CN107995181B - Gait-based identity authentication method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses an identity authentication method, device, equipment and storage medium based on gait. The gait-based identity authentication method comprises the following steps: collecting current gait data of a user to be identified; calculating the distance between the current gait data and each datum gait data in a datum gait data set; calculating a weighted average value corresponding to the current gait data according to the total distance between the current gait data and each datum gait data in the datum gait data set and a weighted value corresponding to each datum gait data which is stored in advance; and verifying the identity of the user to be identified according to the weighted average value. So as to reduce the calculation amount and improve the effect of identifying the user.
Description
Technical Field
The embodiment of the invention relates to the technical field of communication, in particular to an identity authentication method, device, equipment and storage medium based on gait.
Background
Gait recognition is a new biometric authentication technique that has been of increasing interest to more and more researchers in recent years. Gait recognition is a method of recognizing the identity of a person by walking.
With the continuous development of the sensors, the gait data of the human body can be collected by the sensors under the condition that people do not sense the gait data, and then the identity of the people can be identified. At present, a common gait recognition method is to collect gait data of a human body and bring the gait data into a classification model to recognize a user. The existing gait recognition classification model usually adopts a KNN (nearest neighbor algorithm, K-nearest neighbor) model, and two groups of sample data are stored in the KNN model, wherein one group is the sample data of the user, and the other group is the sample data of the non-user; when the gait data to be identified is collected, calculating the distance between the gait data to be identified and all sample data in the KNN model according to an Euclidean distance formula, selecting K nearest sample data to form a neighbor sample set, calculating the proportion of sample data of the person and sample data of a non-person in the neighbor sample set respectively, if the proportion of the sample data of the person is greater than that of the sample data of the non-person, determining that the person to be identified is the person, otherwise, determining that the person to be identified is not the person.
In the identification method in the prior art, the distances between the gait data of the person to be identified and all sample data in the model need to be calculated, and the calculated amount is particularly large. And the walking of people has great randomness, which leads to a plurality of gait data of one person. The user sample data of certain gait data in the K nearest sample data is less, but the non-user sample data is more, so that the identification result is wrong, and the user identity identification effect is reduced.
Disclosure of Invention
The invention provides an identity authentication method, device, equipment and storage medium based on gait, which can reduce the calculation amount and improve the effect of identifying a user.
In a first aspect, an embodiment of the present invention provides a gait-based identity verification method, where the method includes:
collecting current gait data of a user to be identified;
calculating the distance between the current gait data and each datum gait data in a datum gait data set;
calculating a weighted average value corresponding to the current gait data according to the total distance between the current gait data and each datum gait data in the datum gait data set and a weighted value corresponding to each datum gait data which is stored in advance;
and verifying the identity of the user to be identified according to the weighted average value.
In a second aspect, an embodiment of the present invention further provides a gait-based identity authentication device, where the device includes: the device comprises an acquisition module, a calculation module and a verification module; wherein,
the acquisition module is used for acquiring the current gait data of the user to be identified;
the calculation module is used for calculating the distance between the current gait data and each datum gait data in a datum gait data set;
calculating a weighted average value corresponding to the current gait data according to the total distance between the current gait data and each datum gait data in the datum gait data set and a weighted value corresponding to each datum gait data which is stored in advance;
and the verification module is used for verifying the identity of the user to be identified according to the weighted average value.
In a third aspect, an embodiment of the present invention further provides an apparatus, where the apparatus includes:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of the first aspects above.
In a fourth aspect, an embodiment of the present invention further provides a storage medium storing computer-executable instructions, where the computer-executable instructions are configured to perform the method according to any one of the above first aspects.
According to the technical scheme of the embodiment of the invention, the distance between the current gait data and each datum gait data in the datum gait data set is calculated by collecting the current gait data of the user to be identified, the weighted average value corresponding to the current gait data is calculated according to the total distance between the current gait data and each datum gait data in the datum gait data set and the weighted value corresponding to each datum gait data stored in advance, and the identity of the user to be identified is verified according to the weighted average value. So as to reduce the calculation amount and improve the effect of identifying the user.
Drawings
Fig. 1 is a flowchart of a gait-based authentication method according to an embodiment of the invention;
FIG. 2 is a flowchart of model training in a gait-based authentication method according to a second embodiment of the invention;
FIG. 3 is a flow chart of a gait-based authentication method according to a third embodiment of the invention;
fig. 4 is a schematic structural diagram of a gait-based authentication apparatus according to a fourth embodiment of the invention;
fig. 5 is a schematic structural diagram of an apparatus provided in the fifth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a gait-based authentication method according to an embodiment of the present invention, where the embodiment is applicable to an authentication situation, the method may be executed by a gait-based authentication device, and as shown in fig. 1, the gait-based authentication method may include the following steps:
and S110, collecting current gait data of the user to be identified.
In this embodiment, the current gait data refers to a gait cycle data of the user to be identified in the current state, and one gait cycle refers to a gait cycle from the heel at one side to the heel at the same side in walking. And acquiring current gait data of the user to be identified through a sensor in the terminal equipment. Illustratively, the sensor may be an acceleration sensor, a gyro sensor, a gravity sensor, or the like.
And S120, calculating the distance between the current gait data and each datum gait data in the datum gait data set.
And S130, calculating a weighted average value corresponding to the current gait data according to the total distance between the current gait data and each datum gait data in the datum gait data set and a weighted value corresponding to each datum gait data which is stored in advance.
In the present embodiment, a reference gait data set having 3 pieces of reference gait data will be described as an example. Illustratively, the current gait data of the user to be identified is [1, 3], and the reference gait data set is { [1, 2 ]: 3; [3,3]: 4; [1,8]: 5, wherein [1, 2], [3, 3], [1, 8] is reference gait data; 3, 4, 5 are the number of occurrences of the corresponding reference gait data in the reference gait data set.
According to the Euclidean distance formula,
a[x1,y1]；＝[x2,y2]
and calculating the distance between the current gait data and each datum gait data in the datum gait data set. The distance between the current gait data [1, 3] and the reference gait data [1, 2] is 1, the distance between the current gait data [1, 3] and the reference gait data [3, 3] is 2, and the distance between the current gait data [1, 3] and the reference gait data [1, 8] is 5.
And the frequency of the occurrence of each datum gait data in the datum gait data set is used as a weighted value corresponding to each datum gait data. And calculating a weighted average value corresponding to the current gait data according to the total distance between the current gait data and each datum gait data in the datum gait data set and a weighted value corresponding to each datum gait data which is stored in advance.
The formula of the weighted average value is that if the weights of n numbers d1, d2, d3, … … and dn are w1, w2, w3, … … and wn respectively; the weighted average of the n numbers is
And calculating the weighted average value corresponding to the current gait data according to the formula.
The distance between the current gait data [1, 3] and the reference gait data [1, 2] is 1, and the weighted value is 3; the distance between the current gait data [1, 3] and the reference gait data [3, 3] is 2, and the weighted value is 4; the distance between the current gait data [1, 3] and the reference gait data [1, 8] is 5, and the weighted value is 5.
The weighted average corresponding to the current gait data is:
and S140, verifying the identity of the user to be identified according to the weighted average value.
Judging whether the weighted average value is smaller than a preset threshold value or not; and when the weighted average value is greater than or equal to the preset threshold value, the identity verification of the user to be identified is not passed, namely the user to be identified is not the user.
Further, the determination of the preset threshold value can be set manually according to actual conditions, and an appropriate threshold value can also be set according to the accuracy. Where accuracy is equal to the correct number divided by the total number.
For example, the preset threshold is set to 4, and in S130, the weighted average corresponding to the current gait data is calculated to be 3, and the weighted average 3 is smaller than the preset threshold 4. It is indicated that the user to be identified is himself.
According to the technical scheme of the embodiment of the invention, the distance between the current gait data and each datum gait data in the datum gait data set is calculated by collecting the current gait data of the user to be identified, the weighted average value corresponding to the current gait data is calculated according to the total distance between the current gait data and each datum gait data in the datum gait data set and the weighted value corresponding to each datum gait data stored in advance, and the identity of the user to be identified is verified according to the weighted average value. So as to reduce the calculation amount and improve the effect of identifying the user.
Example two
Fig. 2 is a flowchart of model training in the gait-based authentication method according to the second embodiment of the present invention, and the second embodiment of the present invention further adds a model training method in the gait-based authentication method to the above embodiments. As shown in fig. 2. The model training in the gait-based identity verification method can comprise the following steps:
and S210, collecting the current sample gait data of the user to be identified.
In this embodiment, the sample gait data refers to all gait data of the user to be identified.
And S220, calculating the distance between the current sample gait data and each preset current reference gait data.
S230, judging whether the distance between the current sample gait data and any one current reference gait data is smaller than a preset distance; if yes, go to S240; if not, executing S250;
s240, when the distance between the current sample gait data and any one current reference gait data is smaller than a preset distance, adding 1 to a weighted value corresponding to the current reference gait data;
and S250, when the distance between the current sample gait data and any one current reference gait data is not less than the preset distance, setting the current sample gait data as new current reference gait data.
In the embodiment, for each user to be identified, taking the first-appearing sample gait data as the first-class reference gait data, and counting, wherein the corresponding number of the first-class reference gait data is 1;
calculating the distance between the second sample gait data and the first type of reference gait data, merging the two data when the distance between the second sample gait data and the first type of reference gait data is less than the preset distance, and counting, wherein the corresponding number of the first type of reference gait data is 2;
calculating the distance between the third sample gait data and the first type of reference gait data, and when the distance between the third sample gait data and the first type of reference gait data is smaller than a preset distance, merging the third sample gait data into the first type of data reference gait data, wherein the corresponding number of the first type of reference gait data is 3;
and calculating the distance between the fourth sample gait data and the first type of reference gait data, and when the distance between the fourth sample gait data and the first type of data reference gait data is not less than the preset distance, the fourth sample gait data is not merged with the first type of data reference gait data. Taking the fourth sample gait data as second type reference gait data, wherein the second type reference gait data count is 1;
and if the distance between the fifth sample gait data and the second type of reference gait data is less than the preset distance and the distance between the fifth sample gait data and the first type of reference gait data is greater than the preset distance, the fifth sample gait data is merged into the second type of data reference gait data, and the second type of data reference gait data count is 2. And if the distance between the fifth sample gait data and the second type of reference gait data is less than the preset distance and the distance between the fifth sample gait data and the first type of reference gait data is also less than the preset distance, merging the fifth sample gait data and the type of reference gait data with the minimum distance. Exemplarily, if the distance between the fifth sample gait data and the first type of reference gait data is less than the distance between the fifth sample gait data and the second type of reference gait data, the fifth sample gait data is merged with the first type of reference gait data, and the first type of reference gait data count is 4; if the distance between the fifth sample gait data and the second type of reference gait data is less than the distance between the fifth sample gait data and the first type of reference gait data, then the fifth sample gait data and the second type of reference gait data are merged, and the second type of reference gait data count is 2.
If new sample gait data appear, the calculation, combination and counting are carried out by analogy.
Illustratively, the first occurrence of sample gait data is [1, 2 ]; the second sample gait data is [2, 1 ]; the third sample gait data is [1, 3 ]; the fourth sample gait data is [2, 2 ]; the fifth sample gait data is [2, 5 ]; the preset distance is 2.
The first-appearing sample gait data is [1, 2] as the first-class reference gait data [1, 2], and the first-class reference gait data count is 1.
Calculating second sample gait data [2, 1] according to Euclidean distance formula]With reference gait data of the first type [1, 2]]A distance of
Less than the predetermined distance 2, second sample gait data [2, 1]]With radicals of the first kindQuasi gait data [1, 2]Merging, first class reference gait data [1, 2]]The count of (2).
And calculating the distance between the third sample gait data [1, 3] and the first type reference gait data [1, 2] as 1 and smaller than the preset distance 2 according to the Euclidean distance formula, merging the third sample gait data [1, 3] and the first type reference gait data [1, 2], and counting the first type reference gait data [1, 2] as 3.
Calculating the fourth sample gait data as [2, 4] according to the Euclidean distance formula]With reference gait data of the first type [1, 2]]A distance of
If the distance is greater than the preset distance 2, the fourth sample gait data is [2, 4]]As second type reference gait data [2, 4]]The second type of baseline gait data count is noted as 1.
Calculating the gait data of the fifth sample as [2, 5] according to the Euclidean distance formula]Reference gait data [1, 2] with first class data]A distance of
If the distance is larger than the preset distance 2, calculating the gait data of the fifth sample as [2, 5]]And second type of reference gait data [2, 4]]Is 1, is less than the preset distance 2, the fifth sample gait data is [2, 5]]And second type of reference gait data [2, 4]]And merging, and counting and recording the second-class data reference gait data as 2.
And (3) forming a reference gait data set by the first-type reference gait data, the second-type reference gait data and the corresponding numbers of the first-type reference gait data and the second-type reference gait data to form a reference gait data set { [1, 2 ]: 3; [2,4]: 2}. If the current sample gait data is collected, when the distance between the sample gait data and any one current reference gait data is less than the preset distance, adding 1 to the weighted value corresponding to the current reference gait data; and when the distance between the current sample gait data and any one current reference gait data is not less than the preset distance, setting the current sample gait data as new current reference gait data. By analogy, the sample gait data is calculated, merged and counted, and the reference gait data set is continuously updated.
The model training method provided by the embodiment of the invention comprises the steps of collecting current sample gait data of a user to be identified, calculating the distance between the current sample gait data and each piece of predetermined current reference gait data, adding 1 to a weighted value corresponding to the current reference sample gait data when the distance between the current sample gait data and any one piece of current reference gait data is less than a preset distance, and setting the current sample gait data as new current reference gait data when the distance between the current sample gait data and any one piece of current reference gait data is not less than the preset distance. The method solves the problems that the calculation amount of new data and all training data is large and the calculation is repeated in the calculation method in the prior art, and the training data is simply deduplicated, so that a large amount of information of the data is difficult to avoid losing, can reduce the calculation amount of model training, and avoids the loss of data information.
EXAMPLE III
Fig. 3 is a flowchart of an authentication method based on gait according to a third embodiment of the present invention, and in this embodiment, based on the above embodiments, the authentication method based on gait is optimized, and as shown in fig. 3, the authentication method based on gait may include the following steps:
and S310, collecting current gait data of the user to be identified.
And S320, calculating the distance between the current gait data and each datum gait data in the datum gait data set.
And S330, acquiring a preset number of reference gait data with the minimum distance to the current gait data from all the reference gait data.
And S340, calculating a weighted average value corresponding to the current gait data according to the preset number of datum data with the minimum distance from the current gait data and the weighted value corresponding to the preset number of datum gait data with the minimum distance from the current gait data.
In this embodiment, the distance between the current gait data and each reference gait data in the reference gait data set calculated in the first embodiment will be described. The distance between the current gait data [1, 3] and the reference gait data [1, 2] is 1, the distance between the current gait data [1, 3] and the reference gait data [3, 3] is 2, and the distance between the current gait data [1, 3] and the reference gait data [1, 8] is 5. And acquiring a preset number of reference gait data with the minimum distance from the current gait data from all the reference gait data. Illustratively, the predetermined number is 2. And acquiring 2 pieces of reference gait data with the minimum distance from the current gait data from the 3 pieces of reference gait data, namely reference gait data [1, 2] and reference gait data [3, 3 ].
And acquiring the times corresponding to the reference gait data [1, 2] and the reference gait data [3, 3] in the reference gait data set as respective weighted values. The weighting value of the acquired reference gait data [1, 2] is 3, and the weighting value of the reference gait data [3, 3] is 4.
The weighted average corresponding to the current gait data is:
and S350, judging whether the weighted average value is smaller than a preset threshold value.
And S360, when the weighted average value is smaller than a preset threshold value, the identity verification of the person to be identified is passed.
And S370, when the weighted average value is smaller than the preset threshold value, the identity authentication of the person to be identified is not passed.
Judging whether the weighted average value is smaller than a preset threshold value or not; and when the weighted average value is greater than or equal to the preset threshold value, the identity verification of the user to be identified is not passed, namely the user to be identified is not the user.
Further, the determination of the preset threshold value can be set manually according to actual conditions, and an appropriate threshold value can also be set according to the accuracy. Where the accuracy is the correct number divided by the total number.
For example, the preset threshold is set to 4, and the weighted average corresponding to the current gait data is calculated as
Weighted average
Less than a preset threshold 4. It is indicated that the user to be identified is himself.
Furthermore, under the condition that the identity of the user needs to be verified once, a plurality of pieces of gait data of the user to be identified are collected, and when the verification results of the plurality of pieces of gait data are different, the verification results can be determined according to the proportion of the verified gait data in all the gait data. Further, calculating the proportion of the verified gait data in all gait data, and when the proportion of the verified gait data in all gait data is greater than a preset proportion, passing the identity verification; otherwise, the authentication is not passed.
In the embodiment of the invention, the preset number of pieces of reference gait data with the minimum distance from the current gait data are acquired from all pieces of reference gait data, the weighted average value corresponding to the current gait data is calculated according to the preset number of pieces of reference gait data with the minimum distance from the current gait data and the weighted value corresponding to the preset number of pieces of reference gait data with the minimum distance from the current gait data, whether the weighted average value is smaller than the preset threshold value or not is judged, and when the weighted average value is smaller than the preset threshold value, the identity verification of a person to be identified is passed. So as to reduce the calculation amount and improve the effect of identifying the user.
Example four
Fig. 4 is a schematic structural diagram of an identity authentication device based on gait according to a fourth embodiment of the present invention, where the embodiment is applicable to the case of identity authentication, and the specific structure of the identity authentication device based on gait is as follows: an acquisition module 410, a calculation module 420 and a verification module 430; wherein,
the acquisition module 410 is configured to acquire current gait data of a user to be identified.
A calculating module 420, configured to calculate a distance between the current gait data and each reference gait data in the reference gait data set; and calculating a weighted average value corresponding to the current gait data according to the total distance between the current gait data and each datum gait data in the datum gait data set and a weighted value corresponding to each datum gait data which is stored in advance.
And the verification module 430 is configured to verify the identity of the user to be identified according to the weighted average.
Further, the collecting module 410 is further configured to collect current sample gait data of the user to be identified.
The calculating module 420 is further configured to calculate distances between the current sample gait data and each of the predetermined current reference gait data; when the distance between the current sample gait data and any one current reference gait data is smaller than the preset distance, adding 1 to the weighted value corresponding to the current reference sample gait data; and when the distance between the current sample gait data and any one current reference gait data is not less than the preset distance, setting the current sample gait data as new current reference gait data.
Further, the calculation module 420 includes: an acquisition unit and a calculation unit; wherein,
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a preset number of reference gait data with the minimum distance from the current gait data from all the reference gait data;
and the calculating unit is used for calculating a weighted average value corresponding to the current gait data according to the reference gait data with the preset number and the minimum distance from the current gait data and the weighted value corresponding to the reference gait data with the preset number and the minimum distance from the current gait data.
Further, the verification module is specifically configured to determine whether the weighted average is smaller than a preset threshold; and when the weighted average value is smaller than the preset threshold value, the identity verification of the person to be identified is passed.
The gait-based identity verification device provided by the embodiment of the invention calculates the distance between the current gait data and each datum gait data in the datum gait data set by collecting the current gait data of the user to be recognized, calculates the weighted average value corresponding to the current gait data according to the total distance between the current gait data and each datum gait data in the datum gait data set and the weighted value corresponding to each datum gait data stored in advance, and verifies the identity of the user to be recognized according to the weighted average value. So as to reduce the calculation amount and improve the effect of identifying the user.
The gait-based identity authentication device provided by the embodiment of the invention can execute the gait-based identity authentication method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
EXAMPLE five
Fig. 5 is a schematic structural diagram of an apparatus provided in the fifth embodiment of the present invention. FIG. 5 illustrates a block diagram of an exemplary device 512 suitable for use in implementing embodiments of the present invention. The device 512 shown in fig. 5 is only an example and should not bring any limitations to the functionality or scope of use of the embodiments of the present invention.
As shown in fig. 5, device 512 is in the form of a general purpose device. Components of device 512 may include, but are not limited to: one or more processors or processing units 516, a system memory 528, and a bus 518 that couples the various system components including the system memory 528 and the processing unit 516.
The system memory 528 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)530 and/or cache memory 532. The device 512 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 534 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, and commonly referred to as a "hard drive"). Although not shown in FIG. 5, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 518 through one or more data media interfaces. System memory 528 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 540 having a set (at least one) of program modules 542 may be stored, for example, in system memory 528, such program modules 542 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. The program modules 542 generally perform the functions and/or methods of the described embodiments of the invention.
The processing unit 516 executes various functional applications and data processing by running programs stored in the system memory 528, for example, implementing the gait-based authentication method provided by the embodiment of the present invention:
and acquiring current gait data of the user to be identified.
And calculating the distance between the current gait data and each datum gait data in the datum gait data set.
And calculating a weighted average value corresponding to the current gait data according to the total distance between the current gait data and each datum gait data in the datum gait data set and a weighted value corresponding to each datum gait data which is stored in advance.
And verifying the identity of the user to be identified according to the weighted average value.
Further, before collecting the current gait data of the user to be identified, the gait-based identity authentication method further comprises:
acquiring current sample gait data of a user to be identified;
calculating the distance between the current sample gait data and each predetermined current reference gait data;
when the distance between the current sample gait data and any one current reference gait data is smaller than the preset distance, adding 1 to the weighted value corresponding to the current reference gait data;
and when the distance between the current sample gait data and any one current reference gait data is not less than the preset distance, setting the current sample gait data as new current reference gait data.
Further, calculating a weighted average value corresponding to the current gait data according to the total distance between the current gait data and each datum gait data in the datum gait data set and a weighted value corresponding to each datum gait data which is stored in advance, and the method comprises the following steps:
acquiring a preset number of reference gait data with the minimum distance from the current gait data from all the reference gait data;
and calculating a weighted average value corresponding to the current gait data according to the preset number of pieces of reference gait data with the minimum distance from the current gait data and the weighted value corresponding to the preset number of pieces of reference gait data with the minimum distance from the current gait data.
Further, verifying the identity of the person to be identified according to the weighted average value, comprising:
judging whether the weighted average value is smaller than a preset threshold value or not;
and when the weighted average value is smaller than the preset threshold value, the identity verification of the person to be identified is passed.
The device provided by the embodiment of the invention calculates the distance between the current gait data and each datum gait data in the datum gait data set by collecting the current gait data of the user to be identified, calculates the weighted average value corresponding to the current gait data according to the total distance between the current gait data and each datum gait data in the datum gait data set and the weighted value corresponding to each datum gait data stored in advance, and verifies the identity of the user to be identified according to the weighted average value. So as to reduce the calculation amount and improve the effect of identifying the user.
EXAMPLE six
The sixth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the gait-based identity authentication method provided in all the embodiments of the present invention:
and acquiring current gait data of the user to be identified.
And calculating the distance between the current gait data and each datum gait data in the datum gait data set.
And calculating a weighted average value corresponding to the current gait data according to the total distance between the current gait data and each datum gait data in the datum gait data set and a weighted value corresponding to each datum gait data which is stored in advance.
And verifying the identity of the user to be identified according to the weighted average value.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (8)
1. A gait-based identity verification method, the method comprising:
acquiring current sample gait data of a user to be identified;
calculating the distance between the current sample gait data and each predetermined current reference gait data;
when the distance between the current sample gait data and any one current reference gait data is smaller than a preset distance, adding 1 to the weighted value corresponding to the current reference gait data;
when the distance between the current sample gait data and any one current reference gait data is not less than a preset distance, setting the current sample gait data as new current reference gait data;
acquiring current gait data of a user to be identified, wherein the current gait data refers to data of one gait cycle of the user to be identified in the current state, the one gait cycle refers to a period from heel landing at one side to heel landing at the same side again in walking, and the current gait data is acquired by a sensor in terminal equipment;
calculating the distance between the current gait data and each datum gait data in a datum gait data set;
calculating a weighted average value corresponding to the current gait data according to the total distance between the current gait data and each datum gait data in the datum gait data set and a weighted value corresponding to each datum gait data which is stored in advance;
and verifying the identity of the user to be identified according to the weighted average value.
2. The method of claim 1, wherein calculating a weighted average corresponding to the current gait data based on the total distance between the current gait data and each reference gait data in the reference gait data set and a weighting value corresponding to each reference gait data saved in advance comprises:
acquiring a preset number of reference gait data with the minimum distance from the current gait data from all the reference gait data;
and calculating a weighted average value corresponding to the current gait data according to the preset number of pieces of reference gait data with the minimum distance to the current gait data and the weighted value corresponding to the preset number of pieces of reference gait data with the minimum distance to the current gait data.
3. The method according to claim 1, wherein the verifying the identity of the person to be identified according to the weighted average value comprises:
judging whether the weighted average value is smaller than a preset threshold value or not;
and when the weighted average value is smaller than a preset threshold value, the identity verification of the person to be identified is passed.
4. A gait-based authentication apparatus, characterized in that the apparatus comprises: the device comprises an acquisition module, a calculation module and a verification module; wherein,
the system comprises an acquisition module, a processing module and a control module, wherein the acquisition module is used for acquiring current gait data of a user to be identified, the current gait data refers to data of one gait cycle of the user to be identified in the current state, the one gait cycle refers to the state from heel landing at one side to heel landing at the same side again in walking, and the current gait data is acquired by a sensor in terminal equipment;
the calculation module is used for calculating the distance between the current gait data and each datum gait data in a datum gait data set;
calculating a weighted average value corresponding to the current gait data according to the total distance between the current gait data and each datum gait data in the datum gait data set and a weighted value corresponding to each datum gait data which is stored in advance;
the verification module is used for verifying the identity of the user to be identified according to the weighted average value;
the acquisition module is also used for acquiring the current sample gait data of the user to be identified;
the calculation module is further used for calculating the distance between the current sample gait data and each preset current reference gait data; when the distance between the current sample gait data and any one current reference gait data is smaller than a preset distance, adding 1 to the weighted value corresponding to the current reference gait data; and when the distance between the current sample gait data and any one current reference gait data is not less than the preset distance, setting the current sample gait data as new current reference gait data.
5. The apparatus of claim 4, wherein the computing module comprises: an acquisition unit and a calculation unit; wherein,
the acquiring unit is used for acquiring a preset number of pieces of reference gait data with the minimum distance from the current gait data from all pieces of reference gait data;
the calculating unit is used for calculating a weighted average value corresponding to the current gait data according to the reference gait data with the preset number and the minimum distance from the current gait data and the weighted value corresponding to the reference gait data with the preset number and the minimum distance from the current gait data.
6. The apparatus according to claim 4, wherein the verification module is specifically configured to determine whether the weighted average is smaller than a preset threshold; and when the weighted average value is smaller than a preset threshold value, the identity verification of the person to be identified is passed.
7. An apparatus, characterized in that the apparatus comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 3.
8. A storage medium storing computer-executable instructions for performing the method of any one of claims 1 to 3.
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