally, a variety of evaluation indicators are used to evaluate the effects of various models.


### 4.2. The Main Links of User Identification in Internet Financial Platforms

#### 4.2.1. Verifying Customers

Confirm the customer and its actual manager, understand the natural purpose and transaction, confirm the identity of the customer, understand the actual beneficiary of the natural person and the actual management of the transaction, confirm the customer’s valid ID card or other ID, register the customer’s basic identity information and Relevant ID card copy information. This basic task is particularly important. The main reason is that in order to avoid legal sanctions when performing money laundering activities, criminals must conceal their true identity and actual transaction purpose. In order to effectively identify suspicious personnel and suspicious transactions of financial institutions, it is necessary to strictly and carefully determine and verify the true identity of customers and pay attention to daily business and financial transactions to ensure that abnormal situations are discovered in a timely manner.


#### 4.2.2. Continuous and In-Depth Identification of Customers

During the duration of their business, financial institutions also need to continue to reexamine customer accounts and transaction activities. This process is actually a process for financial institutions to learn more about customers and confirm their identities. In order to effectively achieve continuous identification, financial institutions need to do the following.As a work to prevent money laundering, the most basic thing is to be able to update customer information and related data in a timely manner. The staff at the counter of the basic relevant outlets needs to have sufficient sensitivity and attention to customer information and customer managers through early learning of the contact process of customer information. Then, continue to pay attention to the latest trends of customers, understand and update the money in the customer’s account, and the actual owner or beneficiary of these funds. In the absence of a legal person or other legal person organization, the credibility of relevant information must be judged carefully. In order to understand and prevent all the customer’s structure and internal management structure from being false, it is necessary to use the help of the propaganda platform to check and verify the offender as much as possible, to ensure that customers do not use false identities to hold stock or fund accounts and engage in money laundering activities.Please pay attention to the customer’s transaction behavior. Please note that whether the money in the customer’s account flows regularly or is very different from other accounts of the same or similar type. The average daily occurrence of the account, the degree of frequent trading operations, the source of remittance funds and account outflows, and the fixed transaction objects. Please pay attention to capital accounts with common characteristics. Suspicious accounts such as frequent and rapid fund transfers, transactions of almost equal amounts between borrowers and credit cards in a short period of time, and unusually active account transactions.Conduct customer surveys on a regular basis. In this work, the relevant staff must be familiar with the relevant business information of the customer, understand the industry in which the customer is engaged in advance, master the general financial settlement methods in the business process and whether they prefer cash settlement. In the practice of preventing money laundering, criminals frequently use cash transactions in order to avoid transfer of funds between banks or use promissory notes in the same city according to the needs of illegal and criminal activities. The information of the recipient, payer, and correspondent bank cannot be clearly displayed on the same bill, hide the source and location of funds, or personal accounts or frequent electronic remittances between units and personal accounts. Therefore, financial institutions cannot understand the actual parties behind the transaction and make decisions in a timely manner. Finally, it is necessary to strengthen system construction, establish customer electronic information database, establish customer credit database, and integrate system platform resources.


#### 4.2.3. Due Diligence on High-Risk Customers

Usually, the age and occupation of the customer obviously do not match the transaction amount and transaction frequency or customer accounts belong to high-risk industries, such as offshore companies or entertainment facilities, or individual customer accounts registered on the blacklist, such as terrorists or politically sensitive. Digital and financial institutions must conduct detailed due diligence on them. The customer’s due diligence must be carried out through the whole process of the financial institution’s business start, decision-making, and postloan management. The content of the investigation includes the complete collection of the client’s ID card, the understanding of the actual account manager or the actual beneficiary of the transaction, the confirmation of the client's valid ID or other IDs, the understanding of the source of funds, the use of funds, economic status or business status, and other information.


### 4.3. Development Strategies for Risk Prevention and Control of Internet Financial Platforms

#### 4.3.1. Start with the Details of the Process, and Comb Out the Operating Guidelines and Implementation Standards Based on the Characteristics of Your Own Business

Financial institutions need to incorporate customer identification into the internal control construction system, improve operating standards, clarify business processes, and implement customer risk classification and related risk classification standards. Strengthen the specific conditions and recognition points of initial recognition, continuous recognition, and rerecognition. Government regulatory agencies, the People’s Bank of China, financial institutions, public security, taxation, legal affairs, commerce, and foreign exchange administration departments provide advice and relevant professional support for the effective identification of various client ID information of financial institutions. Build a unified comprehensive information query system to solve the information problem of financial institutions’ verification practices.


#### 4.3.2. Make Full Use of Information Technology and Strengthen Identification Methods

One is to maximize the use of existing professional databases, such as World Check, to screen potential risks for customers, partners, transactions, and employees. World Check is public in the world. It contains public data on money launderers, scammers, terrorists, drug smugglers, fraud companies, etc. It is a recognized organization database. The second is to strengthen the use of advanced technologies such as fingerprint recognition, iris detection, and keyboard rhythm. At the same time, in order to improve the recognition accuracy of Internet financial services, in addition to general passwords and digital certificates, it is also necessary to increase the SMS and voice recognition of mobile phones. The third is to integrate the input and integration of scattered customer ID information, combine industries, regions, locations, other dynamic IDs, and automatic screening of high-risk customers to develop ID information tracking, monitoring, and analysis systems. With the rapid development of Internet technology, the conflict between traditional financial commerce and emerging Internet financial commerce has brought more difficulties and problems to the prevention of money laundering. In the new situation, financial institutions must respond in a timely manner. In order to maximize the use of the advantages of Internet financial services, effective countermeasures are taken on the basis of ensuring the identification to the greatest extent to achieve a balance between risks and benefits.


## 4.1. Identification of Fraudulent Users in Internet Financial Platforms

The data in this article comes from the user transaction record data of a European online payment platform, which was collected and shared during the research cooperation process of the machine learning team of Worldline and ULB (University Libre de Bruxelles). Because each transaction data contains a category label (that is, whether it is a fraudulent transaction), this is actually a supervised classification machine learning task. This experiment will first perform data preprocessing, that is, subsamples were obtained by random undersampling, and then, feature engineering was performed, including feature scaling, outlier detection, and feature screening. Subsequently, four supervised machine learning algorithms are used to train and test the model, and finally, a variety of evaluation indicators are used to evaluate the effects of various models.


## 4.2. The Main Links of User Identification in Internet Financial Platforms

### 4.2.1. Verifying Customers

Confirm the customer and its actual manager, understand the natural purpose and transaction, confirm the identity of the customer, understand the actual beneficiary of the natural person and the actual management of the transaction, confirm the customer’s valid ID card or other ID, register the customer’s basic identity information and Relevant ID card copy information. This basic task is particularly important. The main reason is that in order to avoid legal sanctions when performing money laundering activities, criminals must conceal their true identity and actual transaction purpose. In order to effectively identify suspicious personnel and suspicious transactions of financial institutions, it is necessary to strictly and carefully determine and verify the true identity of customers and pay attention to daily business and financial transactions to ensure that abnormal situations are discovered in a timely manner.


### 4.2.2. Continuous and In-Depth Identification of Customers

During the duration of their business, financial institutions also need to continue to reexamine customer accounts and transaction activities. This process is actually a process for financial institutions to learn more about customers and confirm their identities. In order to effectively achieve continuous identification, financial institutions need to do the following.As a work to prevent money laundering, the most basic thing is to be able to update customer information and related data in a timely manner. The staff at the counter of the basic relevant outlets needs to have sufficient sensitivity and attention to customer information and customer managers through early learning of the contact process of customer information. Then, continue to pay attention to the latest trends of customers, understand and update the money in the customer’s account, and the actual owner or beneficiary of these funds. In the absence of a legal person or other legal person organization, the credibility of relevant information must be judged carefully. In order to understand and prevent all the customer’s structure and internal management structure from being false, it is necessary to use the help of the propaganda platform to check and verify the offender as much as possible, to ensure that customers do not use false identities to hold stock or fund accounts and engage in money laundering activities.Please pay attention to the customer’s transaction behavior. Please note that whether the money in the customer’s account flows regularly or is very different from other accounts of the same or similar type. The average daily occurrence of the account, the degree of frequent trading operations, the source of remittance funds and account outflows, and the fixed transaction objects. Please pay attention to capital accounts with common characteristics. Suspicious accounts such as frequent and rapid fund transfers, transactions of almost equal amounts between borrowers and credit cards in a short period of time, and unusually active account transactions.Conduct customer surveys on a regular basis. In this work, the relevant staff must be familiar with the relevant business information of the customer, understand the industry in which the customer is engaged in advance, master the general financial settlement methods in the business process and whether they prefer cash settlement. In the practice of preventing money laundering, criminals frequently use cash transactions in order to avoid transfer of funds between banks or use promissory notes in the same city according to the needs of illegal and criminal activities. The information of the recipient, payer, and correspondent bank cannot be clearly displayed on the same bill, hide the source and location of funds, or personal accounts or frequent electronic remittances between units and personal accounts. Therefore, financial institutions cannot understand the actual parties behind the transaction and make decisions in a timely manner. Finally, it is necessary to strengthen system construction, establish customer electronic information database, establish customer credit database, and integrate system platform resources.


### 4.2.3. Due Diligence on High-Risk Customers

Usually, the age and occupation of the customer obviously do not match the transaction amount and transaction frequency or customer accounts belong to high-risk industries, such as offshore companies or entertainment facilities, or individual customer accounts registered on the blacklist, such as terrorists or politically sensitive. Digital and financial institutions must conduct detailed due diligence on them. The customer’s due diligence must be carried out through the whole process of the financial institution’s business start, decision-making, and postloan management. The content of the investigation includes the complete collection of the client’s ID card, the understanding of the actual account manager or the actual beneficiary of the transaction, the confirmation of the client's valid ID or other IDs, the understanding of the source of funds, the use of funds, economic status or business status, and other information.


## 4.2.1. Verifying Customers

Confirm the customer and its actual manager, understand the natural purpose and transaction, confirm the identity of the customer, understand the actual beneficiary of the natural person and the actual management of the transaction, confirm the customer’s valid ID card or other ID, register the customer’s basic identity information and Relevant ID card copy information. This basic task is particularly important. The main reason is that in order to avoid legal sanctions when performing money laundering activities, criminals must conceal their true identity and actual transaction purpose. In order to effectively identify suspicious personnel and suspicious transactions of financial institutions, it is necessary to strictly and carefully determine and verify the true identity of customers and pay attention to daily business and financial transactions to ensure that abnormal situations are discovered in a timely manner.


## 4.2.2. Continuous and In-Depth Identification of Customers

During the duration of their business, financial institutions also need to continue to reexamine customer accounts and transaction activities. This process is actually a process for financial institutions to learn more about customers and confirm their identities. In order to effectively achieve continuous identification, financial institutions need to do the following.As a work to prevent money laundering, the most basic thing is to be able to update customer information and related data in a timely manner. The staff at the counter of the basic relevant outlets needs to have sufficient sensitivity and attention to customer information and customer managers through early learning of the contact process of customer information. Then, continue to pay attention to the latest trends of customers, understand and update the money in the customer’s account, and the actual owner or beneficiary of these funds. In the absence of a legal person or other legal person organization, the credibility of relevant information must be judged carefully. In order to understand and prevent all the customer’s structure and internal management structure from being false, it is necessary to use the help of the propaganda platform to check and verify the offender as much as possible, to ensure that customers do not use false identities to hold stock or fund accounts and engage in money laundering activities.Please pay attention to the customer’s transaction behavior. Please note that whether the money in the customer’s account flows regularly or is very different from other accounts of the same or similar type. The average daily occurrence of the account, the degree of frequent trading operations, the source of remittance funds and account outflows, and the fixed transaction objects. Please pay attention to capital accounts with common characteristics. Suspicious accounts such as frequent and rapid fund transfers, transactions of almost equal amounts between borrowers and credit cards in a short period of time, and unusually active account transactions.Conduct customer surveys on a regular basis. In this work, the relevant staff must be familiar with the relevant business information of the customer, understand the industry in which the customer is engaged in advance, master the general financial settlement methods in the business process and whether they prefer cash settlement. In the practice of preventing money laundering, criminals frequently use cash transactions in order to avoid transfer of funds between banks or use promissory notes in the same city according to the needs of illegal and criminal activities. The information of the recipient, payer, and correspondent bank cannot be clearly displayed on the same bill, hide the source and location of funds, or personal accounts or frequent electronic remittances between units and personal accounts. Therefore, financial institutions cannot understand the actual parties behind the transaction and make decisions in a timely manner. Finally, it is necessary to strengthen system construction, establish customer electronic information database, establish customer credit database, and integrate system platform resources.


## 4.2.3. Due Diligence on High-Risk Customers

Usually, the age and occupation of the customer obviously do not match the transaction amount and transaction frequency or customer accounts belong to high-risk industries, such as offshore companies or entertainment facilities, or individual customer accounts registered on the blacklist, such as terrorists or politically sensitive. Digital and financial institutions must conduct detailed due diligence on them. The customer’s due diligence must be carried out through the whole process of the financial institution’s business start, decision-making, and postloan management. The content of the investigation includes the complete collection of the client’s ID card, the understanding of the actual account manager or the actual beneficiary of the transaction, the confirmation of the client's valid ID or other IDs, the understanding of the source of funds, the use of funds, economic status or business status, and other information.


## 4.3. Development Strategies for Risk Prevention and Control of Internet Financial Platforms

### 4.3.1. Start with the Details of the Process, and Comb Out the Operating Guidelines and Implementation Standards Based on the Characteristics of Your Own Business

Financial institutions need to incorporate customer identification into the internal control construction system, improve operating standards, clarify business processes, and implement customer risk classification and related risk classification standards. Strengthen the specific conditions and recognition points of initial recognition, continuous recognition, and rerecognition. Government regulatory agencies, the People’s Bank of China, financial institutions, public security, taxation, legal affairs, commerce, and foreign exchange administration departments provide advice and relevant professional support for the effective identification of various client ID information of financial institutions. Build a unified comprehensive information query system to solve the information problem of financial institutions’ verification practices.


### 4.3.2. Make Full Use of Information Technology and Strengthen Identification Methods

One is to maximize the use of existing professional databases, such as World Check, to screen potential risks for customers, partners, transactions, and employees. World Check is public in the world. It contains public data on money launderers, scammers, terrorists, drug smugglers, fraud companies, etc. It is a recognized organization database. The second is to strengthen the use of advanced technologies such as fingerprint recognition, iris detection, and keyboard rhythm. At the same time, in order to improve the recognition accuracy of Internet financial services, in addition to general passwords and digital certificates, it is also necessary to increase the SMS and voice recognition of mobile phones. The third is to integrate the input and integration of scattered customer ID information, combine industries, regions, locations, other dynamic IDs, and automatic screening of high-risk customers to develop ID information tracking, monitoring, and analysis systems. With the rapid development of Internet technology, the conflict between traditional financial commerce and emerging Internet financial commerce has brought more difficulties and problems to the prevention of money laundering. In the new situation, financial institutions must respond in a timely manner. In order to maximize the use of the advantages of Internet financial services, effective countermeasures are taken on the basis of ensuring the identification to the greatest extent to achieve a balance between risks and benefits.


## 4.3.1. Start with the Details of the Process, and Comb Out the Operating Guidelines and Implementation Standards Based on the Characteristics of Your Own Business

Financial institutions need to incorporate customer identification into the internal control construction system, improve operating standards, clarify business processes, and implement customer risk classification and related risk classification standards. Strengthen the specific conditions and recognition points of initial recognition, continuous recognition, and rerecognition. Government regulatory agencies, the People’s Bank of China, financial institutions, public security, taxation, legal affairs, commerce, and foreign exchange administration departments provide advice and relevant professional support for the effective identification of various client ID information of financial institutions. Build a unified comprehensive information query system to solve the information problem of financial institutions’ verification practices.


## 4.3.2. Make Full Use of Information Technology and Strengthen Identification Methods

One is to maximize the use of existing professional databases, such as World Check, to screen potential risks for customers, partners, transactions, and employees. World Check is public in the world. It contains public data on money launderers, scammers, terrorists, drug smugglers, fraud companies, etc. It is a recognized organization database. The second is to strengthen the use of advanced technologies such as fingerprint recognition, iris detection, and keyboard rhythm. At the same time, in order to improve the recognition accuracy of Internet financial services, in addition to general passwords and digital certificates, it is also necessary to increase the SMS and voice recognition of mobile phones. The third is to integrate the input and integration of scattered customer ID information, combine industries, regions, locations, other dynamic IDs, and automatic screening of high-risk customers to develop ID information tracking, monitoring, and analysis systems. With the rapid development of Internet technology, the conflict between traditional financial commerce and emerging Internet financial commerce has brought more difficulties and problems to the prevention of money laundering. In the new situation, financial institutions must respond in a timely manner. In order to maximize the use of the advantages of Internet financial services, effective countermeasures are taken on the basis of ensuring the identification to the greatest extent to achieve a balance between risks and benefits.


## 5. Conclusion

Based on wireless sensor networks, this paper studies the development of user identification and network finance. This paper studies the characteristics of wireless sensor data, better data reduction, and data classification algorithms and proposes a basic action recognition method based on wireless sensor data. Aiming to improve the shortcomings of traditional identification algorithms, an adaptive identification algorithm based on the improved minimum gray tree of the visual sensor network is proposed. The simulation results show that the algorithm shows a high accuracy of identification. On the other hand, wireless sensor call is simple and easy to obtain data, so it forms the basis of wireless sensor data research. Each user who uses the wireless sensor generates sensor data, and the sensor data is generated in different ways depending on the user. Therefore, the user can be identified through wireless sensor data. The prevention of money laundering is affected by the rapid development of Internet technology and the conflict between traditional financial commerce and emerging Internet financial commerce, and many new problems have emerged. Financial institutions in a new situation need to respond in a timely manner. The concept of the effectiveness and maximum guarantee of the countermeasures is to maximize the use of the advantages of Internet financial services to achieve a balance between risks and profits. The establishment of a complete and effective customer information database will undoubtedly increase the human and material resources of various financial institutions. However, there are many problems such as information collection and information verification, and huge investment may be effective, which will have a certain degree of impact on the optimization and improvement of “customer identification.”



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*Source: 6975347-2022-02-28.xml*