Gait-based identity verification method, apparatus and device, and storage medium

[0028] Example one

[0029] figure 1 It is a flowchart of the gait-based identity verification method provided in the first embodiment of the present invention. This embodiment is applicable to the situation of identity verification. The method can be executed by the gait-based identity verification device, such as figure 1 As shown, the gait-based identity verification method can include the following steps:

[0030] S110. Collect current gait data of the user to be identified.

[0031] In this embodiment, the current gait data refers to a gait cycle data in the current state of the user to be identified, and a gait cycle refers to walking from one heel to the ground until the same heel touches the ground again. Collect the current gait data of the user to be identified through the sensor in the terminal device. Exemplarily, the sensor may be an acceleration sensor, a gyroscope sensor, a gravity sensor, or the like.

[0032] S120: Calculate the distance between the current gait data and each reference gait data in the reference gait data set.

[0033] S130: Calculate the weighted average value corresponding to the current gait data according to the total distance between the current gait data and each reference gait data in the reference gait data set and the weighted value corresponding to each reference gait data stored in advance.

[0034] In this embodiment, a reference gait data set with three reference gait data is taken as an example for description. Exemplarily, 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} , Where [1, 2], [3, 3], [1, 8] are reference gait data; 3, 4, 5 are the number of times the corresponding reference gait data appears in the reference gait data set.

[0035] According to the Euclidean distance formula,

[0036] a[x1,y1]; =[x2,y2]

[0037]

[0038] Calculate the distance between the current gait data and each reference gait data in the reference gait data set. The current gait data is [1, 3] and the distance between the reference gait data [1, 2] is 1, the current gait data is [1, 3] and the distance between the reference gait data [3, 3] is 2, The distance between the current gait data [1, 3] and the reference gait data [1, 8] is 5.

[0039] The number of occurrences of each reference gait data in the reference gait data set is used as the weighted value corresponding to each reference gait data. The weighted average value corresponding to the current gait data is calculated according to the total distance between the current gait data and each reference gait data in the reference gait data set and the weighted value corresponding to each reference gait data stored in advance.

[0040] The weighted average formula, if the weights of n numbers d1, d2, d3,..., dn are w1, w2, w3,..., wn; the weighted average of these n numbers is

[0041] Calculate the weighted average corresponding to the current gait data according to the above formula.

[0042] The current gait data is [1, 3] and the reference gait data [1, 2] is 1, and the weight is 3; the current gait data is [1, 3] and the reference gait data [3, 3] The distance of is 2 and the weight is 4; the current gait data is [1,3] and the distance between the reference gait data [1,8] is 5, and the weight is 5.

[0043] The weighted average corresponding to the current gait data is:

[0044]

[0045] S140: Verify the identity of the user to be identified according to the weighted average.

[0046] Determine whether the weighted average value is less than the preset threshold; when the weighted average value is less than the preset threshold, the identity verification of the person to be identified is passed, that is, the user to be identified is himself, and when the weighted average is greater than or equal to the preset threshold, the identity of the person to be identified The verification fails, that is, the user to be identified is not himself.

[0047] Further, the determination of the preset threshold can be artificially set according to actual conditions, or an appropriate threshold can be set according to the accuracy rate. The accuracy rate is equal to the correct number divided by the total number.

[0048] Exemplarily, the preset threshold value is set to 4, the weighted average value corresponding to the current gait data is calculated in S130 as 3, and the weighted average value 3 is less than the preset threshold value 4. It means that the user to be identified is himself.

[0049] In the technical solution of the embodiment of the present invention, by collecting the current gait data of the user to be identified, the distance between the current gait data and each reference gait data in the reference gait data set is calculated, and the current gait data and each reference gait data set are The total distance of the reference gait data and the weighted value corresponding to each reference gait data stored in advance are calculated, and the weighted average value corresponding to the current gait data is calculated, and the identity of the user to be identified is verified according to the weighted average value. In order to reduce the amount of calculation and improve the effect of identifying users.

[0050] Example two

[0051] figure 2 It is a flowchart of model training in the gait-based identity verification method provided in the second embodiment of the present invention. This embodiment further adds a model training method in the gait-based identity verification method on the basis of the foregoing embodiments. Such as figure 2 Shown. Model training in the gait-based identity verification method can include the following steps:

[0052] S210: Collect current sample gait data of the user to be identified.

[0053] In this embodiment, the sample gait data refers to all the gait data of the user to be identified.

[0054] S220: Calculate the distance between the current sample gait data and each predetermined current reference gait data.

[0055] S230. Determine whether the distance between the current sample gait data and any one of the current reference gait data is less than a preset distance; if so, execute S240; if otherwise, execute S250;

[0056] S240. When the distance between the current sample gait data and any current reference gait data is less than the preset distance, add 1 to the weighted value corresponding to the current reference gait data;

[0057] S250: When the distance between the current sample gait data and any current reference gait data is not less than the preset distance, set the current sample gait data as the new current reference gait data.

[0058] In this embodiment, for each user to be identified, the first type of reference gait data is used as the first type of reference gait data, and the number is counted, and the corresponding number of the first type of reference gait data is 1;

[0059] Calculate the distance between the second sample of gait data and the first type of reference gait data. When the distance between the second sample of gait data and the first type of reference gait data is less than the preset distance, the two data are combined and counted , The corresponding number of the first type of benchmark gait data is 2;

[0060] Calculate the distance between the third sample of gait data and the first type of reference gait data. When the distance between the third sample of gait data and the first type of reference gait data is less than the preset distance, the third sample of gait data Incorporate the benchmark gait data of the first type of data, the corresponding number of the first type of benchmark gait data is 3;

[0061] Calculate the distance between the fourth sample of gait data and the first type of reference gait data. When the fourth sample of gait data is not less than the preset distance from the first type of reference gait data, it will no longer be with the first The gait data of the class data were merged. Instead, use the fourth sample gait data as the second type of benchmark gait data, and the second type of benchmark gait data counts as 1;

[0062] Calculate the distance between the fifth sample gait data and the first type of reference gait data, and calculate the distance between the fifth sample gait data and the second type of reference gait data, if the fifth sample gait data and the second type The distance of the class reference gait data is less than the preset distance, and the distance between the fifth sample gait data and the first type reference gait data is greater than the preset distance, then the fifth sample gait data is merged into the second type data Benchmark gait data, the second type of data benchmark gait data count is 2. 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, then the first The five sample gait numbers are combined with the benchmark gait data of the smallest distance. Exemplarily, if the distance between the fifth sample of gait data and the first type of reference gait data is less than the distance between the fifth sample of gait data and the second type of reference gait data, then the fifth sample of gait data Combined with the first type of reference gait data, the first type of reference gait data counts as 4; if the distance between the fifth sample of gait data and the second type of reference gait data is less than the fifth sample of gait data and the first If the distance of the standard gait data is similar, the fifth sample gait data is merged with the second-type standard gait data, and the second-type standard gait data counts as 2.

[0063] If a new sample of gait data appears, the analogy is used to calculate the combined count.

[0064] Exemplarily, the gait data of the first sample is [1, 2]; the gait data of the second sample is [2, 1]; the gait data of the third sample is [1, 3]; the fourth The gait data of the first sample is [2, 2]; the gait data of the fifth sample is [2, 5]; the preset distance is 2.

[0065] The first sample gait data is [1, 2] as the first type of reference gait data [1, 2], and the first type of reference gait data counts as 1.

[0066] According to the Euclidean distance formula, the distance between the second sample of gait data [2, 1] and the first type of benchmark gait data [1, 2] is calculated as , Less than the preset distance 2, the second sample of gait data [2, 1] is combined with the first type of reference gait data [1, 2], the count of the first type of reference gait data [1, 2] is recorded as 2.

[0067] According to the Euclidean distance formula, the distance between the third sample of gait data [1, 3] and the first type of benchmark gait data [1, 2] is 1, which is less than the preset distance 2, and the third sample of gait data [1, 3] is merged with the first type of benchmark gait data [1,2], and the count of the first type of benchmark gait data [1,2] is recorded as 3.

[0068] According to the Euclidean distance formula, the distance between the fourth sample gait data [2, 4] and the first type of benchmark gait data [1, 2] is calculated as , Is greater than the preset distance 2, then the fourth sample gait data is [2, 4] as the second type of reference gait data [2, 4], and the second type of reference gait data count is recorded as 1.

[0069] According to the Euclidean distance formula, the distance between the fifth sample gait data [2, 5] and the first type data reference gait data [1, 2] is calculated as Greater than the preset distance 2, calculate the fifth sample gait data as [2, 5] and the second type of reference gait data [2, 4] the distance is 1, less than the preset distance 2, then the fifth sample The gait data is [2,5] and the second type of reference gait data [2,4] are combined, and the second type of data reference gait data count is recorded as 2.

[0070] The first type of reference gait data and the second type of reference gait data and their respective numbers constitute 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 of the current reference gait data is less than the preset distance, the weighted value corresponding to the current reference gait data is increased by 1; When the distance of a current reference gait data is not less than the preset distance, the current sample gait data is set as the new current reference gait data. By analogy, the sample gait data is calculated and counted, and the benchmark gait data set is constantly updated.

[0071] The model training method provided by the embodiment of the present invention calculates the distance between the current sample gait data and each predetermined current reference gait data by collecting the current sample gait data of the user to be identified. When the current sample gait data is equal to any one When the distance of the current reference gait data is less than the preset distance, add 1 to the weighted value corresponding to the current reference sample gait data, when the distance between the current sample gait data and any current reference gait data is not less than the preset distance , Set the current sample gait data as the new current reference gait data. It solves the problem of calculating the distance between the new data and all the training data in the prior art calculation method, and the calculation amount is relatively large. The problem of repeated calculation and the simple de-duplication of the training data can inevitably lose a large amount of information in the data, which can reduce model training. The amount of calculation avoids the loss of data information.

[0072] Example three

[0073] image 3 It is a flowchart of the gait-based identity verification method provided in the third embodiment of the present invention. This embodiment optimizes the gait-based identity verification method on the basis of the foregoing embodiments, such as image 3 As shown, the gait-based identity verification method can include the following steps:

[0074] S310. Collect current gait data of the user to be identified.

[0075] S320: Calculate the distance between the current gait data and each reference gait data in the reference gait data set.

[0076] S330: Obtain a preset number of reference gait data with the smallest distance from the current gait data from all the reference gait data.

[0077] S340. Calculate the weighted average value corresponding to the current gait data according to the preset number of reference data with the smallest distance from the current gait data and the weighted value corresponding to the preset number of reference gait data with the smallest distance from the current gait data .

[0078] In this embodiment, the description is made on the basis of the distance between the current gait data calculated in the first embodiment and each reference gait data in the reference gait data set. The current gait data is [1, 3] and the distance between the reference gait data [1, 2] is 1, the current gait data is [1, 3] and the distance between the reference gait data [3, 3] is 2, The distance between the current gait data [1, 3] and the reference gait data [1, 8] is 5. Obtain the preset number of reference gait data with the smallest distance from the current gait data from all the reference gait data. Exemplarily, the preset number is 2. Obtain 2 benchmark gait data with the smallest distance from the current gait data among the 3 benchmark gait data, which are benchmark gait data [1, 2] and benchmark gait data [3, 3].

[0079] In the reference gait data set, the number of times corresponding to the reference gait data [1, 2] and the reference gait data [3, 3] are obtained as their respective weighted values. The weighted value of the reference gait data [1, 2] is 3, and the weighted value of the reference gait data [3, 3] is 4.

[0080] The weighted average corresponding to the current gait data is:

[0081]

[0082] S350: Determine whether the weighted average value is less than a preset threshold.

[0083] S360. When the weighted average value is less than the preset threshold, the identity verification of the person to be identified passes.

[0084] S370. When the weighted average value is less than the preset threshold, the identity verification of the person to be identified fails.

[0085] Determine whether the weighted average value is less than the preset threshold; when the weighted average value is less than the preset threshold, the identity verification of the person to be identified is passed, that is, the user to be identified is himself, and when the weighted average is greater than or equal to the preset threshold, the identity of the person to be identified The verification fails, that is, the user to be identified is not himself.

[0086] Further, the determination of the preset threshold can be artificially set according to actual conditions, or an appropriate threshold can be set according to the accuracy rate. The accuracy rate = the correct number divided by the total number.

[0087] Exemplarily, the preset threshold is set to 4, and the weighted average value corresponding to the current gait data is calculated in S340 as Weighted average Less than the preset threshold 4. It means that the user to be identified is himself.

[0088] Further, in the case that the user identity needs to be verified once, multiple pieces of gait data of the user to be identified are collected, and the verification results of these pieces of gait data are not the same, the gait data that passed the verification can be used in all The proportion of gait data determines the verification result. Further, calculate 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 the preset proportion, the identity verification is passed; otherwise , The authentication failed.

[0089] In the embodiment of the present invention, the preset number of reference gait data with the smallest distance from the current gait data is acquired from all reference gait data, and the preset number of reference gait data with the smallest distance from the current gait data is acquired, and The weighted value corresponding to the preset number of reference gait data with the smallest distance from the current gait data, calculates the weighted average corresponding to the current gait data, and judges whether the weighted average is less than the preset threshold, when the weighted average is less than the preset threshold , The identity verification of the person to be identified is passed. In order to reduce the amount of calculation and improve the effect of identifying users.