KR101884090B1 - Apparatus and method of signature authentication - Google Patents

Apparatus and method of signature authentication Download PDF

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KR101884090B1
KR101884090B1 KR1020160159706A KR20160159706A KR101884090B1 KR 101884090 B1 KR101884090 B1 KR 101884090B1 KR 1020160159706 A KR1020160159706 A KR 1020160159706A KR 20160159706 A KR20160159706 A KR 20160159706A KR 101884090 B1 KR101884090 B1 KR 101884090B1
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signature
value
data
learning model
input
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KR20180061489A (en
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최대선
남승수
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공주대학교 산학협력단
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P15/00—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
    • G01P15/02—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration by making use of inertia forces using solid seismic masses
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/45—Structures or tools for the administration of authentication
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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Abstract

The present invention relates to a signature authentication apparatus and method, and more particularly, to an apparatus and method for signature authentication, which includes an input unit for inputting a signature, an acceleration sensor for measuring the acceleration of the signature authentication apparatus and data input through the input unit, acceleration data measured through the acceleration sensor, And a control unit for performing signature authentication on the basis of the authentication information.

Description

[0001] APPARATUS AND METHOD OF SIGNATURE AUTHENTICATION [0002]

The present invention relates to a signature authentication apparatus and method, and more particularly, to a signature authentication apparatus and method for authenticating a dynamic signature.

Recently, as the spread of mobile devices such as smart phones and smart pads has increased, researches on biometric authentication in these devices have been developed.

The most commonly used means of authentication is forgery using fingerprints, recorded voices, and photographs made with a 3D printer in authentication using static bio information such as fingerprints, fingerprints, voices, and faces, There is a problem that it can not be changed when exposed.

As a result, studies on authentication based on dynamic behaviors that are difficult to imitate easily, such as recognition of gait recognition and gesture recognition, have been actively carried out. Among these dynamic behaviors, dynamic signature in a smartphone can be easily performed by a user, There is an advantage that it can be freely changed even when exposed, and no additional device is required.

However, it is an important issue to identify skilled forgery such as shoulder surfing in the dynamic signature, Smudge Attack in the sign of the signature, and so on.

In order to distinguish such falsification signatures, a lot of characteristic information about a dynamic action in which a signature is performed is required. In the past, studies related to signature authentication using a stylus pen have been mainly made, so that the signature shape, pen pressure, A method to utilize it as feature information has been proposed. However, there is a problem that this method can not be applied to a finger signature using a hand.

Meanwhile, the background art of the present invention is disclosed in Korean Patent Publication No. 10-2012-0047973 (2012.05.14).

SUMMARY OF THE INVENTION It is an object of the present invention to provide a signature authentication apparatus and a signature verification method capable of more precise signature classification.

A signature authentication apparatus according to the present invention includes: an input unit for receiving a signature; An acceleration sensor for measuring the acceleration of the signature authentication device; And a controller for performing signature authentication based on data input through the input unit, acceleration data measured through the acceleration sensor, and pre-stored authentication data.

In the present invention, the pre-stored authentication data is a learning model generated by learning data for signature registration.

In the present invention, the control unit performs sign authentication by inputting the two-axis coordinate values of each signature point sampled according to the set period and the three-axis acceleration value at the time point into the learning model.

In the present invention, the control unit calculates an unillustrated figure based on a value output from the learning model and a value input to the learning model, and compares the calculated unillustrated figure with a threshold value to determine whether or not the authentication is successful. .

In the present invention, the controller may calculate a mean squared error between a value output from the learning model and a value input to the learning model using the non-inference diagram.

In the present invention, the control unit may further input a distance between each signature point to the learning model to perform signature authentication.

In the present invention, the control unit may normalize data to be input to the learning model.

In the present invention, the learning model is a model of an autoencoder system.

A signature authentication method according to the present invention includes: a step in which a control unit receives a signature through an input unit; Measuring the acceleration of the device through which the signature is authenticated through the acceleration sensor while the controller receives the signature; Inputting the data and the measured acceleration data input through the input unit to the pre-built learning model to calculate an output value; Calculating a non-derivation based on the output value and the value input to the learning model; And comparing the calculated non-guide figure with a threshold value to determine whether the authentication is successful.

In the present invention, the data input through the input unit is a two-axis coordinate value of each signature point sampled according to a set period, and the acceleration data is a three-axis acceleration value sampled according to the setting period.

The signature authentication method according to the present invention may further comprise: before the step of receiving the signature, the control unit receiving a signature for signature registration at least once through the input unit; Measuring acceleration of the apparatus through the acceleration sensor while the control unit receives a signature for sign registration; And constructing the learning model by learning the data for signature registration by the control unit.

The signature authentication apparatus and method according to the present invention have the effect of improving the accuracy of the signature authentication by increasing the recognition rate of the signature and lowering the recognition rate of the fake signature by using the acceleration information as well as the shape information of the signature.

Further, the signature authentication apparatus and method according to the present invention have an effect of improving the accuracy of signature authentication by using an effective deep-processing technique for distinguishing falsification signatures.

1 is a block diagram illustrating a configuration of a signature authentication apparatus according to an embodiment of the present invention.
FIG. 2 is an exemplary diagram illustrating normalization of data input through an input unit in a signature authentication apparatus according to an exemplary embodiment of the present invention. Referring to FIG.
3 is an exemplary diagram illustrating a relationship between a two-axis coordinate value and a three-axis acceleration value of a signature performed in the signature authentication apparatus according to an exemplary embodiment of the present invention.
4 is an exemplary diagram illustrating a learning model of a signature authentication apparatus according to an embodiment of the present invention.
5 is a flowchart illustrating a signature registration process performed in the signature authentication method according to an embodiment of the present invention.
6 is a flowchart illustrating a signature authentication process performed in the signature authentication method according to an embodiment of the present invention.

Hereinafter, an embodiment of a signature authentication apparatus and method according to the present invention will be described with reference to the accompanying drawings. In this process, the thicknesses of the lines and the sizes of the components shown in the drawings may be exaggerated for clarity and convenience of explanation. In addition, the terms described below are defined in consideration of the functions of the present invention, which may vary depending on the intention or custom of the user, the operator. Therefore, definitions of these terms should be made based on the contents throughout this specification.

FIG. 1 is a block diagram illustrating a configuration of a signature authentication apparatus according to an embodiment of the present invention. FIG. 2 illustrates normalization of data input through an input unit in a signature authentication apparatus according to an exemplary embodiment of the present invention. FIG. 3 is an exemplary diagram for explaining a relationship between a two-axis coordinate value and a three-axis acceleration value of a signature performed in the signature authentication apparatus according to an embodiment of the present invention, and FIG. The signature verification apparatus according to an embodiment of the present invention will now be described with reference to FIG.

1, a signature authentication apparatus according to an exemplary embodiment of the present invention includes a control unit 100, an input unit 110, an acceleration sensor 120, and a storage unit 130.

The input unit 110 may receive a signature from a user. For example, the input unit 110 may be configured in the form of a touch screen to receive a signature input by the user. That is, when the user inputs a signature to the input unit 110 through a finger or the like, the coordinate value of the touched position can be grasped through the input unit 110. [

The control unit 100 can perform registration and authentication of signatures using such coordinate data. 2) of each signature point (indicated by a dotted line in FIG. 2) sampled according to a setting period (for example, 32 ms) by a signature inputted through the input unit 110, ) Coordinate values can be used to authenticate and register the signature. That is, this coordinate data is data related to the shape of the signature. Using the data periodically sampled in this way, even if the time information is not used separately, the same effect as using the signature speed information can be obtained.

The acceleration sensor 120 can measure the acceleration of the signature authentication device. For example, when the signature authentication apparatus according to the present embodiment is a smart phone, it is possible to measure the acceleration during signing using the sensor built in the smart phone. The acceleration information includes three axes (x axis, y Axis, z-axis).

That is, in the present embodiment, the accuracy of the signature authentication can be improved by using the acceleration information of the signature authentication device in which the signature is performed as the feature information about the dynamic behavior.

Specifically, as shown in FIG. 3, when the user inputs a signature, if the direction is changed, a rapid change occurs in the acceleration of the signature authentication device. Depending on the signature shape, speed, signer's habit, Value is determined. Therefore, the acceleration value can be used as feature information for distinguishing the forged signature.

2 and 3, the control unit 100 processes data input through the input unit 110 or measured acceleration data to analyze the signature data and the input signature. . Specifically, the initial value may be subtracted from the coordinate value or the acceleration value to match the starting point, and data normalization may be performed to match the size of the signature.

The storage unit 130 may store authentication data, that is, data related to a signature input for signature registration.

For example, the control unit 100 learns data for signature registration to generate a learning model, and stores the signature in the storage unit. (130).

That is, the control unit can perform signature authentication using the deep learning technique. Specifically, the control unit 100 can learn a signature for registration and construct a model of an autoencoder (AE) method.

The AE has a structure as shown in FIG. 4 and is a type of FNN (Feedforward Neural Network), which is a neural network that learns the inherent characteristics of data. Specifically, AE is a learning model that is learned to generate similar output values to the input values, and generates output values with high similarity for input values similar to the learned data, while output values for input values that do not have low similarity .

Figure 112016116575501-pat00001

The learning method of the AE can be described by Equation (1), where h is the result of encoding from signature data x through AE, z is the result of decoding h, ,

Figure 112016116575501-pat00002
Is an activation function, and L is a loss function. The learning process of AE in this relation is a process of finding W and W 'that minimizes the loss function, and if input of similar data is repeated, W and W' corresponding to the characteristic of the data can be calculated.

That is, when learning is performed by inputting a plurality of similar data, such an AE outputs data having a similarity degree between the input value and the output value with respect to the input value similar to the learned data. And outputs data having a low degree of similarity to the output value, that is, having a high degree of similarity.

Accordingly, the controller 100 inputs the two-axis coordinate values of the sampled signature points and the three-axis acceleration values at the corresponding points in the set period to the learning model to calculate an output value, and outputs the value output from the learning model and the learning By calculating the non-derivation based on the value entered in the model, a forged signature can be determined.

For example, the controller 100 may calculate a mean squared error between a value output from the learning model and a value input to the learning model as the non-inference. If the non-inference is smaller than the threshold It can be determined that the signature authentication is successful.

This learning model used by the control unit 100 is not a two-class method for dividing input data into a subject and a non-object, and is a one-class method. Therefore, Can be more effective. That is, in the two-class method, the fake signature is classified closer to the subject class, so the one class method is more suitable for authentication for security.

However, since the learning model used in the present invention is not limited to the model of the AE system, data having high similarity between the input value and the output value is output for an input value similar to the learned data. However, And a method of outputting data with a low degree of similarity between output values can be employed.

In addition, the control unit 100 may further perform a signature authentication by inputting the distance between each signature point to the learning model. The more the feature information is used, the more accurate the authentication can be.

Meanwhile, the signature authentication device may be a mobile device such as a mobile phone, a smart phone, a smart pad PMP (Portable Media Player), a PDA (Personal Digital Assistant), a portable game machine, a digital camera, an e-book reader, The present invention is not limited to this, and various devices can be employed as signature authentication devices.

FIG. 5 is a flowchart illustrating a signature registration process performed in the signature authentication method according to an embodiment of the present invention. FIG. 6 illustrates a signature authentication process performed in the signature authentication method according to an embodiment of the present invention. A signature authentication method according to the present embodiment will be described with reference to FIG.

As shown in FIG. 5, the control unit 100 may perform the signature registration process first.

Specifically, the control unit 100 receives a signature for signature registration through the input unit 110 (S200). At this time, the controller 100 can measure the acceleration of the signature authentication device through the acceleration sensor 120, and can use the input or measured data as signature registration data. For example, the control unit 100 determines whether the three-axis acceleration value sampled according to the setting period, which is the data indicating the two-axis coordinate value of each signature point sampled according to the setting period, Value can be used as signature data, and the distance between each signature point can be further utilized.

Next, the control unit 100 normalizes the inputted signature data (S210), and builds a learning model by learning the normalized signature data (S220). That is, the control unit 100 may process the signature data to perform learning, and may perform data processing or the like to match the starting point in addition to the normalization.

In addition, the control unit learns signatures so as to perform signature authentication using a deep learning technique, and can construct a model of an autoencoder (AE) method, for example.

Meanwhile, as shown in FIG. 6, in order to authenticate a signature, the control unit 100 first receives a signature through the input unit 110 (S300). Like the above-described step S200, the controller 100 can measure the acceleration of the signature authentication device through the acceleration sensor 120 and utilize it as signature data.

Next, the control unit 100 normalizes the input signature data (S310), inputs the normalized signature data to the learning model to calculate an output value (S320), and calculates a non-willow chart based on the normalized signature data and the output value S330). That is, as described above, the AE outputs data having similarity between the input value and the output value with respect to the input value similar to the learned data. However, with respect to the input value other than the input value, the similarity between the input value and the output value is low The control unit 100 can determine the fake signature by calculating the non-derivation based on the value output from the learning model and the value input to the learning model.

If the calculated non-default value is less than the threshold value, the control unit 100 determines that the authentication is successful. Otherwise, if the calculated non-default value is greater than or equal to the threshold value, the control unit 100 determines that the authentication fails (S340 to S360). For example, the controller 100 may calculate a mean squared error between a value output from the learning model and a value input to the learning model as a non-dioptric value. In this case, if the non- The control unit 100 can determine that the signature is correct, and in the opposite case, the control unit 100 determines that the signature is a forged signature or the like, and can perform the authentication failure process.

As described above, the signature authentication apparatus and method according to the embodiment of the present invention not only uses the shape information of the signature but also the acceleration information to increase the recognition rate of the signature, lower the recognition rate of the fake signature, and use an effective deep- Thereby improving the accuracy of signature authentication.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. I will understand. Accordingly, the technical scope of the present invention should be defined by the following claims.

100:
110: input unit
120: Accelerometer
130:

Claims (11)

An input unit for inputting a signature;
An acceleration sensor for measuring the acceleration of the signature authentication device; And
And a controller for performing signature authentication based on data input through the input unit, acceleration data measured through the acceleration sensor, and pre-stored authentication data,
Wherein the pre-stored authentication data is a learning model generated by learning data for signature registration, the learning model is a model of an autoencoder system,
Wherein the control unit inputs the two-axis coordinate value of each signature point sampled according to the set period and the three-axis acceleration value at the time point to the learning model to perform signature verification.
delete delete The method according to claim 1,
Wherein the control unit calculates an unillustrated figure based on a value output from the learning model and a value input to the learning model, and compares the calculated unillustrated figure with a threshold value to determine whether or not the authentication is successful. Authentication device.
5. The method of claim 4,
Wherein the control unit calculates a mean squared error between a value output from the learning model and a value input to the learning model by the non-inference.
The method according to claim 1,
Wherein the control unit further inputs the distance between each signature point to the learning model to perform signature authentication.
The method according to claim 1,
Wherein the control unit normalizes data to be input to the learning model.
delete The control unit receiving the signature through the input unit;
Measuring the acceleration of the device through which the signature is authenticated through the acceleration sensor while the controller receives the signature;
Inputting the data and the measured acceleration data input through the input unit to the pre-built learning model to calculate an output value;
Calculating a non-derivation based on the output value and the value input to the learning model; And
Comparing the calculated non-default value with a threshold value to determine whether authentication is successful,
The previously constructed learning model is a model of an autoencoder system,
Wherein the data input through the input unit is a two-axis coordinate value of each signature point sampled according to a setting period, and the acceleration data is a three-axis acceleration value sampled according to the setting period.
delete 10. The method of claim 9,
Before receiving the signature,
The control unit receiving a signature for sign registration at least once through the input unit;
Measuring acceleration of the apparatus through the acceleration sensor while the control unit receives a signature for sign registration; And
Further comprising the step of the control unit learning data for sign registration to construct the learning model.
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