CN115909414A

CN115909414A - Double-factor authentication method and device, computer equipment and storage medium

Info

Publication number: CN115909414A
Application number: CN202211677563.5A
Authority: CN
Inventors: 罗思维; 张艳霞; 房新彦
Original assignee: Tianyi IoT Technology Co Ltd
Current assignee: Tianyi IoT Technology Co Ltd
Priority date: 2022-12-26
Filing date: 2022-12-26
Publication date: 2023-04-04

Abstract

The embodiment of the invention discloses a double-factor authentication method, a double-factor authentication device, computer equipment and a storage medium. The method comprises the following steps: acquiring an image to be verified; performing color space conversion on the image to be verified to obtain a conversion result; comparing the faces of the conversion results to obtain comparison results; judging whether the comparison result is passed or not; if the comparison result is that the comparison is passed, performing static gesture analysis on the conversion result to obtain a password character string; carrying out password verification on the password character string to obtain a verification result; judging whether the checking result is passed or not; and if the verification result is verification passing, generating a signal of passing authentication. By implementing the method provided by the embodiment of the invention, the safety level of the face entrance guard can be improved.

Description

Double-factor authentication method and device, computer equipment and storage medium

Technical Field

The present invention relates to AI intelligence, and more particularly, to a method, an apparatus, a computer device, and a storage medium for dual factor authentication.

Background

With the development of a plurality of new technologies such as communication technology, image recognition technology and the like, a biological recognition access control system taking artificial intelligence and deep learning as directions is rapidly developed; the face recognition access control system is fast and convenient to pass, and user experience is good, so that the face recognition access control system is more and more used and popularized in an access control system.

At present, the use scenes of the face access control are more and more, because the face authentication is convenient and quick, and the operation threshold is low. But in fact, the face access control has unsafe factors, some face access controls do not support the live body detection function, and local photos or videos can be used for 'cheat verification' under certain conditions, so that the face access control safety level is reduced.

Therefore, it is necessary to design a method to improve the security level of the face access control.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provides a double-factor authentication method, a double-factor authentication device, computer equipment and a storage medium.

In order to achieve the purpose, the invention adopts the following technical scheme: a two-factor authentication method, comprising:

acquiring an image to be verified;

performing color space conversion on the image to be verified to obtain a conversion result;

comparing the face of the conversion result to obtain a comparison result;

judging whether the comparison result is passed or not;

if the comparison result is passed, performing static gesture analysis on the conversion result to obtain a password character string;

carrying out password verification on the password character string to obtain a verification result;

judging whether the checking result is passed or not;

and if the verification result is verification passing, generating a signal of passing authentication.

The further technical scheme is as follows: the face comparison of the conversion result is performed to obtain a comparison result, which includes:

extracting the face information in the conversion result to obtain the face information to be verified;

and comparing the face information to be verified with the archived face information to obtain a comparison result.

The further technical scheme is as follows: the static gesture analysis is performed on the conversion result to obtain a password character string, and the method comprises the following steps:

and performing static gesture analysis on the conversion result based on the enhanced CNN and RandomForest recognition method to obtain a password character string.

The further technical scheme is as follows: the method for recognizing the random forest based on the enhanced CNN and the enhanced RandomForest is used for performing static gesture analysis on the conversion result to obtain a password character string, and comprises the following steps:

carrying out binarization on the conversion result, and carrying out gesture segmentation to obtain two pieces of image information except for the human face;

respectively storing two image information except for the human face from left to right according to the pixel sequence to obtain a first sub-image and a second sub-image;

recognizing gestures in the first sub-image and the second sub-image in sequence to obtain recognition results;

acquiring a preset character corresponding to the recognition result from a preset feature table;

and splicing the preset characters according to the number sequence of the first sub-image and the second sub-image to obtain the password character string.

The further technical scheme is as follows: the binarization and gesture segmentation are carried out on the conversion result to obtain two pieces of image information except for the human face, and the method comprises the following steps:

and carrying out binarization on the conversion result by adopting an Otsu algorithm, and carrying out gesture segmentation on the binarization result to obtain two image information except the human face.

The further technical scheme is as follows: the method for recognizing the gestures in the first sub-image and the second sub-image sequentially based on the enhanced CNN and the RandomForest recognition method to obtain recognition results comprises the following steps:

extracting feature vectors in the first sub-image and the second sub-image by utilizing a feature extraction function of the convolutional network;

and classifying the feature vectors by using a random forest classifier to obtain a recognition result.

The invention also provides a double-factor authentication device, comprising:

the image acquisition unit is used for acquiring an image to be verified;

the conversion unit is used for carrying out color space conversion on the image to be verified to obtain a conversion result;

the comparison unit is used for comparing the human face of the conversion result to obtain a comparison result;

the first judging unit is used for judging whether the comparison result passes the comparison;

the analysis unit is used for carrying out static gesture analysis on the conversion result to obtain a password character string if the comparison result is that the comparison is passed;

the verification unit is used for carrying out password verification on the password character string to obtain a verification result;

a second judging unit, configured to judge whether the check result passes the check;

and the signal generating unit is used for generating a signal which passes the authentication if the verification result is verification passing.

The further technical scheme is as follows: the alignment unit comprises:

the information extraction subunit is used for extracting the face information in the conversion result to obtain the face information to be verified;

and the information comparison subunit is used for comparing the face information to be verified with the archived face information to obtain a comparison result.

The invention also provides a computer device, which comprises a memory and a processor, wherein the memory is stored with a computer program, and the processor executes the computer program to realize the method.

The invention also provides a storage medium storing a computer program which, when executed by a processor, is operable to carry out the method as described above.

Compared with the prior art, the invention has the beneficial effects that: the invention obtains the skin color likelihood map by performing color space conversion on the image to be verified, performs face comparison on the map, performs static gesture analysis after the face comparison is passed, performs gesture recognition by adopting a recognition method based on enhanced CNN and RandomForest to improve the recognition accuracy and recall rate, and realizes the improvement of the safety level of face entrance guard by combining the verification of face recognition and gesture recognition and double-factor authentication.

The invention is further described below with reference to the accompanying drawings and specific embodiments.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic view of an application scenario of a two-factor authentication method according to an embodiment of the present invention;

fig. 2 is a schematic flowchart of a two-factor authentication method according to an embodiment of the present invention;

fig. 3 is a schematic sub-flow diagram of a dual-factor authentication method according to an embodiment of the present invention;

fig. 4 is a sub-flow diagram of a dual-factor authentication method according to an embodiment of the present invention;

fig. 5 is a sub-flow diagram of a dual-factor authentication method according to an embodiment of the present invention;

fig. 6 is a schematic block diagram of a dual-factor authentication apparatus according to an embodiment of the present invention;

fig. 7 is a schematic block diagram of a comparison unit of a dual-factor authentication device according to an embodiment of the present invention;

fig. 8 is a schematic block diagram of a parsing unit of a two-factor authentication device according to an embodiment of the present invention;

fig. 9 is a schematic block diagram of an identification subunit of a dual-factor authentication device according to an embodiment of the present invention;

FIG. 10 is a schematic block diagram of a computer device provided by an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

Referring to fig. 1 and fig. 2, fig. 1 is a schematic view of an application scenario of a dual-factor authentication method according to an embodiment of the present invention. Fig. 2 is a schematic flowchart of a two-factor authentication method according to an embodiment of the present invention. The double-factor authentication method is applied to a server. The server performs data interaction with the camera and the entrance guard, realizes that the camera is used for shooting an image to be verified, and the image to be verified is converted into a color space, so that the skin color in another domain has better clustering performance and certain anti-interference capability. And obtaining a skin color likelihood map through skin color model and dynamic threshold segmentation, and performing face comparison, password identification and double-factor authentication on the skin color likelihood map to improve the face access security level.

Fig. 2 is a schematic flowchart of a two-factor authentication method according to an embodiment of the present invention. As shown in fig. 2, the method includes the following steps S110 to S150.

And S110, acquiring an image to be verified.

In this embodiment, the image to be verified refers to an image with head information and a recognizable gesture.

When the people appear before the camera, the candid photograph of the camera is activated, recognizable gestures need to be made on two sides of the face of the people by two hands of the people at the moment, and the camera collects pictures and transmits the pictures to the server.

And S120, performing color space conversion on the image to be verified to obtain a conversion result.

In this embodiment, the conversion result refers to a result formed by performing color space conversion on the image to be verified.

Specifically, the image to be verified is converted from an RGB image to an image in YCrCb space.

And S130, carrying out face comparison on the conversion result to obtain a comparison result.

In this embodiment, the comparison result indicates whether the face information of the conversion result matches with the stored face information.

In an embodiment, referring to fig. 3, the step S130 may include steps S131 to S132.

S131, extracting the face information in the conversion result to obtain the face information to be verified.

In this embodiment, the face information to be verified refers to an image in which only a face exists.

S132, comparing the face information to be verified with the archived face information to obtain a comparison result.

S140, judging whether the comparison result is passed or not;

if the comparison result is not that the comparison is passed, executing the step S110;

s150, if the comparison result is that the comparison is passed, static gesture analysis is carried out on the conversion result to obtain the password character string.

In this embodiment, the password character string refers to the character content of the password.

Specifically, static gesture analysis is performed on the conversion result based on the enhanced CNN and RandomForest recognition method to obtain the password character string.

In an embodiment, referring to fig. 3, the step S150 may include steps S151 to S155.

And S151, carrying out binarization on the conversion result, and carrying out gesture segmentation to obtain two pieces of image information except for the human face.

In this embodiment, the two pieces of image information except for the face refer to image information formed by binarizing and gesture-dividing an image including a static gesture.

Specifically, an Otsu algorithm is adopted to carry out binarization on the conversion result, and gesture segmentation is carried out on the binarization result so as to obtain two pieces of image information except the human face.

At most one of the two image information is an empty image.

Of course, in one embodiment, after the conversion result is input, the conversion result needs to be buffered.

S152, respectively storing two image information except the human face from left to right according to the pixel sequence to obtain a first sub-image and a second sub-image;

s153, recognizing the gestures in the first sub-image and the second sub-image in sequence to obtain recognition results.

In this embodiment, the recognition result refers to the meaning of the gesture in the image.

In an embodiment, referring to fig. 5, the step S153 may include steps S1531 to S1532.

S1531, extracting feature vectors in the first sub-image and the second sub-image by using a feature extraction function of a convolutional network;

s1532, classifying the feature vectors by using a random forest classifier to obtain an identification result.

In this embodiment, the null image obtains a preset character corresponding to null.

S154, acquiring a preset character corresponding to the recognition result from a preset feature table;

s155, splicing the preset characters according to the number sequence of the first sub-image and the second sub-image to obtain a password character string.

S160, carrying out password verification on the password character string to obtain a verification result;

s170, judging whether the checking result is passed;

and S180, if the verification result is that the verification is passed, generating a signal that the authentication is passed.

In this embodiment, if the verification passes, the cache is cleared, and if the verification fails, the conversion result of the cache is read, the process of password string identification is repeated, and if the verification fails again, the authentication is re-performed.

If the verification result is not a verification pass, the step S150 is executed.

And after passing the password verification sub-process, completing complete double-factor authentication, and outputting a gate magnetic control signal by the system.

In this embodiment, in order to improve the accuracy of gesture recognition, an enhanced CNN and Random Forest based recognition method is used. According to the method, firstly, after a skin color likelihood graph is obtained, binaryzation is carried out on a static gesture picture by using an Otsu algorithm, and gesture segmentation is completed after noise is removed. And then extracting feature vectors by using a feature extraction function of the convolutional network, and finally classifying the feature vectors by using a random forest classifier. On one hand, the convolutional neural network has the capability of layered learning and can collect more representative information on the picture; on the other hand, the random forest has randomness for sample and feature selection, and each decision tree result is averaged, so that the over-fitting problem is not easy to occur.

According to the double-factor authentication method, the color space conversion is carried out on the image to be verified to obtain the skin color likelihood map, the face comparison is carried out on the map, the static gesture analysis is carried out after the face comparison is passed, the gesture recognition is carried out by adopting the recognition method based on the enhanced CNN and the RandomForest to improve the recognition accuracy and the recall rate, the verification of the face recognition and the gesture recognition is combined, and the double-factor authentication is carried out to improve the safety level of the entrance guard of the face.

Fig. 6 is a schematic block diagram of a two-factor authentication apparatus 300 according to an embodiment of the present invention. As shown in fig. 6, the present invention also provides a dual-factor authentication device 300 corresponding to the above dual-factor authentication method. The two-factor authentication apparatus 300 includes a unit for performing the above-described two-factor authentication method, and the apparatus may be configured in a server. Specifically, referring to fig. 6, the dual-factor authentication apparatus 300 includes an image obtaining unit 301, a converting unit 302, a comparing unit 303, a first determining unit 304, an analyzing unit 305, a verifying unit 306, a second determining unit 307, and a signal generating unit 308.

An image acquisition unit 301, configured to acquire an image to be verified; a conversion unit 302, configured to perform color space conversion on the image to be verified to obtain a conversion result; a comparison unit 303, configured to perform face comparison on the conversion result to obtain a comparison result; a first determining unit 304, configured to determine whether the comparison result passes the comparison; an analysis unit 305, configured to perform static gesture analysis on the conversion result to obtain a password character string if the comparison result is that the comparison is passed; a checking unit 306, configured to perform password checking on the password character string to obtain a checking result; a second judging unit 307, configured to judge whether the verification result is a verification pass; a signal generating unit 308, configured to generate a signal that the authentication is passed if the verification result is verification passed.

In one embodiment, as shown in fig. 7, the comparing unit 303 includes an information extracting sub-unit 3031 and an information comparing sub-unit 3032.

An information extraction subunit 3031, configured to extract the face information in the conversion result to obtain the face information to be verified; and the information comparison subunit 3032 is configured to compare the face information to be verified with the archived face information to obtain a comparison result.

In an embodiment, the parsing unit 305 is configured to perform static gesture parsing on the conversion result based on the enhanced CNN and RandomForest recognition method to obtain a password character string.

In an embodiment, as shown in fig. 8, the parsing unit 305 includes a binarization subunit 3051, a storage subunit 3052, an identification subunit 3053, a character acquisition subunit 3054, and a concatenation subunit 3055.

A binarization subunit 3051, configured to perform binarization on the conversion result, and perform gesture segmentation to obtain two pieces of image information except for a human face; the storage subunit 3052 is configured to store, from left to right in the pixel sequence, two pieces of image information other than the face respectively to obtain a first sub-image and a second sub-image; the recognition subunit 3053 is configured to sequentially recognize the gestures in the first sub-image and the second sub-image to obtain a recognition result; a character obtaining subunit 3054, configured to obtain, from a preset feature table, a preset character corresponding to the recognition result; and the splicing subunit 3055, configured to splice the preset characters according to the number sequences of the first sub-image and the second sub-image, so as to obtain a password character string.

In an embodiment, the binarization subunit 3051 is configured to binarize the conversion result by using an algorithm of madzu corporation, and perform gesture segmentation on the binarization result to obtain two pieces of image information except for a human face.

In one embodiment, as shown in fig. 9, the recognition subunit 3053 includes an extraction module 30531 and a classification module 30532.

An extracting module 30531, configured to extract feature vectors in the first sub-image and the second sub-image by using a feature extraction function of a convolutional network; a classification module 30532, configured to classify the feature vector using a random forest classifier to obtain an identification result.

It should be noted that, as can be clearly understood by those skilled in the art, the specific implementation processes of the dual-factor authentication apparatus 300 and each unit may refer to the corresponding descriptions in the foregoing method embodiments, and for convenience and brevity of description, no further description is provided herein.

The above-described two-factor authentication apparatus 300 may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 10.

Referring to fig. 10, fig. 10 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 may be a server, wherein the server may be an independent server or a server cluster composed of a plurality of servers.

Referring to fig. 10, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.

The non-volatile storage medium 503 may store an operating system 5031 and computer programs 5032. The computer programs 5032 comprise program instructions that, when executed, cause the processor 502 to perform a two-factor authentication method.

The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.

The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 can be caused to perform a two-factor authentication method.

The network interface 505 is used for network communication with other devices. Those skilled in the art will appreciate that the configuration shown in fig. 10 is a block diagram of only a portion of the configuration relevant to the present teachings and is not intended to limit the computing device 500 to which the present teachings may be applied, and that a particular computing device 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

Wherein the processor 502 is configured to run the computer program 5032 stored in the memory to perform the steps of:

acquiring an image to be verified; performing color space conversion on the image to be verified to obtain a conversion result; comparing the face of the conversion result to obtain a comparison result; judging whether the comparison result is passed or not; if the comparison result is that the comparison is passed, performing static gesture analysis on the conversion result to obtain a password character string; carrying out password verification on the password character string to obtain a verification result; judging whether the checking result is passed or not; and if the verification result is verification passing, generating a signal of passing authentication.

In an embodiment, when the processor 502 implements the step of performing face comparison on the conversion result to obtain a comparison result, the following steps are specifically implemented:

extracting the face information in the conversion result to obtain the face information to be verified; and comparing the face information to be verified with the archived face information to obtain a comparison result.

In an embodiment, when the processor 502 performs the step of performing static gesture analysis on the conversion result to obtain the password character string, the following steps are specifically implemented:

and performing static gesture analysis on the conversion result based on the enhanced CNN and RandomForest identification method to obtain a password character string.

In an embodiment, when the processor 502 implements the step of performing static gesture analysis on the conversion result based on the enhanced CNN and RandomForest recognition method to obtain the password character string, the following steps are specifically implemented:

carrying out binarization on the conversion result, and carrying out gesture segmentation to obtain two pieces of image information except for the face; respectively storing two image information except for the human face from left to right according to the pixel sequence to obtain a first sub-image and a second sub-image; recognizing gestures in the first sub-image and the second sub-image in sequence to obtain recognition results; acquiring a preset character corresponding to the recognition result from a preset feature table; and splicing the preset characters according to the number sequence of the first sub-image and the second sub-image to obtain the password character string.

In an embodiment, when the processor 502 implements the steps of binarizing the conversion result and performing gesture segmentation to obtain two image information except for a human face, the following steps are specifically implemented:

In an embodiment, the processor 502 performs binarization on the conversion result by using the madzu algorithm, and performs gesture segmentation on the binarization result to obtain two pieces of image information except for a human face.

When the steps are carried out, the following steps are concretely realized:

extracting feature vectors in the first sub-image and the second sub-image by utilizing a feature extraction function of the convolutional network; and classifying the feature vectors by using a random forest classifier to obtain a recognition result.

It should be understood that, in the embodiment of the present application, the processor 502 may be a Central Processing Unit (CPU), and the processor 502 may also be other general-purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

It will be understood by those skilled in the art that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program instructing relevant hardware. The computer program includes program instructions, and the computer program may be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.

Accordingly, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program, when executed by a processor, causes the processor to perform the steps of:

acquiring an image to be verified; performing color space conversion on the image to be verified to obtain a conversion result; comparing the faces of the conversion results to obtain comparison results; judging whether the comparison result is passed or not; if the comparison result is passed, performing static gesture analysis on the conversion result to obtain a password character string; carrying out password verification on the password character string to obtain a verification result; judging whether the checking result is passed or not; and if the verification result is verification passing, generating a signal of passing authentication.

In an embodiment, when the processor executes the computer program to perform the face comparison on the conversion result to obtain a comparison result, the following steps are specifically implemented:

In an embodiment, when the processor executes the computer program to perform the static gesture analysis on the conversion result to obtain the password character string, the following steps are specifically implemented:

In an embodiment, when the processor executes the computer program to implement the step of performing static gesture analysis on the conversion result based on the enhanced CNN and RandomForest recognition method to obtain a password character string, the following steps are specifically implemented:

carrying out binarization on the conversion result, and carrying out gesture segmentation to obtain two pieces of image information except for the human face; respectively storing two image information except for the human face from left to right according to the pixel sequence to obtain a first sub-image and a second sub-image; recognizing gestures in the first sub-image and the second sub-image in sequence to obtain recognition results; acquiring a preset character corresponding to the recognition result from a preset feature table; and splicing the preset characters according to the number sequence of the first sub-image and the second sub-image to obtain a password character string.

In an embodiment, when the processor executes the computer program to implement the step of performing static gesture analysis on the conversion result based on the enhanced CNN and randomfortest recognition method to obtain a password character string, the following steps are specifically implemented:

In an embodiment, when the processor executes the computer program to realize the step of sequentially recognizing the gestures in the first sub-image and the second sub-image based on the enhanced CNN and RandomForest recognition method to obtain the recognition result, the following steps are specifically realized:

The storage medium may be a usb disk, a removable hard disk, a Read-only memory (ROM), a magnetic disk or an optical disk, and various computer readable storage media that can store program codes.

Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, various elements or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented.

The steps in the method of the embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the invention can be combined, divided and deleted according to actual needs. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a terminal, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention.

While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A two-factor authentication method, comprising:

acquiring an image to be verified;

comparing the face of the conversion result to obtain a comparison result;

judging whether the comparison result is passed or not;

if the comparison result is that the comparison is passed, performing static gesture analysis on the conversion result to obtain a password character string;

judging whether the checking result is passed;

2. The method of claim 1, wherein the comparing the face of the converted result to obtain a comparison result comprises:

3. The two-factor authentication method of claim 1, wherein the performing static gesture parsing on the conversion result to obtain a password character string comprises:

and performing static gesture analysis on the conversion result based on the enhanced CNN and Random Forest identification method to obtain a password character string.

4. The two-factor authentication method of claim 1, wherein the performing static gesture analysis on the conversion result based on the enhanced CNN and Random Forest recognition method to obtain a password character string comprises:

sequentially recognizing the gestures in the first sub-image and the second sub-image to obtain recognition results;

5. The two-factor authentication method according to claim 4, wherein the binarizing the conversion result and performing gesture segmentation to obtain two image information except for a human face comprises:

6. The two-factor authentication method of claim 4, wherein the recognizing the gestures in the first sub-image and the second sub-image sequentially based on the enhanced CNN and Random Forest recognition methods to obtain the recognition result comprises:

extracting feature vectors in the first sub-image and the second sub-image by using a feature extraction function of a convolutional network;

7. A two-factor authentication apparatus, comprising:

the image acquisition unit is used for acquiring an image to be verified;

the conversion unit is used for performing color space conversion on the image to be verified to obtain a conversion result;

the comparison unit is used for comparing the faces of the conversion results to obtain comparison results;

a second judging unit, configured to judge whether the verification result passes the verification;

8. The dual-factor authentication device as claimed in claim 7, wherein the comparing unit comprises:

9. A computer device, characterized in that the computer device comprises a memory, on which a computer program is stored, and a processor, which when executing the computer program implements the method according to any of claims 1 to 6.

10. A storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.