CN110929244A - Digital identity identification method, device, equipment and storage medium - Google Patents
Digital identity identification method, device, equipment and storage medium Download PDFInfo
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Abstract
The application provides a digital identity recognition method, a digital identity recognition device, equipment and a storage medium, and relates to the technical field of identity recognition. The method comprises the following steps: obtaining login information and related information of an object to be identified, wherein the related information of the object to be identified comprises: voice information, face information and behavior information; matching relevant information of an object to be recognized according to a preset model, and acquiring a matching result; wherein the preset model comprises: presetting a voice recognition model, a face recognition model and a behavior recognition model; and if the matching is successful, returning a login success instruction. Compared with the prior art, the method and the device avoid the problems that the authentication mode is single, the user identity is easy to forge, and the safety factor is not high.
Description
Technical Field
The present application relates to the field of identity recognition technologies, and in particular, to a digital identity recognition method, apparatus, device, and storage medium.
Background
With the development of science and technology, users can carry out some business operations at home without specially going to banks through the banking system installed on the terminal equipment, so that the life of people is greatly facilitated, but how to ensure the operation safety of the banking system becomes a problem which is most concerned by people.
In a digital transaction scenario, in order to ensure the property security of a user, an existing banking system further obtains user information corresponding to login information after obtaining the login information, for example: face information, voice information and the like, thereby ensuring that the user who logs in the banking system is the user himself.
However, this login method has a single authentication mode, for example: the identity authentication is carried out only through the voice characteristics or only through the face characteristics, so that the identity of the user is easy to forge, and the safety factor is not high.
Disclosure of Invention
An object of the present application is to provide a digital identity recognition method, apparatus, device and storage medium, aiming at the above deficiencies in the prior art, so as to solve the problems in the prior art that the authentication mode is single, the user identity is easy to be forged, and the safety factor is not high.
In order to achieve the above purpose, the technical solutions adopted in the embodiments of the present application are as follows:
in a first aspect, an embodiment of the present application provides a digital identity recognition method, where the method includes:
obtaining login information and related information of an object to be identified, wherein the related information of the object to be identified comprises: voice information, face information and behavior information;
matching relevant information of an object to be recognized according to a preset model, and acquiring a matching result; wherein the preset model comprises: presetting a voice recognition model, a face recognition model and a behavior recognition model;
and if the matching is successful, returning a login success instruction.
Optionally, before the obtaining of the login information and the related information of the object to be identified, the method further includes:
according to the login information, a target voice feature library corresponding to the login information is obtained in a preset voice recognition model;
acquiring a target face feature library corresponding to the login information from the preset face recognition model;
and acquiring a target behavior feature library corresponding to the login information in the preset behavior recognition model.
Optionally, the comparing, according to the preset model, the related information of the object to be recognized includes:
recognizing the voice information of the object to be recognized according to the preset voice recognition model, and judging whether the voice information of the object to be recognized is matched with the voice characteristics in a target voice characteristic library;
according to the preset face recognition model, recognizing the face information of the object to be recognized, and judging whether the face information of the object to be recognized is matched with the face features in a target face feature library;
according to the preset behavior recognition model, performing behavior recognition on the behavior information of the object to be recognized, and judging whether the behavior information of the object to be recognized is matched with behavior characteristics in a target behavior characteristic library or not;
and if the voice information, the face information and the behavior information are successfully matched, returning a matching success instruction.
Optionally, before the target voice feature library corresponding to the login information is acquired in a preset voice recognition model, the method further includes:
establishing a preset voice library in the preset voice recognition model, wherein the preset voice library comprises at least ten languages;
and voice information of a plurality of target objects is collected, and a voice feature library corresponding to each target object is respectively established in the preset voice recognition model according to the collected voice information.
Optionally, before the target face feature library corresponding to the login information is acquired from the preset face recognition model, the method further includes:
the method comprises the steps of collecting face key point information of a plurality of target objects, and respectively establishing face feature libraries corresponding to the target objects in a preset face recognition model according to the collected face key point information.
Optionally, before the target behavior feature library corresponding to the login information is acquired from the preset behavior recognition model, the method further includes:
and acquiring the behavior characteristics of a plurality of target objects, and respectively establishing a behavior characteristic library corresponding to each target object in a preset behavior recognition model according to the acquired behavior characteristics.
In a second aspect, another embodiment of the present application provides a digital identification apparatus, including: the device comprises an acquisition module, a matching module and a login module, wherein:
the acquisition module is used for acquiring login information and relevant information of an object to be identified, wherein the relevant information of the object to be identified comprises: voice information, face information and behavior information;
the matching module is used for matching the relevant information of the object to be recognized according to a preset model and acquiring a matching result; wherein the preset model comprises: presetting a voice recognition model, a face recognition model and a behavior recognition model;
and the login module is used for returning a login success instruction if the matching is successful.
Optionally, the obtaining module is further configured to obtain, according to the login information, a target voice feature library corresponding to the login information in a preset voice recognition model; acquiring a target face feature library corresponding to the login information from the preset face recognition model; and acquiring a target behavior feature library corresponding to the login information in the preset behavior recognition model.
Optionally, the apparatus further comprises: judge module and return module, wherein:
the judging module is used for identifying the voice information of the object to be identified according to the preset voice identification model and judging whether the voice information of the object to be identified is matched with the voice characteristics in the target voice characteristic library or not; according to the preset face recognition model, recognizing the face information of the object to be recognized, and judging whether the face information of the object to be recognized is matched with the face features in a target face feature library; according to the preset behavior recognition model, performing behavior recognition on the behavior information of the object to be recognized, and judging whether the behavior information of the object to be recognized is matched with behavior characteristics in a target behavior characteristic library or not;
and the returning module is used for returning a matching success instruction if the voice information, the face information and the behavior information are matched successfully.
Optionally, the apparatus further comprises: the establishing module is used for establishing a preset voice library in the preset voice recognition model, wherein the preset voice library comprises at least ten languages; and voice information of a plurality of target objects is collected, and a voice feature library corresponding to each target object is respectively established in the preset voice recognition model according to the collected voice information.
Optionally, the establishing module is further configured to acquire face key point information of a plurality of target objects, and respectively establish a face feature library corresponding to each target object in the preset face recognition model according to the acquired face key point information.
Optionally, the establishing module is further configured to collect behavior features of the plurality of target objects, and respectively establish a behavior feature library corresponding to each target object in a preset behavior recognition model according to the collected behavior features.
In a third aspect, another embodiment of the present application provides a digital identity recognition device, including: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, when the digital identity recognition device runs, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to perform the steps of the method according to any one of the first aspect.
In a fourth aspect, another embodiment of the present application provides a storage medium having a computer program stored thereon, where the computer program is executed by a processor to perform the steps of the method according to any one of the above first aspects.
The beneficial effect of this application is: after the login information is acquired, the related information of the object to be recognized is continuously acquired, and the related information of the object to be recognized is matched according to the preset model, wherein the related information of the object to be recognized comprises voice information, face information and behavior information, so that after the related information of the object to be recognized is matched, if the matching is successful, the login information, the voice information, the face information and the behavior information of the current user are all in accordance, the legality of the current login user is determined, and a login success instruction is returned.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic flowchart of a digital identity recognition method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a digital identity recognition method according to another embodiment of the present application;
fig. 3 is a schematic view of a complete process for building a preset model according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a digital identity recognition apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a digital identity recognition apparatus according to another embodiment of the present application;
fig. 6 is a schematic structural diagram of a digital identity recognition apparatus according to another embodiment of the present application;
fig. 7 is a schematic structural diagram of a digital identity recognition device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments.
Fig. 1 is a schematic flow chart of a digital identity recognition method according to an embodiment of the present application, where the digital identity recognition method may be executed by any terminal device such as a mobile phone, a tablet computer, and a wearable device. In an embodiment of the present application, the application scenario is identification of a mobile phone application login of a banking system, but the specific application is not limited thereto, and any occasion that needs identification of a user identity may be used, for example: the method includes the following steps that the identity of a hospital business system login is identified, the identity of a school business system login is identified, the identity of a company business system login is identified, and the like, and the method is not limited in any way, and as shown in fig. 1, the method includes the following steps:
s101: and obtaining login information and related information of the object to be identified.
Wherein, the relevant information of the object to be identified comprises: voice information, face information, and behavior information.
The login information may be a login name and a password of the user, or an identity card number or a mobile phone number of the user, and the setting of the login information is not limited to the above embodiment, and any login information with a uniquely determined user identity may be used.
Optionally, in an embodiment of the present application, taking the terminal device as a mobile phone as an example, a mobile phone camera acquires face information of a current object to be recognized, where the mobile phone camera may be a front-facing camera or a rear-facing camera, and the present application is not limited herein; the method comprises the steps that voice information of a current object to be recognized of a mobile phone is sent to a mobile phone microphone; the banking system installed on the mobile phone collects behavior information of the current object to be identified.
It should be noted that the acquisition of the voice information, the face information and the behavior information all needs to be acquired in real time, and it is not possible to select a shot or recorded picture or voice in an album or a recording folder of a mobile phone, and acquire the related information with an identification object in real time, so that the accuracy of current identification can be ensured, the use of counterfeit information to log in a banking system is prevented, and the use safety of a user is further ensured.
The sequence of acquiring the voice information, the face information and the behavior information of the current object to be recognized is not limited in the application, and only the normal acquisition of the three kinds of information is required.
S102: and matching the relevant information of the object to be recognized according to the preset model, and acquiring a matching result.
And respectively matching the voice information, the face information and the behavior information of the object to be recognized according to a preset model, and acquiring a matching result.
Optionally, in an embodiment of the present application, it is determined that the current object to be recognized is successfully logged in only if the voice information, the face information, and the behavior information of the current object to be recognized are successfully matched; however, it may also be determined that the current object to be identified is successfully logged in if at least two matching types of the three types of information are successfully matched, and the setting of the successful login of the specific object to be identified may be adjusted according to the user's needs.
Wherein, predetermine the model and include: the method comprises the steps of presetting a voice recognition model, a face recognition model and a behavior recognition model.
S103: and if the matching is successful, returning a login success instruction.
The login success instruction is used for indicating that the identity of the current object to be identified passes verification, namely the current object can normally operate on the banking system.
Optionally, if the banking system does not receive the operation instruction of the user within the preset time, after the preset time, the banking system forcibly quits the current login, and when the banking system is operated next time, the login operation needs to be performed again; or when the current banking system is not on the main interface but runs in the background, the logging-out is carried out, and the next time the banking system returns to the main interface of the mobile phone, the logging-in operation still needs to be carried out again; this further ensures the security of the user's banking system.
The preset time is generally set to 1-15 minutes, but may also be set to any other time interval, and the selection of the specific preset time may be adjusted according to the user's needs, which is not limited herein.
By adopting the digital identity recognition method provided by the application, after the login information is acquired, the related information of the object to be recognized is continuously acquired, and the related information of the object to be recognized is matched according to the preset model, wherein the related information of the object to be recognized comprises voice information, face information and behavior information, so that after the related information of the object to be recognized is matched, if the matching is successful, the login information, the voice information, the face information and the behavior information of the current user are all in accordance, the legality of the current login user is determined, and a login success instruction is returned.
Fig. 2 is a schematic flow chart of a digital identity recognition method according to another embodiment of the present application, as shown in fig. 2, before S101, the method further includes:
s104: and acquiring a target voice feature library corresponding to the login information in a preset voice recognition model according to the login information.
The target voice feature library is preset and comprises voice features of target objects corresponding to the current login information, and each target object corresponds to an exclusive target voice feature library.
S105: and acquiring a target face feature library corresponding to the login information from a preset face recognition model.
The target face feature library is preset and comprises face features of target objects corresponding to current login information, and each target object corresponds to an exclusive target face feature library.
S106: and acquiring a target behavior feature library corresponding to the login information in a preset behavior recognition model.
The target behavior feature library is preset and comprises behavior features of target objects corresponding to the current login information, and each target object corresponds to an exclusive target behavior feature library.
Alternatively, S102 may include: and recognizing the voice information of the object to be recognized according to a preset voice recognition model, and judging whether the voice information of the object to be recognized is matched with the voice characteristics in the target voice characteristic library.
The process of recognizing according to the voice information of the object to be recognized is as follows: after voice information collected by a microphone is acquired, firstly, the voice information is analyzed and processed, the processed voice information is compared with voice in a target voice feature library, the similarity between the processed voice information and the voice in the target voice feature library is judged, if the similarity is larger than a preset threshold value, that is, the voice information of the current object to be recognized and the voice information in the target voice feature library are the voice information of the same person, the voice verification of the object to be recognized is passed.
And according to a preset face recognition model, recognizing the face information of the object to be recognized, and judging whether the face information of the object to be recognized is matched with the face features in the target face feature library.
The process of identifying the face information of the object to be identified is as follows: after a face image acquired by a camera is acquired, firstly face detection is carried out, the size and the position of the face in the image are determined, then the detected face is aligned, the alignment operation is used to find several key points (i.e. reference points, such as the corners of the eyes, the tip of the nose, the corners of the mouth, etc.) of the face, then use these corresponding key points to transform the face as much as possible to a standard face through similarity transformation (rotation, scaling and translation), finally model the standardized face image through features, obtaining vectorized face features, judging the similarity between the current face features and the face features in the target face feature library through the classifier, if the similarity exceeds a preset threshold, judging that the matching is successful, that is, the face information of the current object to be recognized and the face information in the target face feature library are face information of the same person, which indicates that the face verification of the object to be recognized passes.
And according to a preset behavior recognition model, performing behavior recognition on the behavior information of the object to be recognized, and judging whether the behavior information of the object to be recognized is matched with the behavior characteristics in the target behavior characteristic library.
Wherein the behavior collection may include any one of: for example, when bank transaction is realized through a banking system, the transaction behavior, access interface, key operation log, preference function field distribution and the like of the current user are realized. After the current behavior of the object to be recognized is collected, the current behavior is compared with the behavior in the preset target behavior feature library to judge whether the current behavior is matched with the behavior in the preset target behavior feature library, if the matching is successful, the behavior information of the current object to be recognized and the behavior information in the target behavior feature library are the face information of the same person, and the behavior verification of the object to be recognized is passed.
The judgment sequence of the voice information, the face information and the behavior information is not limited in the application, and can be adjusted according to the needs of the user.
And if the voice information, the face information and the behavior information are successfully matched, returning a matching success instruction.
Fig. 3 is a schematic view of a complete process for establishing a preset model according to an embodiment of the present application, and as shown in fig. 3, the establishing of the preset model includes:
s201: and loading a preset algorithm.
Wherein, the preset algorithm comprises: speech recognition algorithm 201a, face recognition algorithm 201b and behavior recognition algorithm 201 c.
S202: a sample of the object to be identified is collected.
Wherein the sample collection comprises: the method comprises the steps of collecting a voice sample of an object to be recognized, collecting a face sample of the object to be recognized and collecting a behavior sample of the object to be recognized.
Then, according to the collected samples, respectively:
s203 a: and (5) voice recognition comparison. And the voice sample is used for comparing and matching the voice sample with the voice characteristics in the preset target voice characteristic library.
S203 b: and (5) face recognition and comparison. And the face sample is used for comparing and matching the face sample with the face features in the preset target face feature library.
S203 c: and (5) behavior recognition comparison. And the behavior sample is used for comparing and matching the behavior sample with the behavior characteristics in the preset target behavior characteristic library.
The specific identification and comparison manner is shown in the above embodiments, and is not described herein again.
After the comparison result is acquired, executing S204: the load server cluster distributes the comparison results.
And if the banking business system receives that all the characteristic identifications pass, the load server returns a login success instruction to the user.
Optionally, the process of sending the result to the banking system by the load server may be sequentially sending the result to the banking system, or may be returning all the comparison results to the banking system at one time after receiving the comparison results returned by all the backgrounds, where a specific return mode of the comparison results may be set according to a user requirement, and the present application is not limited thereto.
Subsequently, S205 is executed: and (4) storing a database. The whole process is ended.
If the comparison and matching result shows that the comparison and matching is successful, storing the current sample data into the corresponding feature library, for example: if the current voice sample information to be recognized is successfully displayed after being compared and matched, storing the voice sample information into a preset target voice feature library corresponding to the current pair of poplars to be recognized; the arrangement can ensure that the more user characteristics are stored in the background preset characteristic library of the banking business along with the longer time that the user uses the banking business system, so that the verification of the user identity is more accurate.
Optionally, the method further comprises: establishing a preset voice library in a preset voice recognition model, wherein the preset voice library comprises at least ten languages; collecting voice information of a plurality of target objects, and respectively establishing a voice feature library corresponding to each target object in a preset voice recognition model according to the collected voice information.
Optionally, the preset speech library supports multiple languages, and in an embodiment of the present application, the preset speech library includes top ten languages of the number of people using world languages: although the specific languages are not limited to the above embodiments, the specific languages may be adjusted according to the needs of the user, and the present application is not limited thereto.
Optionally, after the preset voice library is established, multiple times of voice acquisition are performed on different user groups through a banking system, acquired data are transmitted to a system background through a hypertext Transfer Protocol (HTTP), and are distributed to a specific wireless access point AP server through a complex equalization server, the acquired voice information is analyzed and processed, and a standard voice library corresponding to each user is established in the preset voice database to establish a voice feature library corresponding to each user. Each user has a corresponding voice feature library in a preset voice library, and one voice feature library only comprises voice features of one user.
The method comprises the steps of collecting face key point information of a plurality of target objects, and respectively establishing a face feature library corresponding to each target object in a preset face recognition model according to the collected face key point information.
Optionally, according to the face photo images of a plurality of target objects collected by the camera or each frame image in a plurality of sections of face videos collected, the face key point information in the images is extracted, and a face feature library corresponding to the current target object is established. Each user has a corresponding face feature library in a preset face recognition model, and one face feature library only comprises the face features of one user.
The behavior characteristics of a plurality of target objects are collected, and behavior characteristic libraries corresponding to the target objects are respectively established in a preset behavior recognition model according to the collected behavior characteristics.
The behavior feature library is established after a user uses the banking system for a period of time, and is established by collecting behavior habits of the user in the process of using the banking system.
Optionally, the establishment process of the voice feature library, the face feature library and the behavior feature library may be established after a user initially registers the banking system, or activates the banking system, that is, in a first use state of the user, the banking system sends a prompt to "please complete acquisition of related information", and then the face information, the voice information and the behavior information of the user are respectively acquired through a camera, a microphone and a banking system interface, wherein the face information may continuously acquire a plurality of face photos at different angles, or at least one section of face video including a plurality of different angles; the voice information can continuously collect at least one section of voice information of the user; however, the sequence of information collection is not limited in this application, and can be adjusted according to the user's needs.
The behavior feature library is established after the user uses the bank business system for a period of time, so that the behavior information does not need to be verified when the user uses the bank business system for a plurality of times, and the behavior information is verified after the behavior feature library is established.
Fig. 4 is a schematic structural diagram of a digital identity recognition apparatus according to an embodiment of the present application, and as shown in fig. 4, the apparatus includes: an obtaining module 301, a matching module 302 and a logging module 303, wherein:
an obtaining module 301, configured to obtain login information and relevant information of an object to be identified, where the relevant information of the object to be identified includes: voice information, face information, and behavior information.
The matching module 302 is configured to match the relevant information of the object to be recognized according to a preset model, and obtain a matching result; wherein, predetermine the model and include: the method comprises the steps of presetting a voice recognition model, a face recognition model and a behavior recognition model.
And the login module 303 is configured to return a login success instruction if the matching is successful.
Optionally, the obtaining module 301 is further configured to obtain, according to the login information, a target voice feature library corresponding to the login information in a preset voice recognition model; acquiring a target face feature library corresponding to login information from a preset face recognition model; and acquiring a target behavior feature library corresponding to the login information in a preset behavior recognition model.
Fig. 5 is a schematic structural diagram of a digital identity recognition apparatus according to an embodiment of the present application, and as shown in fig. 5, the apparatus further includes: a decision block 304 and a return block 305, wherein:
the judging module 304 is configured to recognize the voice information of the object to be recognized according to a preset voice recognition model, and judge whether the voice information of the object to be recognized matches with the voice features in the target voice feature library; according to a preset face recognition model, recognizing face information of an object to be recognized, and judging whether the face information of the object to be recognized is matched with face features in a target face feature library or not; and according to a preset behavior recognition model, performing behavior recognition on the behavior information of the object to be recognized, and judging whether the behavior information of the object to be recognized is matched with the behavior characteristics in the target behavior characteristic library.
And a returning module 305, configured to return a matching success instruction if the voice information, the face information, and the behavior information are all successfully matched.
Fig. 6 is a schematic structural diagram of a digital identity recognition apparatus according to an embodiment of the present application, and as shown in fig. 6, the apparatus further includes: an establishing module 306, configured to establish a preset speech library in the preset speech recognition model, where the preset speech library includes at least ten languages; collecting voice information of a plurality of target objects, and respectively establishing a voice feature library corresponding to each target object in a preset voice recognition model according to the collected voice information.
Optionally, the establishing module 306 is further configured to collect face key point information of a plurality of target objects, and respectively establish a face feature library corresponding to each target object in a preset face recognition model according to the collected face key point information.
Optionally, the establishing module 306 is further configured to collect behavior features of a plurality of target objects, and respectively establish a behavior feature library corresponding to each target object in the preset behavior recognition model according to the collected behavior features.
The above-mentioned apparatus is used for executing the method provided by the foregoing embodiment, and the implementation principle and technical effect are similar, which are not described herein again.
These above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when one of the above modules is implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
Fig. 7 is a schematic structural diagram of a digital identity recognition device according to an embodiment of the present disclosure, where the digital identity recognition device may be integrated in a terminal device or a chip of the terminal device.
The digital identity recognition device includes: a processor 501, a storage medium 502, and a bus 503.
The processor 501 is used for storing a program, and the processor 501 calls the program stored in the storage medium 502 to execute the method embodiment corresponding to fig. 1-2. The specific implementation and technical effects are similar, and are not described herein again.
Optionally, the present application also provides a program product, such as a storage medium, on which a computer program is stored, including a program, which, when executed by a processor, performs embodiments corresponding to the above-described method.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to perform some steps of the methods according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Claims (10)
1. A digital identity recognition method, the method comprising:
obtaining login information and related information of an object to be identified, wherein the related information of the object to be identified comprises: voice information, face information and behavior information;
matching relevant information of an object to be recognized according to a preset model, and acquiring a matching result; wherein the preset model comprises: presetting a voice recognition model, a face recognition model and a behavior recognition model;
and if the matching is successful, returning a login success instruction.
2. The method of claim 1, wherein before obtaining the login information and the information related to the object to be identified, the method further comprises:
according to the login information, a target voice feature library corresponding to the login information is obtained in a preset voice recognition model;
acquiring a target face feature library corresponding to the login information from the preset face recognition model;
and acquiring a target behavior feature library corresponding to the login information in the preset behavior recognition model.
3. The method according to claim 1, wherein comparing the related information of the object to be recognized according to the preset model comprises:
recognizing the voice information of the object to be recognized according to the preset voice recognition model, and judging whether the voice information of the object to be recognized is matched with the voice characteristics in a target voice characteristic library;
according to the preset face recognition model, recognizing the face information of the object to be recognized, and judging whether the face information of the object to be recognized is matched with the face features in a target face feature library;
according to the preset behavior recognition model, performing behavior recognition on the behavior information of the object to be recognized, and judging whether the behavior information of the object to be recognized is matched with behavior characteristics in a target behavior characteristic library or not;
and if the voice information, the face information and the behavior information are successfully matched, returning a matching success instruction.
4. The method of claim 2, wherein before the obtaining of the target voice feature library corresponding to the login information in a preset voice recognition model, the method further comprises:
establishing a preset voice library in the preset voice recognition model, wherein the preset voice library comprises at least ten languages;
and voice information of a plurality of target objects is collected, and a voice feature library corresponding to each target object is respectively established in the preset voice recognition model according to the collected voice information.
5. The method as claimed in claim 2, wherein before the obtaining of the target face feature library corresponding to the login information in the preset face recognition model, the method further comprises:
the method comprises the steps of collecting face key point information of a plurality of target objects, and respectively establishing face feature libraries corresponding to the target objects in a preset face recognition model according to the collected face key point information.
6. The method of claim 2, wherein before the obtaining of the target behavior feature library corresponding to the login information in the preset behavior recognition model, the method further comprises:
the behavior characteristics of a plurality of target objects are collected, and behavior characteristic libraries corresponding to the target objects are respectively established in a preset behavior recognition model according to the collected behavior characteristics.
7. A digital identification device, the device comprising: the device comprises an acquisition module, a matching module and a login module, wherein:
the acquisition module is used for acquiring login information and relevant information of an object to be identified, wherein the relevant information of the object to be identified comprises: voice information, face information and behavior information;
the matching module is used for matching the relevant information of the object to be recognized according to a preset model and acquiring a matching result; wherein the preset model comprises: presetting a voice recognition model, a face recognition model and a behavior recognition model;
and the login module is used for returning a login success instruction if the matching is successful.
8. The apparatus of claim 7, wherein the obtaining module is further configured to obtain, according to the login information, a target voice feature library corresponding to the login information in a preset voice recognition model; acquiring a target face feature library corresponding to the login information from the preset face recognition model; and acquiring a target behavior feature library corresponding to the login information in the preset behavior recognition model.
9. A digital identification device, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the digital identity recognition device is operating, the processor executing the machine-readable instructions to perform the method of any of claims 1-6.
10. A storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, performs the method of any of the preceding claims 1-6.
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