CN112232443B - Identity authentication method, device, equipment and storage medium - Google Patents
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Abstract
The embodiment of the application provides an identity authentication method, an identity authentication device, identity authentication equipment and a storage medium, wherein the method comprises the following steps: acquiring at least two target features of a target object, wherein each target feature is matched with two target similarity thresholds; comparing the at least two target features with features in a feature library according to the two target similarity thresholds corresponding to the at least two target features to obtain at least two feature sets to be selected; and determining whether the identity of the target object is successfully authenticated or not through a preset combination strategy according to the object identifications and the first similarity thresholds in the at least two feature sets to be selected. The method provided by the embodiment of the application can overcome the defect that the identity authentication in the prior art has no flexibility, and further can not effectively provide better service for users.
Description
Technical Field
The embodiment of the application relates to the technical field of identity authentication, in particular to an identity authentication method, an identity authentication device, identity authentication equipment and an identity authentication storage medium.
Background
With the development of the age, people pay more and more attention to the safety of a closed environment. In order to ensure environmental security, identity authentication of personnel entering a closed environment is often performed by using a coded lock. Since everyone has unique biological characteristics, user authentication based on human body biological characteristic recognition technology has become a trend. People can carry out identity verification through individual biological characteristic information such as faces, fingerprints, irises, voiceprints and the like, and great convenience is provided for life of people.
Currently, the authentication method may be single feature authentication or multiple feature authentication, but in the authentication method of the prior art, all individuals using the authentication need to provide the set features. Such as a combination of iris and voiceprint, then all individuals using authentication need to provide iris and voiceprint. But may be due to factors that may prevent some individuals from providing an iris or voiceprint, thereby preventing such individuals from being authenticated.
Therefore, the identity authentication in the prior art has no flexibility, and further, better service cannot be effectively provided for the user.
Disclosure of Invention
The embodiment of the application provides an identity authentication method, an identity authentication device, identity authentication equipment and a storage medium, which are used for overcoming the defect that the identity authentication in the prior art is inflexible and further cannot effectively provide better service for users.
In a first aspect, an embodiment of the present application provides an identity authentication method, including:
acquiring at least two target features of a target object, wherein each target feature is matched with two target similarity thresholds;
comparing the at least two target features with features in a feature library according to the two target similarity thresholds corresponding to the at least two target features to obtain at least two feature sets to be selected, wherein each feature set to be selected comprises target information of at least one feature to be selected corresponding to a type of feature, and the target information comprises an object identifier corresponding to the feature to be selected and a first similarity threshold of the target feature and the feature to be selected;
And determining whether the identity of the target object is successfully authenticated or not through a preset combination strategy according to each object identifier in the at least two feature sets to be selected and each first similarity threshold.
In one possible design, the determining, according to each of the object identifiers in the at least two feature sets to be selected and each of the first similarity thresholds, whether the identity of the target object is successfully authenticated by a preset combination policy includes:
according to the object identifiers in the at least two feature sets to be selected, acquiring the first similarity threshold corresponding to each feature set to be selected of the same object from the at least two feature sets to be selected;
aiming at the same object, comparing each combination corresponding to each feature to be selected with a preset combination strategy according to the first similarity threshold corresponding to each feature to be selected in the at least two feature sets to be selected, and determining whether target combinations conforming to the preset combination strategy exist in each combination corresponding to each feature to be selected;
and if the target combinations which accord with the preset combination strategies exist in the combinations corresponding to the features to be selected, determining that the identity authentication of the target object is successful.
In one possible design, the preset combination strategy includes a plurality of preset combinations corresponding to different types of features and similarity threshold comparison conditions corresponding to the features in each preset combination;
comparing each combination corresponding to each feature to be selected with a preset combination policy according to the first similarity threshold corresponding to each feature to be selected in the at least two feature sets to be selected, and determining whether a target combination conforming to the preset combination policy exists in each combination corresponding to each feature to be selected, including:
according to the at least two feature sets to be selected, obtaining target combinations consistent with target preset combinations in the plurality of preset combinations from each combination corresponding to each feature to be selected;
judging whether the first similarity threshold corresponding to each feature to be selected in the target combination meets a similarity threshold comparison condition corresponding to each feature in the target preset combination.
In one possible design, if there is a target combination conforming to the preset combination policy in each combination corresponding to each feature to be selected, determining that the identity authentication of the target object is successful includes:
If the first similarity threshold values corresponding to at least two features to be selected in the target combination meet the similarity threshold value comparison condition corresponding to each feature in the target preset combination, determining that the identity authentication of the target object is successful;
wherein, the object corresponding to the target combination is the target object.
In one possible design, the acquiring at least two target features of the target object includes:
if the target object is detected, an identity recognition function is started, wherein the identity recognition function is used for acquiring at least two target characteristics of voiceprint, face characteristics, fingerprint and password of the target object in real time.
In one possible design, the feature library includes a plurality of feature sets, each feature set includes information of at least one feature corresponding to a type of feature, and the information of the feature includes a feature and an object identifier corresponding to the feature;
comparing the at least two target features with features in a feature library according to the two target similarity thresholds corresponding to the at least two target features to obtain at least two feature sets to be selected, including:
for each target feature in the at least two target features, acquiring a first feature set to which the target feature belongs from a plurality of feature sets in the feature library, wherein the number of the first feature sets is at least two;
Comparing the target feature with each first feature in the first feature set aiming at each first feature set to obtain a second similarity threshold of the target feature and each first feature in the first feature set;
and acquiring a first target similarity threshold with a low threshold value from the two target similarity thresholds corresponding to the target features, and determining at least two feature sets to be selected according to the first target similarity threshold, each second similarity threshold and each first feature set corresponding to the at least two target features.
In one possible design, the determining at least two feature sets to be selected according to the first target similarity threshold value, each second similarity threshold value, and each first feature set corresponding to the at least two target features includes:
comparing each second similarity threshold with the first target similarity threshold corresponding to the target feature for each first feature set, and acquiring at least one second feature with the second similarity threshold larger than or equal to the first target similarity threshold from the first features;
Obtaining object identifiers corresponding to the at least one second feature from each first feature set, and generating the at least two feature sets to be selected according to a second similarity threshold corresponding to the at least one second feature and the object identifiers corresponding to the at least one second feature;
the second similarity threshold corresponding to the at least one second feature is a first similarity threshold corresponding to each of the at least two candidate features in the at least two candidate feature sets.
In a second aspect, an embodiment of the present application provides an identity authentication device, including:
the feature acquisition module is used for acquiring at least two target features of the target object, and each target feature is matched with two target similarity thresholds;
the similarity analysis module is used for comparing the at least two target features with features in a feature library according to the two target similarity thresholds corresponding to the at least two target features to obtain at least two feature sets to be selected, wherein each feature set to be selected comprises target information of at least one feature to be selected corresponding to a type of feature, and the target information comprises an object identifier corresponding to the feature to be selected and a first similarity threshold of the target feature and the feature to be selected;
And the identity authentication module is used for determining whether the identity of the target object is successfully authenticated or not through a preset combination strategy according to each object identifier and each first similarity threshold. The feature acquisition module is used for acquiring at least two target features of the target object, and each target feature is matched with two target similarity thresholds;
the similarity analysis module is used for comparing the at least two target features with features in a feature library according to the two target similarity thresholds corresponding to the at least two target features to obtain at least two feature sets to be selected, wherein each feature set to be selected comprises target information of at least one feature to be selected corresponding to a type of feature, and the target information comprises an object identifier corresponding to the feature to be selected and a first similarity threshold of the target feature and the feature to be selected;
and the identity authentication module is used for determining whether the identity of the target object is successfully authenticated or not through a preset combination strategy according to each object identifier in the at least two feature sets to be selected and each first similarity threshold.
In a third aspect, an embodiment of the present application provides an identity authentication device, including: at least one processor and memory;
The memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored in the memory, such that the at least one processor performs the authentication method as described above in the first aspect and possible designs of the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, where computer executable instructions are stored, and when executed by a processor, implement the identity authentication method according to the first aspect and the possible designs of the first aspect.
The identity authentication method, the identity authentication device, the identity authentication equipment and the storage medium provided by the embodiment firstly acquire at least two target characteristics of a target object, wherein each target characteristic is matched with two target similarity thresholds; comparing the at least two target features with features in a feature library according to the two target similarity thresholds corresponding to the at least two target features to obtain at least two feature sets to be selected, wherein each feature set to be selected comprises target information of at least one feature to be selected corresponding to a type of feature, and the target information comprises an object identifier corresponding to the feature to be selected and a first similarity threshold of the target feature and the feature to be selected; and determining whether at least two feature sets to be selected contain the target object or not through a preset combination strategy according to each object identifier and each first similarity threshold value, further determining whether the identity of the target object is successfully authenticated or not, and meeting the requirement of diversification through the identity verification of a plurality of different combination features, so that the verification process is more flexible and quick, the feature combination verification can ensure the verification accuracy, and better service can be provided for users effectively.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it will be obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a schematic view of a scenario of an identity authentication method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of an identity authentication method according to an embodiment of the present application;
FIG. 3 is a flowchart illustrating an identity authentication method according to another embodiment of the present application;
FIG. 4 is a flowchart illustrating an identity authentication method according to another embodiment of the present application;
fig. 5 is a schematic structural diagram of an identity authentication device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an identity authentication device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented, for example, in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Currently, the identity authentication method can be single feature authentication or multiple feature authentication, and the identity authentication method aims at single feature authentication: easy counterfeiting and low safety; verification for multiple features: because of sequential authentication, authentication fails as long as one feature authentication is unsuccessful. Many biological features are often greatly affected by the environment, such as fingerprints are prone to error in dry environments or after labor; the requirements of the face and the iris on the light rays are high. And the processing time is long because the operation is sequential. Thus, in the authentication method of the prior art, all individuals using authentication need to provide the set feature. Such as a combination of iris and voiceprint, then all individuals using authentication need to provide iris and voiceprint. But may be due to factors that may prevent some individuals from providing an iris or voiceprint, thereby preventing such individuals from being authenticated. Therefore, the identity authentication in the prior art has no flexibility, and further, better service cannot be effectively provided for the user.
In order to solve the problems, the technical idea of the application is as follows: aiming at the situation that some biological characteristics cannot be provided due to objective reasons, the provided characteristics can be selected from a plurality of characteristics to be combined, so that the identity authentication can meet the requirement of more diversification; in the authentication process, various features are not sequentially authenticated, and after the authentication is respectively performed, the authentication is combined according to the authenticated level to check whether the requirements of a combination strategy are met, so that the authentication process is more flexible and rapid; and then, ensuring that other accidentally acquired features do not interfere with the final identity verification result by recording the features set by each user, and ensuring the accuracy.
Referring to fig. 1, fig. 1 is a schematic view of a scenario of an identity authentication method according to an embodiment of the present application. The identity authentication device in fig. 1 may be a smart entry device 10, where the smart entry device 10 may collect various features or classes of features, including, for example, fingerprints, irises, voiceprints, faces, passwords, etc.
In practical application, the identity authentication device comprises a key password, a camera, a fingerprint identification module, a microphone and the like, and when identity recording is carried out, various characteristics are selected according to the actual condition of an operator, and the characteristics selected by the operator are recorded. Such as: the disabled can select voiceprint and voice password; the child can select voiceprints and facial features; the deaf-mute can select the face and the fingerprint; the old can select the face to add the traditional password; the general logger can choose arbitrarily. Then, two features selected by the user are recorded, for example, user a selects feature F1 and feature F2, and further, features F1 and F2 are recorded, and each feature is classified and stored in the database.
When the intelligent access control device 10 is used, the intelligent access control device detects the surrounding environment of the access control in real time, detects whether a human body (namely, a target object 20) approaches, and starts operations such as picking up sound and capturing human faces after the detection module detects information of the approach of the human body. The identity of the target object is then verified based on the at least two detected target features of the target object 20. The specific procedure may be illustrated in detail by the following examples.
The technical scheme of the application is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
Referring to fig. 2, fig. 2 is a flow chart of an identity authentication method according to an embodiment of the present application.
Referring to fig. 2, the identity authentication method includes:
s201, at least two target characteristics of the target object are obtained.
Wherein each target feature matches two target similarity thresholds.
In this embodiment, the executing entity of the method may be an identity authentication device. Firstly, the combination characteristics of personnel with authority to enter a target area are recorded in the identity authentication equipment, and the combination characteristics can be selected and recorded according to the personnel.
In one possible design, the acquiring at least two target features of the target object may include:
if the target object is detected, an identity recognition function is started, wherein the identity recognition function is used for acquiring at least two target characteristics of voiceprint, face characteristics, fingerprint and password of the target object in real time.
In practical application, after a detection module in the identity authentication equipment detects a message that a human body (namely a target object) approaches, operations such as picking up sound and capturing a human face are started, at least two characteristics of the target object are collected, the collected characteristics may not exist in the recorded characteristics, but the successful authentication of the identity can be realized only by ensuring that the combination of at least two characteristics exists in all the collected characteristics in the recorded combined characteristics. Therefore, other accidentally collected characteristics can be guaranteed not to interfere with the final authentication result, and accuracy is guaranteed.
Two target similarity thresholds are configured for each type of feature, wherein the two target similarity thresholds include a first similarity threshold level1 and a first similarity threshold level2, and the level1 is smaller than the level2.
In addition, the purpose of configuring the target similarity threshold can be to preliminarily screen out target features, which are acquired in real time, of which the similarity threshold of features in the feature library is lower than level1, so that time and resources are wasted in subsequent feature comparison and strategy comparison are avoided.
S202, comparing the at least two target features with features in a feature library according to the two target similarity thresholds corresponding to the at least two target features to obtain at least two feature sets to be selected.
Each feature set to be selected comprises target information of at least one feature to be selected corresponding to a type of feature, wherein the target information comprises an object identifier corresponding to the feature to be selected and a first similarity threshold value of the target feature and the feature to be selected.
In this embodiment, the database stores multiple types of features, and each type of feature forms a feature set. And aiming at each target feature, acquiring a feature set of the category to which the target feature belongs from a database, respectively comparing the target feature with each feature in the corresponding feature set to obtain a similarity threshold value of the target feature and each feature in the corresponding feature set, selecting a feature meeting a condition from the feature set, and generating a set to be selected according to a target object identifier corresponding to the feature meeting the condition and the corresponding similarity threshold value.
S203, determining whether the identity of the target object is successfully authenticated or not through a preset combination strategy according to the object identifications and the first similarity thresholds in the at least two feature sets to be selected.
In this embodiment, according to the object identifier included in each of the at least two feature sets to be selected, the same object of the feature set to be selected from the at least two feature sets to be selected with different sources is selected, then a combined feature is formed based on the same object, then at least two target features are freely combined to form at least one target combination, and each target combination includes the object identifiers corresponding to the at least two target features and the first similarity threshold. Comparing the target combinations with a preset combination strategy based on the plurality of target combinations, and if the target combinations are in accordance with the preset combination strategy, indicating that target objects exist in at least two candidate sets, namely indicating that the identity authentication of the target objects is successful, controlling the intelligent access control equipment to open and opening the access control.
According to the identity authentication method provided by the embodiment, at least two target characteristics of a target object are obtained, wherein each target characteristic is matched with two target similarity thresholds; comparing the at least two target features with features in a feature library according to the two target similarity thresholds corresponding to the at least two target features to obtain at least two feature sets to be selected, wherein each feature set to be selected comprises target information of at least one feature to be selected corresponding to a type of feature, and the target information comprises an object identifier corresponding to the feature to be selected and a first similarity threshold of the target feature and the feature to be selected; and determining whether at least two feature sets to be selected contain the target object or not through a preset combination strategy according to each object identifier and each first similarity threshold value, further determining whether the identity of the target object is successfully authenticated or not, and meeting the requirement of diversification through the identity verification of a plurality of different combination features, so that the verification process is more flexible and quick, the feature combination verification can ensure the verification accuracy, and better service can be provided for users effectively.
Referring to fig. 3, fig. 3 is a flowchart of an identity authentication method according to another embodiment of the present application, and S202 is described in detail based on the above embodiment, for example, the embodiment shown in fig. 2. The feature library comprises a plurality of feature sets, each feature set comprises information of at least one feature corresponding to one type of feature, and the information of the feature comprises the feature and an object identifier corresponding to the feature.
The comparing the at least two target features with features in a feature library according to the two target similarity thresholds corresponding to the at least two target features to obtain at least two feature sets to be selected may include:
s301, acquiring a first feature set of each target feature from a plurality of feature sets in the feature library, wherein the number of the first feature sets is at least two, aiming at each target feature in the at least two target features.
S302, comparing the target feature with each first feature in the first feature set aiming at each first feature set, and obtaining a second similarity threshold of the target feature and each first feature in the first feature set.
S303, acquiring a first target similarity threshold with a low threshold value from the two target similarity thresholds corresponding to the target features, and determining at least two feature sets to be selected according to the first target similarity threshold, each second similarity threshold and each first feature set.
In this embodiment, taking one target feature as an example, the other target features may be repeated by: and searching a feature set matched with the target feature, namely a first feature set, namely a feature set of the category to which the target feature belongs, from a feature library, comparing the target feature with each feature in the feature set, and calculating a similarity threshold value between the target feature and each feature in the feature set, namely a second similarity threshold value.
Each feature set contains information of at least one feature corresponding to one type of feature, and the information of each feature comprises corresponding features and object identifiers corresponding to the features. Can be represented by a binary group (ID, characteristic)
Specifically, each target feature is configured with two target similarity thresholds, a first target similarity threshold and a second target similarity threshold, wherein the first target similarity threshold is less than the second target similarity threshold. After a second similarity threshold value between the target feature and each feature in the feature set is obtained through calculation, combining the feature corresponding to the second similarity threshold value in the first feature set, the object identifier corresponding to the feature and the first target similarity threshold value corresponding to the feature, and obtaining a feature set to be selected corresponding to the target feature based on a feature library. Since the target features are at least two, the number of the feature sets to be selected may be identical to the number of the target features, or less than the number of the target features, and needs to be determined according to specific comparison results.
For example, if one target feature is a target fingerprint feature, a fingerprint set is obtained from a feature library, where the fingerprint set includes information of a plurality of fingerprint features, and the information of each fingerprint feature includes a fingerprint feature and an object identifier corresponding to the fingerprint feature, which may be represented by a binary group (ID, fingerprint feature). And comparing the target fingerprint features with each fingerprint feature in the fingerprint set respectively, calculating to obtain a second similarity threshold which is a similarity threshold of the target fingerprint features and each fingerprint feature, and generating a feature set to be selected according to the second similarity threshold, the information of the fingerprint features and the obtained lower target similarity threshold which is a first target similarity threshold and is matched with the target fingerprint features. And similarly, the other target features can realize the determination of other feature sets to be selected by repeating the steps.
It should be noted that the generated feature set to be selected may be an empty set, that is, no feature matching the target feature is found in the feature library.
In one possible design, S303 is described in detail on the basis of the above embodiment, for example, on the basis of the embodiment shown in fig. 3. The determining at least two feature sets to be selected according to the first target similarity threshold, each second similarity threshold and each first feature set corresponding to the at least two target features may be implemented by:
Step a1, comparing each second similarity threshold with the first target similarity threshold corresponding to the target feature for each first feature set, and obtaining at least one second feature with the second similarity threshold greater than or equal to the first target similarity threshold from the first features.
Step a2, obtaining object identifiers corresponding to the at least one second feature from each first feature set, and generating the at least two feature sets to be selected according to a second similarity threshold corresponding to the at least one second feature and the object identifiers corresponding to the at least one second feature.
Wherein the second similarity threshold corresponding to the at least one second feature is a first similarity threshold corresponding to each of the respective candidate features in the at least two candidate feature sets
In this embodiment, taking one first feature set as an example, the other first feature sets may be repeated by: and comparing each second similarity threshold value obtained by comparing the target feature with each feature in the first feature set matched with the target feature with the first target similarity threshold value, discarding the information of the feature corresponding to the second similarity threshold value lower than the first target similarity threshold value, and reserving the information of the feature corresponding to the second similarity threshold value which is greater than or equal to the first target similarity threshold value and corresponds to the first feature set. The object identifier to be reserved and the corresponding second similarity threshold value are used as the binary group (ID, second similarity threshold value) to generate a feature set to be selected.
The feature meeting the retention condition is a feature to be selected, and the second similarity threshold corresponding to the feature meeting the retention condition is a second similarity threshold corresponding to the feature to be selected.
Illustratively, after a certain feature is received, the features are compared with a corresponding feature library. A result set (i.e., a candidate set) of the plurality of features is obtained. Multiple features may be acquired simultaneously or sequentially. If three features f1, f2 and f3 exist, the verification results are set s1, set s2 and set s3 respectively, each set is formed into a binary group (ID, level), and the level represents a similarity threshold, that is, the safety level assessment result of the similarity score of the feature fi on a certain input individual IDi after threshold comparison. Such as: and comparing the acquired fingerprint features to obtain a result set (ID 1, level 1), (ID 2, level 2) }, wherein level1 is larger than the highest fingerprint similarity threshold, level2 is larger than the similarity threshold to be determined for verification, and level1 is larger than level2, which means that the fingerprint similarity with ID1 exceeding the highest fingerprint similarity threshold is the verification success level, and the similarity of ID2 only exceeds the similarity threshold to be determined for verification. Note that only if at least the verification-pending similarity threshold is exceeded will it appear in the result set.
Referring to fig. 4, fig. 4 is a flowchart illustrating an identity authentication method according to another embodiment of the present application. The present embodiment describes S203 in detail based on the above embodiment, for example, based on the embodiment described in fig. 2. The determining whether the identity of the target object is successfully authenticated according to the object identifiers in the at least two feature sets to be selected and the first similarity thresholds through a preset combination policy may include:
s401, according to the object identifiers in the at least two feature sets to be selected, obtaining the first similarity threshold corresponding to each feature set to be selected of the same object from the at least two feature sets to be selected.
In this embodiment, objects containing the same object identifier are searched from at least two feature sets to be selected, and then, according to the contained same object identifier, a first similarity threshold corresponding to the contained same object identifier is obtained from the feature sets to be selected.
S402, comparing each combination corresponding to each feature to be selected with a preset combination strategy according to the first similarity threshold corresponding to each feature to be selected in the at least two feature sets to be selected aiming at the same object, and determining whether target combinations conforming to the preset combination strategy exist in each combination corresponding to each feature to be selected.
In this embodiment, taking one object containing the same object identifier as an example, other objects containing the same object identifier may repeat the following steps: and performing free combination on each feature to be selected, comparing each combination of the free combination with a preset combination strategy based on each first similarity threshold, and determining whether each combination contains a combination conforming to the preset combination strategy.
In one possible design, S402 is described in detail on the basis of the above embodiment. The preset combination strategy comprises a plurality of preset combinations corresponding to different types of features and similarity threshold comparison conditions corresponding to the features in each preset combination. S402 may be implemented by:
step b1, according to the at least two feature sets to be selected, obtaining target combinations consistent with target preset combinations in the plurality of preset combinations from each combination corresponding to each feature to be selected;
and b2, judging whether the first similarity threshold corresponding to each feature to be selected in the target combination meets a similarity threshold comparison condition corresponding to each feature in the target preset combination.
In this embodiment, any feature to be selected of the same object is selected from each feature to be selected set to perform free combination, then according to each combination of the free combinations, whether a target combination matched with any one of a plurality of preset combinations is determined, and then according to whether a first similarity threshold corresponding to the feature to be selected in the target combination meets a combination policy of the preset combination matched with the first similarity threshold corresponding to the feature to be selected in the target combination, namely, whether the first similarity threshold corresponding to the feature to be selected in the target combination meets a similarity threshold comparison condition corresponding to each feature in the preset combination matched with the first similarity threshold corresponding to the feature to be selected in the target combination.
S403, if target combinations conforming to the preset combination strategies exist in the combinations corresponding to the features to be selected, determining that the identity authentication of the target object is successful.
In this embodiment, if there is a target combination conforming to the preset combination policy in each combination corresponding to each feature to be selected, it is indicated that the target object is in the feature library, and then it is determined that the identity authentication of the target object is successful.
In one possible design, how to determine that the identity of the target object is successful may be accomplished by:
and if the first similarity threshold values corresponding to at least two features to be selected in the target combination meet the similarity threshold value comparison condition corresponding to each feature in the target preset combination, determining that the identity authentication of the target object is successful.
Wherein, the object corresponding to the target combination is the target object.
Illustratively, the above examples are combined. And comprehensively judging a possible identity set of the current identity authentication request according to the acquired characteristics and the strategy in the characteristic library during recording. For example, the s1 set and the s2 set are combined according to a combination predetermined policy of f1 and f 2. And obtaining a possible input individual set SID, namely a set corresponding to the target combination. Such as: the result set of voiceprints is { (ID 1, level 1), (ID 2, level 2) }, the result set of faces is { (ID 1, level 2), (ID 3, level 2) }, so that only the combination of ID1 satisfies the preset combination, and since the sound may be affected by noise and the face is affected by light, the set voiceprint and face combination is just two level 2; it can be seen that there is just ID1 in the set while satisfying the similarity above level 2. The possible strategies will also be slightly different when combining features according to different features.
If the SID is not null, the verification feature corresponding to each ID in the SID is looked up from the database. Continuing taking f1 and f2 as examples, if a certain ID is in the obtained identity set, and the input feature set corresponding to the ID is just f1 and f2, the authentication is considered to be successful, otherwise, the authentication fails.
Aiming at the problems of the existing intelligent access control identity authentication method, the application provides a customizable multi-mode identity authentication method, which can solve the problem that the identity authentication cannot be performed because the required identity authentication characteristic cannot be provided for objective reasons. Simultaneous verification of multiple features is performed simultaneously to optimize time consumption.
In order to implement the identity authentication method, the embodiment provides an identity authentication device. Referring to fig. 5, fig. 5 is a schematic structural diagram of an identity authentication device according to an embodiment of the present application; the identity authentication device 50 includes: a feature acquisition module 501, a similarity analysis module 502 and an identity authentication module 503; a feature acquisition module 501, configured to acquire at least two target features of a target object, where each target feature matches two target similarity thresholds; the similarity analysis module 502 is configured to compare the at least two target features with features in a feature library according to the two target similarity thresholds corresponding to the at least two target features, so as to obtain at least two feature sets to be selected, where each feature set to be selected includes target information of at least one feature to be selected corresponding to a class of features, and the target information includes an object identifier corresponding to the feature to be selected and a first similarity threshold of the target feature and the feature to be selected; the identity authentication module 503 is configured to determine whether the identity of the target object is successfully authenticated according to each of the object identifiers in the at least two feature sets to be selected and each of the first similarity thresholds through a preset combination policy.
In this embodiment, a feature obtaining module 501, a similarity analyzing module 502, and an identity authentication module 503 are configured to obtain at least two target features of a target object, where each target feature matches two target similarity thresholds; comparing the at least two target features with features in a feature library according to the two target similarity thresholds corresponding to the at least two target features to obtain at least two feature sets to be selected, wherein each feature set to be selected comprises target information of at least one feature to be selected corresponding to a type of feature, and the target information comprises an object identifier corresponding to the feature to be selected and a first similarity threshold of the target feature and the feature to be selected; and determining whether at least two feature sets to be selected contain the target object or not through a preset combination strategy according to each object identifier and each first similarity threshold value, further determining whether the identity of the target object is successfully authenticated or not, and meeting the requirement of diversification through the identity verification of a plurality of different combination features, so that the verification process is more flexible and quick, the feature combination verification can ensure the verification accuracy, and better service can be provided for users effectively.
The device provided in this embodiment may be used to implement the technical solution of the foregoing method embodiment, and its implementation principle and technical effects are similar, and this embodiment will not be described herein again.
In one possible design, the identity authentication module 503 is specifically configured to: according to the object identifiers in the at least two feature sets to be selected, acquiring the first similarity threshold corresponding to each feature set to be selected of the same object from the at least two feature sets to be selected; aiming at the same object, comparing each combination corresponding to each feature to be selected with a preset combination strategy according to the first similarity threshold corresponding to each feature to be selected in the at least two feature sets to be selected, and determining whether target combinations conforming to the preset combination strategy exist in each combination corresponding to each feature to be selected; and if the target combinations which accord with the preset combination strategies exist in the combinations corresponding to the features to be selected, determining that the identity authentication of the target object is successful.
In one possible design, the preset combination strategy includes a plurality of preset combinations corresponding to different types of features and similarity threshold comparison conditions corresponding to the features in each preset combination; the identity authentication module 503 is specifically configured to: according to the at least two feature sets to be selected, obtaining target combinations consistent with target preset combinations in the plurality of preset combinations from each combination corresponding to each feature to be selected; judging whether the first similarity threshold corresponding to each feature to be selected in the target combination meets a similarity threshold comparison condition corresponding to each feature in the target preset combination.
In one possible design, the identity authentication module 503 is specifically configured to: if the first similarity threshold values corresponding to at least two features to be selected in the target combination meet the similarity threshold value comparison condition corresponding to each feature in the target preset combination, determining that the identity authentication of the target object is successful; wherein, the object corresponding to the target combination is the target object.
In one possible design, feature acquisition module 501 is specifically configured to: if the target object is detected, an identity recognition function is started, wherein the identity recognition function is used for acquiring at least two target characteristics of voiceprint, face characteristics, fingerprint and password of the target object in real time.
In one possible design, the feature library includes a plurality of feature sets, each feature set includes information of at least one feature corresponding to a type of feature, and the information of the feature includes a feature and an object identifier corresponding to the feature; the similarity analysis module 502 is specifically configured to: for each target feature in the at least two target features, acquiring a first feature set to which the target feature belongs from a plurality of feature sets in the feature library, wherein the number of the first feature sets is at least two; comparing the target feature with each first feature in the first feature set aiming at each first feature set to obtain a second similarity threshold of the target feature and each first feature in the first feature set; and acquiring a first target similarity threshold with a low threshold value from the two target similarity thresholds corresponding to the target features, and determining at least two feature sets to be selected according to the first target similarity threshold, each second similarity threshold and each first feature set corresponding to the at least two target features.
In one possible design, the similarity analysis module 502 is specifically configured to: comparing each second similarity threshold with the first target similarity threshold corresponding to the target feature for each first feature set, and acquiring at least one second feature with the second similarity threshold larger than or equal to the first target similarity threshold from the first features; obtaining object identifiers corresponding to the at least one second feature from each first feature set, and generating the at least two feature sets to be selected according to a second similarity threshold corresponding to the at least one second feature and the object identifiers corresponding to the at least one second feature; the second similarity threshold corresponding to the at least one second feature is a first similarity threshold corresponding to each of the at least two candidate features in the at least two candidate feature sets.
In order to implement the identity authentication method, the embodiment provides identity authentication equipment. Fig. 6 is a schematic structural diagram of an identity authentication device according to an embodiment of the present application. As shown in fig. 6, the authentication apparatus 60 of the present embodiment includes: a processor 601 and a memory 602; wherein the memory 602 is configured to store computer-executable instructions; a processor 601 for executing computer-executable instructions stored in a memory to perform the steps performed in the above embodiments. See in particular the description of the method embodiments described above.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores computer execution instructions, and when a processor executes the computer execution instructions, the identity authentication method is realized.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms. In addition, each functional module in the embodiments of the present application may be integrated in one processing unit, or each module may exist alone physically, or two or more modules may be integrated in one unit. The units formed by the modules can be realized in a form of hardware or a form of hardware and software functional units.
The integrated modules, which are implemented in the form of software functional modules, may be stored in a computer readable storage medium. The software functional module is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (english: processor) to perform some of the steps of the methods according to the embodiments of the application. It should be understood that the above processor may be a central processing unit (english: central Processing Unit, abbreviated as CPU), or may be other general purpose processors, digital signal processors (english: digital Signal Processor, abbreviated as DSP), application specific integrated circuits (english: application Specific Integrated Circuit, abbreviated as ASIC), or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.
The memory may comprise a high-speed RAM memory, and may further comprise a non-volatile memory NVM, such as at least one magnetic disk memory, and may also be a U-disk, a removable hard disk, a read-only memory, a magnetic disk or optical disk, etc. The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (Peripheral Component, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of the present application are not limited to only one bus or to one type of bus. The storage medium may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC for short). It is also possible that the processor and the storage medium reside as discrete components in an electronic device or a master device.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application.
Claims (9)
1. An identity authentication method, comprising:
collecting all the characteristics of a target object, and obtaining at least two target characteristics in all the characteristics, wherein each target characteristic is matched with two target similarity thresholds, and the characteristics which are not recorded by the target object exist in all the characteristics;
comparing the at least two target features with features in a feature library according to the two target similarity thresholds corresponding to the at least two target features to obtain at least two feature sets to be selected, wherein each feature set to be selected comprises target information of at least one feature to be selected corresponding to a type of feature, and the target information comprises an object identifier corresponding to the feature to be selected and a first similarity threshold of the target feature and the feature to be selected;
determining whether the identity of the target object is successfully authenticated or not through a preset combination strategy according to each object identifier in the at least two feature sets to be selected and each first similarity threshold;
the determining whether the identity of the target object is successfully authenticated according to the object identifiers in the at least two feature sets to be selected and the first similarity thresholds through a preset combination strategy comprises:
According to the object identifiers in the at least two feature sets to be selected, acquiring the first similarity threshold corresponding to each feature set to be selected of the same object from the at least two feature sets to be selected;
aiming at the same object, comparing each combination corresponding to each feature to be selected with a preset combination strategy according to the first similarity threshold corresponding to each feature to be selected in the at least two feature sets to be selected, and determining whether target combinations conforming to the preset combination strategy exist in each combination corresponding to each feature to be selected;
and if the target combinations which accord with the preset combination strategies exist in the combinations corresponding to the features to be selected, determining that the identity authentication of the target object is successful.
2. The method according to claim 1, wherein the preset combination strategy comprises a plurality of preset combinations corresponding to different types of features and similarity threshold comparison conditions corresponding to the features in each preset combination;
comparing each combination corresponding to each feature to be selected with a preset combination policy according to the first similarity threshold corresponding to each feature to be selected in the at least two feature sets to be selected, and determining whether a target combination conforming to the preset combination policy exists in each combination corresponding to each feature to be selected, including:
According to the at least two feature sets to be selected, obtaining target combinations consistent with target preset combinations in the plurality of preset combinations from each combination corresponding to each feature to be selected;
judging whether the first similarity threshold corresponding to each feature to be selected in the target combination meets a similarity threshold comparison condition corresponding to each feature in the target preset combination.
3. The method according to claim 2, wherein determining that the identity authentication of the target object is successful if there is a target combination conforming to the preset combination policy in each combination corresponding to each candidate feature includes:
if the first similarity threshold values corresponding to at least two features to be selected in the target combination meet the similarity threshold value comparison condition corresponding to each feature in the target preset combination, determining that the identity authentication of the target object is successful;
wherein, the object corresponding to the target combination is the target object.
4. A method according to any of claims 1-3, wherein said acquiring all features of the target object, acquiring at least two target features of said all features, comprises:
If the target object is detected, an identity recognition function is started, wherein the identity recognition function is used for acquiring at least two target characteristics of voiceprint, face characteristics, fingerprint and password of the target object in real time.
5. The method according to claim 4, wherein the feature library comprises a plurality of feature sets, each feature set contains information of at least one feature corresponding to a type of feature, and the information of the feature comprises a feature and an object identifier corresponding to the feature;
comparing the at least two target features with features in a feature library according to the two target similarity thresholds corresponding to the at least two target features to obtain at least two feature sets to be selected, including:
for each target feature in the at least two target features, acquiring a first feature set to which the target feature belongs from a plurality of feature sets in the feature library, wherein the number of the first feature sets is at least two;
comparing the target feature with each first feature in the first feature set aiming at each first feature set to obtain a second similarity threshold of the target feature and each first feature in the first feature set;
And acquiring a first target similarity threshold with a low threshold value from the two target similarity thresholds corresponding to the target features, and determining at least two feature sets to be selected according to the first target similarity threshold, each second similarity threshold and each first feature set corresponding to the at least two target features.
6. The method of claim 5, wherein the determining at least two feature sets to be selected based on the first target similarity threshold, each of the second similarity thresholds, and each of the first feature sets for the at least two target features comprises:
comparing each second similarity threshold with the first target similarity threshold corresponding to the target feature for each first feature set, and acquiring at least one second feature with the second similarity threshold larger than or equal to the first target similarity threshold from the first features;
obtaining object identifiers corresponding to the at least one second feature from each first feature set, and generating the at least two feature sets to be selected according to a second similarity threshold corresponding to the at least one second feature and the object identifiers corresponding to the at least one second feature;
The second similarity threshold corresponding to the at least one second feature is a first similarity threshold corresponding to each of the at least two candidate features in the at least two candidate feature sets.
7. An identity authentication device, comprising:
the feature acquisition module is used for acquiring all features of a target object, acquiring at least two target features of the target object in all the features, wherein each target feature is matched with two target similarity thresholds, and the features which are not input by the target object exist in all the features;
the similarity analysis module is used for comparing the at least two target features with features in a feature library according to the two target similarity thresholds corresponding to the at least two target features to obtain at least two feature sets to be selected, wherein each feature set to be selected comprises target information of at least one feature to be selected corresponding to a type of feature, and the target information comprises an object identifier corresponding to the feature to be selected and a first similarity threshold of the target feature and the feature to be selected;
the identity authentication module is used for determining whether the identity of the target object is successfully authenticated or not through a preset combination strategy according to each object identifier in the at least two feature sets to be selected and each first similarity threshold;
The identity authentication module is specifically configured to:
according to the object identifiers in the at least two feature sets to be selected, acquiring the first similarity threshold corresponding to each feature set to be selected of the same object from the at least two feature sets to be selected;
aiming at the same object, comparing each combination corresponding to each feature to be selected with a preset combination strategy according to the first similarity threshold corresponding to each feature to be selected in the at least two feature sets to be selected, and determining whether target combinations conforming to the preset combination strategy exist in each combination corresponding to each feature to be selected;
and if the target combinations which accord with the preset combination strategies exist in the combinations corresponding to the features to be selected, determining that the identity authentication of the target object is successful.
8. An identity authentication device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing computer-executable instructions stored in the memory causes the at least one processor to perform the authentication method of any one of claims 1-6.
9. A computer readable storage medium having stored therein computer executable instructions which, when executed by a processor, implement the identity authentication method of any one of claims 1-6.
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