CN113177795A

CN113177795A - Identity recognition method, device, equipment and medium

Info

Publication number: CN113177795A
Application number: CN202110647085.2A
Authority: CN
Inventors: 杜志军
Original assignee: Alipay Hangzhou Information Technology Co Ltd
Current assignee: Alipay Hangzhou Information Technology Co Ltd
Priority date: 2021-06-10
Filing date: 2021-06-10
Publication date: 2021-07-27
Anticipated expiration: 2041-06-10
Also published as: CN113177795B

Abstract

The embodiment of the specification discloses an identity identification method, an identity identification device, identity identification equipment and identity identification media. The scheme comprises the following steps: acquiring input behavior characteristics when a user inputs a password; calculating the similarity between the input behavior features and preset input behavior features in a preset input behavior feature set; judging whether the value of at least one similarity in the similarities of the input behavior features and the preset input behavior features in the preset input behavior feature set is greater than or equal to a first threshold value or not to obtain a first judgment result; and if the first judgment result shows that the numerical values of the similarity between the input behavior feature and each preset input behavior feature in the preset input behavior feature set are all smaller than the first threshold, executing an identity verification process based on biological information identification for the user.

Description

Identity recognition method, device, equipment and medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a medium for identity recognition.

Background

With the development of computer technology, the payment security of mobile devices is an important issue, and although users usually set payment passwords, the payment passwords are also potentially leaked and mastered. Under the condition of obtaining the mobile phone and the password of the victim, the operations such as transfer, payment and the like can be easily realized. At present, aiming at the situation, the server cannot trace the operation of the owner or the stolen behavior of the acquaintance.

Therefore, how to identify the user identity to improve the security is an urgent technical problem to be solved.

Disclosure of Invention

Embodiments of the present specification provide an identity identification method, apparatus, device, and medium, so as to solve a problem that a user identity of an operating user cannot be confirmed in an existing method.

In order to solve the above technical problem, the embodiments of the present specification are implemented as follows:

an identity identification method provided by an embodiment of the present specification includes:

acquiring input behavior characteristics when a user inputs a password;

calculating the similarity between the input behavior features and preset input behavior features in a preset input behavior feature set to obtain the similarity between the input behavior features and each preset input behavior feature in the input behavior feature set; the preset input behavior feature set comprises at least one preset input behavior feature;

judging whether the value of at least one similarity in the similarities of the input behavior features and the preset input behavior features in the preset input behavior feature set is greater than or equal to a first threshold value or not to obtain a first judgment result;

and if the first judgment result shows that the numerical values of the similarity between the input behavior feature and each preset input behavior feature in the preset input behavior feature set are all smaller than the first threshold, executing an identity verification process based on biological information identification for the user.

An identity recognition device provided by the embodiments of this specification includes:

the characteristic acquisition module is used for acquiring input behavior characteristics when a user inputs a password;

the similarity calculation module is used for calculating the similarity between the input behavior features and preset input behavior features in a preset input behavior feature set to obtain the similarity between the input behavior features and each preset input behavior feature in the input behavior feature set; the preset input behavior feature set comprises at least one preset input behavior feature;

the judging module is used for judging whether the value of at least one similarity in the similarities of the input behavior features and the preset input behavior features in the preset input behavior feature set is larger than or equal to a first threshold value or not to obtain a first judging result;

and the identity verification module is used for executing an identity verification process based on biological information identification for the user if the first judgment result shows that the input behavior feature and the numerical value of the similarity of each preset input behavior feature in the preset input behavior feature set are all smaller than the first threshold.

An identity recognition device provided in an embodiment of the present specification includes:

at least one processor; and the number of the first and second groups,

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to:

acquiring input behavior characteristics when a user inputs a password;

Embodiments of the present specification provide a computer readable medium having stored thereon computer readable instructions executable by a processor to implement an identification method.

One embodiment of the present description achieves the following advantageous effects: by calculating the similarity between the input behavior characteristics of the user when inputting the password and the preset input behavior characteristics, when the similarity between the input behavior characteristics of the user inputting the password and each preset input behavior characteristic in the preset input behavior characteristic set is smaller than a first threshold value, it is determined that the current user has a risk, an identity verification process based on biological information identification needs to be executed for the user, whether the current user is a real user of a known account is identified, and the security of the account can be improved.

Drawings

In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.

Fig. 1 is a schematic view of an application scenario of an identity recognition method provided in an embodiment of the present specification;

fig. 2 is a schematic flow chart of an identity recognition method provided in an embodiment of the present disclosure;

FIG. 3 is a swim lane diagram of an identification method provided in an embodiment of the present disclosure;

fig. 4 is a schematic structural diagram of an identification apparatus provided in an embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of an identification device provided in an embodiment of this specification.

Detailed Description

To make the objects, technical solutions and advantages of one or more embodiments of the present disclosure more apparent, the technical solutions of one or more embodiments of the present disclosure will be described in detail and completely with reference to the specific embodiments of the present disclosure and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present specification, and not all embodiments. All other embodiments that can be derived by a person skilled in the art from the embodiments given herein without making any creative effort fall within the scope of protection of one or more embodiments of the present specification.

The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.

In the prior art, when mobile payment is carried out, only the password is required to be input correctly to complete one payment operation, because a server providing payment service only checks a payment account and the input password. If the equipment (such as a mobile phone, a computer and the like) for carrying out mobile payment is stolen, and meanwhile, the account password of the user is leaked, the risk of stealing funds is caused at the moment. Particularly, in the case of a case where a acquaintance works, for example, a child holds a mobile phone of a parent or holds a mobile phone of a friend or a family, the fund loss is difficult to prevent and control.

In order to solve the defects in the prior art, the scheme provides the following embodiments:

fig. 1 is a schematic view of an application scenario of an identity recognition method provided in an embodiment of this specification. As shown in fig. 1, the scheme mainly includes a terminal 1 and a server 2. In practical application, a user can input a password in the terminal 1 through the terminal 1 to perform operations such as payment and account login, when the user inputs the password in the terminal 1, the terminal 1 can acquire operation data such as an input mode selected when the user inputs the password, a key time interval during input, and the pressure of a key, and the server 2 can acquire the operation data acquired in the terminal 1 when the user inputs the password to obtain input behavior characteristics when the user inputs the password. The server 2 may store a preset input behavior feature set in advance, where the preset input behavior feature in the feature set is a behavior feature of a real user. For example, when a user performs a payment operation through an application in the terminal, the registered user of the application may be an actual user of the application in the terminal. The server 2 can calculate the similarity between the input behavior characteristics of the user who inputs the password and the preset input behavior characteristics in the preset input behavior characteristic set, when the similarity between the input behavior characteristics of the user who inputs the password and the preset input behavior characteristics in the preset input behavior characteristic set is small, the user who inputs the password may not be a real user and needs to call a biological core, an identity verification process based on biological information identification is executed on the user, whether the user is the real user is further determined, the phenomenon that the account is stolen can be timely discovered and intercepted, and the safety of the user account is improved. For the scene of payment through the password, the resource loss of the user can be effectively avoided.

Next, an identity recognition method provided in the embodiments of the specification will be specifically described with reference to the accompanying drawings:

fig. 2 is a schematic flow chart of an identity recognition method provided in an embodiment of the present disclosure. From the viewpoint of a program, the execution subject of the flow may be a program installed in an application server or an application client.

As shown in fig. 2, the process may include the following steps:

step 202: and acquiring the input behavior characteristics when the user inputs the password.

In this embodiment of the present specification, a user may perform service processing by inputting a password through a terminal, for example, perform operations such as payment and account login through the password. The terminal can collect operation data such as an input mode selected by a user when the user inputs the password, a key time interval when the user inputs the password, the pressure of the key and the like, and can obtain input behavior characteristics when the user inputs the password based on the operation data. The terminal can be a mobile phone, a computer, an intelligent wearable device and the like.

In the embodiment of the present specification, the input behavior characteristic when the user inputs the password is obtained based on the entire operation behavior of the user inputting the password at the current time, and is a piece of characteristic data representing the operation behavior of the user inputting the password at the current time.

Step 204: calculating the similarity between the input behavior features and preset input behavior features in a preset input behavior feature set to obtain the similarity between the input behavior features and each preset input behavior feature in the input behavior feature set; the preset input behavior feature set comprises at least one preset input behavior feature.

In this embodiment, the preset input behavior feature may be a behavior feature of a password input by a real user of the terminal, and may be used to distinguish whether a user who inputs a password in the terminal is a real user. In the embodiment of the present specification, the similarity between the input behavior feature generated when the user inputs the password and the preset input behavior feature may be calculated, and then it is determined whether the user inputting the password is a real user.

Step 206: and judging whether the value of at least one similarity in the similarities of the input behavior features and the preset input behavior features in the preset input behavior feature set is greater than or equal to a first threshold value or not, and obtaining a first judgment result.

Step 208: and if the first judgment result shows that the numerical values of the similarity between the input behavior feature and each preset input behavior feature in the preset input behavior feature set are all smaller than the first threshold, executing an identity verification process based on biological information identification for the user.

In the embodiment of the present specification, when the similarity between the input behavior feature of the user inputting the password and the preset input behavior feature is small, it may be determined that the user currently inputting the password may not be the real user of the terminal, and in order to ensure security, the user may be subjected to biometric information verification, and the user is required to input biometric information, such as a face, a fingerprint, and the like, to verify the identity of the user.

It should be understood that the order of some steps in the method described in one or more embodiments of the present disclosure may be interchanged according to actual needs, or some steps may be omitted or deleted.

In the method in fig. 2, by calculating the similarity between the input behavior feature when the user inputs the password and the preset input behavior feature, when the numerical value of the similarity between the input behavior feature of the user who inputs the password and each preset input behavior feature in the preset input behavior feature set is smaller than the first threshold, it is determined that the current user is at risk, and an authentication process based on biological information identification needs to be performed for the user to identify whether the current user is a real user of a known account, so that the security of the account can be improved.

When the password is stolen by others, the stealing phenomenon can be found in time, an identity verification process based on biological information identification is executed, whether the user who inputs the password is a real user is further confirmed, and account safety is determined.

For example, when a child purchases an item using a parent's mobile phone, even if the child knows a payment password set by the parent, due to a difference between input behavior characteristics when the child and the parent input the password, it can be determined that the password is not a real user of the terminal, and the payment can be completed only by further performing an authentication process based on biometric information identification, so that a phenomenon that an acquaintance steals the user terminal to perform password payment can be avoided, and the security of the transaction can be improved.

Based on the method of fig. 2, the present specification also provides some specific embodiments of the method, which are described below.

Optionally, after obtaining the first determination result in the embodiment of the present specification, the method may further include:

and if the first judgment result shows that the numerical value of at least one similarity in the similarities of the input behavior features and the preset input behavior features in the preset input behavior feature set is greater than or equal to the first threshold, determining that the user is a real user of the known account.

In practical applications, the user may perform service processing in an application program of the terminal, for example, may perform payment service processing in an application program with a payment function. The known account can be an account which is logged in the terminal or an account which has not been logged in when account information is just input, and the user can perform business processing in the logged-in account or log in the account which has not been logged in when account information is just input by inputting a password. The real users of the known accounts can be users who register the known accounts or users who often use the known accounts.

As an implementation manner, in this embodiment, the server may calculate similarity between the input behavior feature and a preset input behavior feature in a preset input behavior feature set item by item, and may further determine, when the similarity between the input behavior feature and any one preset input behavior feature is calculated, whether a numerical value of the similarity between the input behavior feature and any one preset input behavior feature is greater than or equal to a first threshold, if the numerical value of the similarity is greater than or equal to the first threshold, it may be determined that the user is a real user of a known account, and the similarity between the user and other preset input behavior features may not be calculated any more; if the numerical value of the similarity is smaller than a first threshold, another preset input behavior feature can be selected from a preset input behavior feature set for similarity calculation, and by analogy, if one numerical value of the similarity between the preset input behavior feature and the input behavior feature in the preset input behavior feature set is larger than or equal to the first threshold, the user can be determined as a real user of the known account, and if the numerical value of the similarity between the input behavior feature and each preset input behavior feature in the preset input behavior feature set is smaller than the first threshold, an identity verification process based on biological information recognition is executed for the user.

As another implementation manner, in this embodiment of the present description, similarities between behavior features and each preset input behavior feature in the input behavior feature set may be input, then a maximum similarity among the calculated similarities is selected, and whether a numerical value of the maximum similarity is greater than or equal to a first threshold value is determined, if yes, it may be determined that the user is a real user of a known account; and if not, executing an identity verification process based on biological information identification for the user.

In practical application, gesture behaviors of each user are different when the user inputs a password, for example, the key is light and heavy, the pressure of corresponding screen clicking is different, for example, some people type quickly, some people type slowly, the password input consumes different time, for example, the user is different in personal habit input mode, some people are used in Sudoku, and some people are used in all spellings, handwriting and the like. These behavioral characteristics may be used to describe a user.

In the embodiment of the present specification, the input behavior feature of the user is a feature obtained based on at least one behavior of an input mode, a key pressure, a key area, a key duration, and a key position that is adopted when the user inputs a password at the current time.

The input mode may include squared figure, spell, handwriting, etc. and may represent the use habit of different people in inputting characters.

The key pressure may be the pressure of pressing keys in a keyboard provided by the terminal by a user, and the pressure represents the difference of the weights of the keys of different people. Wherein the measurement can be performed by means of a pressure sensor or the like. In practical applications, the key pressure may include a pressure of each key by the user, or may include a mean value, a variance, an extreme value, and the like of pressures corresponding to all keys pressed when the user inputs a password, and the key pressure may also correspond to a character represented by the key, or may also correspond to relative position information of the key in the keyboard.

The key area is the area of the finger in contact with the screen when the user inputs the password, and can reflect the key difference of different people. In practical application, the sensor in the terminal can detect the key area information, for example, when the touch screen in the terminal is a capacitive screen, the area of the touch between the finger and the screen can be determined based on the current in the screen; the area of the key pressed by the finger can be determined based on the position range of the key pressed by the finger; the area of the key pressed by the finger can also be determined based on factors such as the temperature of the finger contacted by the key. In practical application, the area of the key may correspond to the character represented by the key, or may correspond to the relative position information of the key in the keyboard.

The key duration may include the duration of pressing each key by the user, the duration of inputting the password once completely, the duration of key interval between adjacent characters in the password input by the user, and the like, which may reflect the input habits of different people.

The key position may be a coordinate position where a pressed key position when the user presses each key corresponds to a screen in the terminal, or a coordinate position where the pressed key position corresponds to a keyboard displayed in the terminal.

In consideration of the fact that in practical application, a user may perform password input by using a security keyboard provided by a terminal application program, and the position of a number in the security keyboard may change every time the password is input, in this embodiment of the present specification, a key position of a key pressed by the user may be obtained according to a position of the pressed key on the key when the user presses the key, relative to the position of the area where the key is located, based on the area where each key is located, and the position information is combined with position information of the key in the whole keyboard or a screen to obtain key position information when the user inputs the password.

It is to be understood that the behavior data when the user inputs the password may be determined according to actual needs, which is merely an illustration for explaining the scheme of the present application, and the specific manner of acquiring the input behavior of the user input the password is not particularly limited herein.

In practical application, features can be extracted from the collected information such as input modes, key pressure, key duration, key positions and the like by using feature extraction, and the behavior of one-time password input by a user can be converted into a behavior feature. An input behavior feature may represent the operation behavior of a user inputting a password once, and is obtained based on the total feature behavior in the process of inputting the password by the user.

In practical applications, the number of dimensions of the feature extracted from the behavior data of the password input by the user may be more, for example, the number of dimensions of the feature extracted from the behavior data of the password input by the user is 40 dimensions, and in order to simplify the calculation amount, the extracted input behavior feature may be subjected to a dimension reduction process, for example, the extracted input behavior feature of 40 dimensions is reduced to 16 dimensions for subsequent calculation.

In this embodiment of the present specification, after obtaining the input behavior feature when the user inputs the password, the method may further include:

performing dimension reduction processing on the input behavior characteristics based on a neural network model to obtain dimension-reduced input behavior characteristics;

the calculating the similarity between the input behavior feature and a preset input behavior feature in a preset input behavior feature set specifically includes:

and calculating the similarity between the input behavior characteristics after dimension reduction and preset input behavior characteristics in the preset input behavior characteristic set.

In this embodiment of the present description, the neural network model for reducing dimensions may be a pre-trained neural network model, where DNNs (deep neural networks) including a plurality of fully connected layers may be trained by using a tripletloss (ternary loss) training method to obtain the neural network model for reducing dimensions, and after the model is used for reducing dimensions, low-dimensional features of the same person are very similar, and low-dimensional features of different persons are not similar.

In practical application, the server may record the behavior characteristics of the user when inputting the password each time, may regard the historical behavior characteristics of correctly inputting the password each time as the behavior characteristics of the real user of the terminal, may perform similarity calculation between the behavior characteristics of the current new password input this time and each recorded historical behavior when inputting the new password once, and may determine that the user inputting the password this time is the real user of the terminal if the behavior characteristics of the current password input this time are similar to most of the historical behavior characteristics.

Considering that each password input corresponds to one historical input behavior feature in practical application, the number of the historical input behavior features of the user is increased along with the increase of the password input times of the user, and if the input behavior features of the current password input by the user are compared with each historical input behavior feature, the similarity is calculated, and the calculation amount of the server is large. Moreover, the multiple historical input behavior characteristics generated by the same user when inputting the password may be similar, for example, when the user inputs the password multiple times in the same input mode, the input speed, the key pressure, and other characteristics of the user may be similar or identical, and therefore, the comparison between the input behavior characteristics when the user inputs the password at the current time and the multiple similar characteristics may also cause waste of resources.

In order to more effectively determine whether the user who inputs the password is the real user, in this embodiment of the present specification, the preset input behavior feature set is obtained based on the historical input behavior features of the real user who inputs the preset password in the known account, and specifically may include:

acquiring a historical input behavior feature set of the real user aiming at the preset password; the historical input behavior feature set comprises at least one historical input behavior feature generated when the real user inputs the preset password;

clustering the historical input behavior characteristics in the historical input behavior characteristic set to obtain a clustering result;

and obtaining the preset input behavior feature set based on the clustering result.

The historical input behavior feature may be an input behavior feature obtained based on a behavior of inputting a password before the user inputs the password this time, for example, the historical input behavior feature may be an input behavior feature of inputting a password by the user in a preset time period before the user inputs the password this time, or a historical input behavior feature of selecting a preset number of passwords closest to the user input password this time in a time sequence from near to far. The specific manner of selecting the historical input behavior feature is not limited here, as long as the preset input behavior feature can be determined according to the selected historical input behavior feature.

In consideration of the fact that in practical application, when a user resets a password for a newly registered user of an application program or a user, the number of times of performing service processing on the password input by the user is small, and the number of pieces of behavior data of the password input by the user recorded in a server is small, in order to more accurately identify the user inputting the password, in the embodiment of the present specification, the user behavior feature of the currently input password may be compared with all historical behavior features, and when the number of the historical input behavior features recorded in the server is large, a plurality of representative behavior features are selected from the historical input behavior features to serve as preset input behavior features, and are compared with the behavior feature of the password input by the current user to identify the user.

In this embodiment of this specification, before performing clustering processing on the historical input behavior features in the historical input behavior feature set, the method further includes:

judging whether the number of the historical input behavior features contained in the historical input behavior feature set is greater than or equal to a first preset number or not to obtain a second judgment result;

if the second judgment result indicates that the number of the historical input behavior features contained in the historical input behavior feature set is smaller than a first preset number, determining the historical input behavior features contained in the historical input behavior feature set as preset input behavior features in the preset input behavior feature set;

the clustering processing of the historical input behavior features in the historical input behavior feature set specifically includes:

and if the second judgment result shows that the number of the historical input behavior features contained in the historical input behavior feature set is greater than or equal to a first preset number, clustering the historical input behavior features in the historical input behavior feature set.

The first preset number may be set according to actual requirements, and is not specifically limited herein.

In order to improve the efficiency of the clustering process, in the embodiment of the present specification, the dimension reduction process may be performed on each historical input behavior feature subjected to the clustering process, and then the clustering process is performed on the historical input behavior features subjected to the dimension reduction process, so as to obtain a preset input behavior feature set.

In this embodiment of this specification, before performing clustering processing on the historical input behavior features in the historical input behavior feature set, the method may further include:

performing dimension reduction processing on the historical input behavior feature set to obtain a dimension-reduced historical input behavior feature set; the historical input behavior features in the historical input behavior feature set after dimension reduction are historical input behavior features after dimension reduction;

the clustering process of the historical input behavior features in the historical input behavior feature set may specifically include:

and clustering each historical input behavior feature in the dimensionality reduced historical input behavior feature set.

Performing dimension reduction processing on the historical input behavior feature set specifically may include: and performing dimension reduction processing on each historical input behavior feature in the historical input behavior feature set. And clustering each historical input behavior feature in the dimensionality reduced historical input behavior feature set, which may be clustering each dimensionality reduced historical input behavior feature.

In the prior art, a mean value is usually calculated from a plurality of behavior characteristics, and then the mean value is used to represent typical behavior characteristics of a user. Although the method is effective, certain information loss exists, because the average value is not a real behavior characteristic at one time, but a result of accumulating and averaging a large number of behaviors, if the user behavior itself has several different input characteristics, adverse effects are generated, for example, when a user inputs a password through a full spelling input mode, one characteristic corresponds to the user, when the user inputs the password through a Sudoku input mode, the other characteristic corresponds to the user, and the performance is reduced by adopting averaging processing.

In order to determine the preset input behavior characteristics that meet the behavior characteristics of the user more accurately, the clustering process performed on the historical input behavior characteristics in the historical input behavior characteristic set in the embodiment of the present specification may specifically include:

for any historical input behavior feature in the historical input behavior feature set, calculating the similarity between the any historical input behavior feature and other historical input behavior features in the historical input behavior feature set to obtain the similarity between the any historical input behavior feature and each other historical input behavior feature in the historical input behavior feature set; the other historical input behavior features are historical input behavior features except the any historical input behavior feature in the historical input behavior feature set;

determining the number of other historical input behavior features, of which the similarity between other historical input behavior features and any one historical input behavior feature is greater than or equal to a second threshold value, in the historical input behavior feature set;

judging whether the number is greater than or equal to a second preset number to obtain a third judgment result;

and if the third judgment result shows that the number is greater than or equal to a second preset number, determining any one of the historical input behavior characteristics as one preset input behavior characteristic in the preset input behavior characteristic set.

The second threshold and the second preset number may be set according to actual requirements, and are not specifically limited herein. In practical application, a plurality of differentiated behavior characteristics can be mined from the historical input behavior characteristic set by adjusting the second threshold and/or the second preset number, so that the user behavior can be described more comprehensively, for example, when the user adopts different input modes, the corresponding input behavior characteristics are different, the different input behavior characteristics corresponding to the different input modes can be selected by the specific method, and the user inputting the password can be identified more accurately.

In order to reduce the similarity of the preset input behavior features in the preset input behavior feature set and avoid comparing the input behavior features of the current user input password with too many similar behavior features, in this embodiment of the present specification, after determining that the any one historical input behavior feature is one preset input behavior feature in the preset input behavior feature set, the method may further include:

and deleting the any one historical input behavior feature and other historical input behavior features with the similarity value of the any one historical input behavior feature and the similarity value of the any one historical input behavior feature being larger than or equal to a second threshold value from the historical input behavior feature set.

In the embodiment of the present specification, in a historical input behavior feature set in which a historical input behavior feature that has been determined as a preset input behavior feature and is similar to the preset input behavior feature is deleted, one historical input behavior feature is arbitrarily selected to perform clustering processing, and so on, a plurality of differentiated historical input behavior features can be obtained as the preset input behavior feature.

In practical application, the K-means clustering algorithm can be adopted for clustering.

In this embodiment of the present description, a cosine similarity calculation method may be adopted to calculate a similarity, where the calculating a similarity between any one of the historical input behavior features and other historical input behavior features in the historical input behavior feature set specifically may include:

and calculating the similarity between any one historical input behavior feature and other historical input behavior features in the historical input behavior feature set based on a cosine similarity calculation method.

The calculating the similarity between the input behavior feature and a preset input behavior feature in a preset input behavior feature set may specifically include:

and calculating the similarity between the input behavior feature and a preset input behavior feature in a preset input behavior feature set based on a cosine similarity calculation method. Wherein the input behavior feature has the same feature dimension as the preset input behavior feature.

In this embodiment of the present description, when performing dimension reduction processing on the input behavior feature and the preset input behavior feature, the same pre-trained neural network model may be used, and the obtained dimension of the dimension-reduced input behavior feature may be the same as the dimension of the dimension-reduced preset input behavior feature.

In this embodiment of the present specification, the performing, for the user, an identity verification process based on biometric information identification may specifically include:

sending indication information for indicating a user to input biological identity information to a terminal; the terminal is used for executing password input operation by a user;

acquiring biological identity information acquired by the terminal; the biometric identity information includes: at least one of face information, fingerprint information, iris information and voice information;

judging whether the biological identity information is consistent with preset identity information or not;

and if so, the user identity authentication is passed, and the user is determined to be a real user of the known account.

In practical application, when a user registers an account or uses the account for the first time, the server may collect biological identity information of the user as a user identity credential for storage, where the preset identity information may be information that is collected in advance and can prove the identity of the real user.

In order to ensure the reliability of data, the preset identity information in the embodiment of the present specification may be stored in the blockchain system, and the server may obtain the preset identity information from the blockchain system when performing identity verification.

The judgment of whether the biological identity information input by the user for inputting the password is consistent with the preset identity information can also be understood as judging whether the similarity between the biological identity information input by the user and the preset identity information is greater than or equal to a preset threshold value.

The method provided in the embodiment of the present specification is to perform risk assessment on an operation behavior of inputting a password by a user, and identify whether the user inputting the password is a real user of a terminal account, and in the embodiment of the present specification, the method may further perform verification on the password input by the user, and specifically, the method may further include:

acquiring a password input by the user;

and judging whether the password is consistent with a preset password.

The preset password is a real password corresponding to the terminal application program, and may be used to log in the application program or perform service processing after the application program has been logged in, for example, service such as payment may be performed through the password.

In this embodiment of this specification, after a user inputs a password, the method may identify the user identity based on an input behavior characteristic when the user inputs the password, determine whether the user who inputs the password is a real user, and then determine whether the input password is correct, and specifically, after the method in this embodiment of this specification confirms that the user is a real user with a known account, the method may further include:

acquiring a password input by the user;

judging whether the password is consistent with a preset password or not;

if the password is consistent with the target task, the password is verified to be passed, and the target task is executed; the target task may be a service executed based on the password;

if the password is inconsistent with the password, the task processing is ended, and prompt information indicating that the password is wrong can be sent to the terminal.

In a password payment scene, if the password input by the user is verified to be the preset password of the payment account, the payment task can be carried out, namely, the corresponding payment amount is deducted from the payment account; if the password input by the user is incorrect, the payment task is ended, and the server can also send prompt information indicating that the password is incorrect to the terminal.

As another embodiment, in this specification, after the user inputs a password, it may be verified whether the password input by the user is a preset password, and then the user identity is identified based on the input behavior characteristic when the user inputs the password, and it is determined whether the user inputting the password is a real user, specifically, before acquiring the input behavior characteristic when the user inputs the password, the method may further include:

acquiring a password input by the user;

judging whether the password is consistent with a preset password or not;

if the password is inconsistent with the password, the task processing is ended, and prompt information indicating that the password is wrong can be sent to the terminal;

the obtaining of the input behavior characteristics when the user inputs the password specifically includes:

and if so, acquiring the input behavior characteristics when the user inputs the password.

Wherein the target task may be performed after confirming that the user is an actual user of the known account based on the input behavior characteristics when the user inputs the password.

In order to more clearly illustrate the identity recognition method provided in the embodiment of the present specification, a scenario of password payment is described below, fig. 3 is a swim lane diagram of the identity recognition method provided in the embodiment of the present specification, and as shown in fig. 3, the method may include a feature acquisition stage, a determination stage, and a payment stage, and specifically may include:

step 302: the user enters a password in the terminal.

Step 304: the terminal collects operation information of the user when inputting the password, and the operation information can include information such as an input mode, key pressure, key area, key duration, key position and the like adopted when the user inputs the password.

Step 306: based on the operation information collected by the terminal, the server obtains the input behavior characteristics when the user inputs the password.

Step 308: the server calculates the similarity between the input behavior characteristics and preset input behavior characteristics in a preset input behavior characteristic set to obtain the similarity between the input behavior characteristics and each preset input behavior characteristic in the input behavior characteristic set; the preset input behavior feature set comprises at least one preset input behavior feature. The preset input behavior characteristic is a behavior characteristic of inputting a preset password by a real user of the terminal and can represent the real user of the terminal.

Step 310: and judging whether the numerical value of at least one similarity in the similarities of the input behavior features and the preset input behavior features in the preset input behavior feature set is larger than or equal to a first threshold value.

Step 312: and if the numerical value of at least one similarity degree in the similarity degrees of the input behavior features and the preset input behavior features in the preset input behavior feature set is larger than or equal to the first threshold value, determining that the user is a real user of the known account.

Step 314: and if the numerical value of the similarity between the input behavior feature and each preset input behavior feature in the preset input behavior feature set is smaller than the first threshold, executing an identity verification process based on biological information identification for the user.

Wherein, performing an authentication process based on biometric information identification for the user may include:

step 316: and sending indication information for indicating the user to input the biological identity information to the terminal.

Step 318: and the terminal receives the indication information sent by the server.

Step 320: the terminal collects biological identity information.

Step 322: the server acquires the biological identity information acquired by the terminal.

Step 324: judging whether the biological identity information is consistent with preset identity information or not; if yes, the user identity authentication is passed, and the user is determined to be a real user, as shown in step 312; if not, the user identity authentication fails, and a prompt message indicating the failure of the identity authentication can be sent to the terminal, and the payment process is ended if the payment fails, as shown in step 332.

In the embodiment of the present specification, after determining that the user is a real user based on the input behavior characteristics of the password input by the user, the password input by the user may be verified, specifically:

step 326: the server acquires a password input by a user in the terminal, wherein the password can be a character string consisting of at least one character of numbers, letters and symbols.

Step 328: and judging whether the password is consistent with a preset password.

Step 330: and if the password is consistent with the preset password, the password passes the verification, and the payment is finished.

Step 332: if the password is inconsistent with the preset password, the password authentication fails, the payment process is ended, and prompt information indicating that the password is wrong can be sent to the terminal.

Based on the same idea, the embodiment of the present specification further provides a device corresponding to the above method. Fig. 4 is a schematic structural diagram of an identification apparatus provided in an embodiment of the present disclosure. As shown in fig. 4, the apparatus may include:

a characteristic obtaining module 402, configured to obtain an input behavior characteristic when a user inputs a password;

a similarity calculation module 404, configured to calculate a similarity between the input behavior feature and a preset input behavior feature in a preset input behavior feature set, so as to obtain a similarity between the input behavior feature and each preset input behavior feature in the input behavior feature set; the preset input behavior feature set comprises at least one preset input behavior feature;

a determining module 406, configured to determine whether a numerical value of at least one similarity in similarities between the input behavior feature and each preset input behavior feature in the preset input behavior feature set is greater than or equal to a first threshold, so as to obtain a first determination result;

the identity verification module 408 is configured to execute an identity verification process based on biometric information identification for the user if the first determination result indicates that the input behavior feature and a numerical value of similarity of each preset input behavior feature in the preset input behavior feature set are all smaller than the first threshold.

Based on the same idea, the embodiment of the present specification further provides a device corresponding to the above method. Fig. 5 is a schematic structural diagram of an identification device provided in an embodiment of this specification. As shown in fig. 5, the apparatus 500 may include:

at least one processor 510; and the number of the first and second groups,

a memory 530 communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory 530 stores instructions 520 executable by the at least one processor 510 to enable the at least one processor 510 to:

acquiring input behavior characteristics when a user inputs a password;

Based on the same idea, the embodiment of the present specification further provides a computer-readable medium corresponding to the above method. The computer readable medium has stored thereon computer readable instructions executable by a processor to implement the above-described method of identification.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus shown in fig. 5, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to part of the description of the method embodiment.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose logic functions are determined by programming the device by a user. A digital character system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate a dedicated integrated circuit chip. Furthermore, nowadays, instead of manually making an integrated circuit chip, such programming is often implemented by "logic compiler" (software), which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific programming language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced library expression language), ahdl (alternate language description language), traffic, pl (kernel universal programming language), HDCal, JHDL (alternate description language), langva, Lola, HDL, pamm, hardward description language (vhigh description language), and the like, which are currently used by java-language (hardware description language). It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC625D, atmel at91SAM, microchip pic18F26K20, and silicon labsc8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information which can be accessed by a computing device. As defined herein, computer readable media does not include transitory computer readable media (transmyedia) such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. An identity recognition method, comprising:

acquiring input behavior characteristics when a user inputs a password;

2. The method of claim 1, after obtaining the first determination result, further comprising:

3. The method according to claim 1, wherein the input behavior characteristic is a characteristic obtained based on at least one behavior of an input mode, a key pressure, a key area, a key duration and a key position adopted when the user inputs a password at the current time.

4. The method of claim 1, after obtaining the input behavior characteristic when the user inputs the password, further comprising:

5. The method according to claim 1, wherein the preset input behavior feature set is obtained based on historical input behavior features of a real user of a known account when inputting a preset password, and specifically comprises:

6. The method of claim 5, before clustering the historical input behavior features in the set of historical input behavior features, further comprising:

7. The method of claim 5, before clustering the historical input behavior features in the set of historical input behavior features, further comprising:

8. The method according to claim 5, wherein the clustering the historical input behavior features in the historical input behavior feature set specifically includes:

9. The method according to claim 8, after determining that the any one of the historical input behavior features is one of the preset input behavior features in the preset input behavior feature set, further comprising:

10. The method according to claim 8, wherein the calculating the similarity between any one of the historical input behavior features and other historical input behavior features in the historical input behavior feature set specifically includes:

11. The method according to claim 1, wherein the calculating of the similarity between the input behavior feature and a preset input behavior feature in a preset input behavior feature set specifically includes:

based on a cosine similarity calculation method, calculating the similarity between the input behavior feature and a preset input behavior feature in a preset input behavior feature set; the input behavior feature has the same feature dimension as the preset input behavior feature.

12. The method according to claim 1, wherein the performing an authentication procedure based on biometric information recognition for the user specifically comprises:

sending indication information for indicating a user to input biological identity information to a terminal;

13. The method of claim 1, further comprising:

acquiring a password input by the user;

and judging whether the password is consistent with a preset password.

14. An identification device comprising:

15. An identification device comprising:

at least one processor; and the number of the first and second groups,

acquiring input behavior characteristics when a user inputs a password;

16. A computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement the method of identification of any of claims 1 to 13.