CN112115925A - Face recognition method and device and computer readable storage medium - Google Patents
Face recognition method and device and computer readable storage medium Download PDFInfo
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Abstract
The invention discloses a face recognition method, a face recognition device and a computer readable storage medium, wherein the face recognition method comprises the following steps: by acquiring interference information; modulating the interference information into a light intensity control signal, and sending the light intensity control signal to the lighting device so that the lighting device adjusts the light intensity according to the light intensity control signal; acquiring a face image detected by a camera, wherein the face image comprises light intensity information of the lighting device; and carrying out face recognition according to the face image. The invention is beneficial to improving the safety of the face recognition system.
Description
Technical Field
The present invention relates to the field of image recognition, and in particular, to a method and an apparatus for face recognition and a computer-readable storage medium.
Background
Interference for face recognition systems. The face recognition system is deceived by forging the face by using equipment such as a photo, a 3D face mask and the like in a presentation attack mode, the operation is simple, the cost is low, the technical threshold is low, and the interference effect is good.
Disclosure of Invention
The embodiment of the invention provides a face recognition method, a face recognition device and a computer readable storage medium, and aims to solve the bottleneck problem that the face recognition system is difficult to further break through security because the threat of the existing attack method to the face recognition system is low in a practical scene.
A first aspect of an embodiment of the present invention provides a face recognition method, where the face recognition method includes:
acquiring interference information;
modulating the interference information into a light intensity control signal, and sending the light intensity control signal to a lighting device so that the lighting device adjusts the light intensity according to the light intensity control signal;
acquiring a face image detected by a camera, wherein the face image comprises light intensity information of the lighting device;
and carrying out face recognition according to the face image.
In an embodiment, the step of obtaining interference information includes:
determining an interference type;
and acquiring the interference information according to the interference type.
In an embodiment, the step of obtaining the interference information according to the interference type includes:
and when the interference type is a thick light and dark stripe, generating long-run interference information.
In an embodiment, the step of obtaining the interference information according to the interference type includes:
and when the interference type is the thin bright and dark stripe, generating short-run interference information.
In one embodiment, the step of modulating the interference information into the light intensity control signal comprises:
determining a curve change relation between light intensity and time points in the interference information according to the interference information;
and generating the light intensity control signals of high and low levels according to the curve change relationship.
In an embodiment, after the step of performing face recognition according to the face image, the method further includes:
determining a first image gradient of the face image;
comparing the first image gradient with a prestored second image gradient to obtain a comparison result;
and determining a face recognition result according to the comparison result.
In an embodiment, the step of determining a face recognition result according to the comparison result includes:
when the similarity between the first image gradient and the second image gradient is greater than or equal to a preset value, determining that the face recognition is successful;
and when the similarity of the first image gradient and the second image gradient is lower than the preset value, determining that the face recognition fails.
In an embodiment, after the step of determining the face recognition result according to the comparison result, the method further includes:
and outputting the face recognition result.
In order to achieve the above object, the present invention provides a face recognition apparatus, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the face recognition method as described above when executing the computer program.
To achieve the above object, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the face recognition method as described above.
The human face recognition method, the human face recognition device and the computer readable storage medium provided by the invention have the advantages that the interference information is obtained; modulating the interference information into a light intensity control signal, and sending the light intensity control signal to a lighting device so that the lighting device adjusts the light intensity according to the light intensity control signal; acquiring a face image detected by a camera, wherein the face image comprises light intensity information of the lighting device; and carrying out face recognition according to the face image. Because the human face recognition device implants the interference information in the stage of collecting the human face image before the human face recognition is carried out, so as to recognize the human face information in the human face image carrying the interference information, and the safety of the human face recognition system can be improved by overcoming the interference information.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of a hardware architecture of a face recognition apparatus according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of a face recognition method according to the present invention;
FIG. 3 is a flowchart illustrating a second embodiment of a face recognition method according to the present invention;
FIG. 4 is a flowchart illustrating a face recognition method according to a third embodiment of the present invention;
FIG. 5 is a schematic flow chart of a face recognition method according to a fourth embodiment of the present invention;
FIG. 6 is a flowchart illustrating a fifth embodiment of a face recognition method according to the present invention;
FIG. 7 is a flowchart illustrating a sixth embodiment of a face recognition method according to the present invention;
FIG. 8 is a flowchart illustrating a detailed process of step 70 of a seventh embodiment of a face recognition method according to the present invention;
fig. 9 is a flowchart illustrating an eighth embodiment of a face recognition method according to the present invention.
Detailed Description
For a better understanding of the above technical solutions, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The main solution of the invention is: the human face recognition method, the human face recognition device and the computer readable storage medium provided by the invention have the advantages that the interference information is obtained; modulating the interference information into a light intensity control signal, and sending the light intensity control signal to a lighting device so that the lighting device adjusts the light intensity according to the light intensity control signal; acquiring a face image detected by a camera, wherein the face image comprises light intensity information of the lighting device; and carrying out face recognition according to the face image.
Because the human face recognition device implants the interference information in the stage of collecting the human face image before the human face recognition is carried out, so as to recognize the human face information in the human face image carrying the interference information, and the bottleneck of the safety of the human face recognition can be improved by overcoming the interference information.
Referring to fig. 1, fig. 1 is a schematic diagram of a hardware architecture of a face recognition apparatus according to an embodiment of the present invention.
The embodiment of the invention relates to a controller, which comprises: a processor 101, e.g. a CPU, a memory 102, a communication bus 103, an acquisition module 104, a lighting module 105. Wherein a communication bus 103 is used for enabling the connection communication between these components.
The memory 102 may be a high-speed RAM memory or a non-volatile memory (e.g., a disk memory). As shown in fig. 1, a memory, which is a kind of computer storage medium, may include a detection program therein; and the processor 101 may be configured to call the detection program stored in the memory 102 and perform the following operations:
acquiring interference information;
modulating the interference information into a light intensity control signal, and sending the light intensity control signal to a lighting device so that the lighting device adjusts the light intensity according to the light intensity control signal;
acquiring a face image detected by a camera, wherein the face image comprises light intensity information of the lighting device;
and carrying out face recognition according to the face image.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
determining an interference type;
and acquiring the interference information according to the interference type.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
and when the interference type is a thick light and dark stripe, generating long-run interference information.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
and when the interference type is the thin bright and dark stripe, generating short-run interference information.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
determining a curve change relation between light intensity and time points in the interference information according to the interference information;
and generating the light intensity control signals of high and low levels according to the curve change relationship.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
determining a first image gradient of the face image;
comparing the first image gradient with a prestored second image gradient to obtain a comparison result;
and determining a face recognition result according to the comparison result.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
when the similarity between the first image gradient and the second image gradient is greater than or equal to a preset value, determining that the face recognition is successful;
and when the similarity of the first image gradient and the second image gradient is lower than the preset value, determining that the face recognition fails.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
and outputting the face recognition result.
In the technical scheme provided by the embodiment, interference information is acquired; modulating the interference information into a light intensity control signal, and sending the light intensity control signal to a lighting device so that the lighting device adjusts the light intensity according to the light intensity control signal; acquiring a face image detected by a camera, wherein the face image comprises light intensity information of the lighting device; and carrying out face recognition according to the face image. Because the human face recognition device implants the interference information in the stage of collecting the human face image before the human face recognition is carried out, so as to recognize the human face information in the human face image carrying the interference information, and the bottleneck of the safety of the human face recognition can be improved by overcoming the interference information.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Referring to fig. 2, fig. 2 is a first embodiment of the face recognition method of the present invention, which includes the following steps:
in step S10, interference information is acquired.
Attacks against face recognition systems include presence attacks and adversarial attacks. The face recognition system is deceived by forging the face by using equipment such as a photo, a 3D face mask and the like in the presence of attack, the operation is simple, the cost is low, the technical threshold is low, and the attack effect is good, but at present, a plurality of attack presence detection schemes aiming at the attack method are proposed and perfected one after another, and the simple attack presence detection scheme is difficult to escape from the detection technology. The adversarial attack is a novel attack method, which uses the loophole on the artificial intelligence algorithm to elaborately construct an input sample, even if human eyes can detect the abnormal situation, the human face recognition system can be misled, and a good attack effect is generated. For example: antagonistic eyeglasses, antagonistic stickers, and the like. Experiments prove that after the user wears the 'antagonism glasses' or pastes the 'antagonism paster', the face recognition system can wrongly judge the identity of the user.
In this embodiment, optionally, the face recognition device of the present invention includes, but is not limited to, an interference information input module, an interference information implantation and signal modulation module, an illumination driving module, an illumination lamp, an acquisition module, and a face recognition system, where the interference information input module, the interference information implantation and signal adjustment module, the illumination driving module, and the face recognition system are all located in a processor of the face recognition device, the interference information input module is connected to the interference information implantation and signal modulation module, and the interference information implantation and signal modulation module is connected to the illumination driving module and the illumination lamp, and modulates a transmission signal in which interference information is implanted onto the illumination lamp to emit a high-speed bright and dark flashing light signal that is imperceptible to human eyes. The signal modulation module modulates the lighting light, and attack gradient information is regularly implanted in the image acquisition stage of the face recognition system, so that the face recognition system cannot detect faces and even cannot recognize different faces, and denial of service attack and escape attack are realized. The interference information may be, but is not limited to, modulating a transmission signal of the implantation attack information onto a lighting fixture to emit a high-speed bright-dark flashing light signal which is not detected by human eyes, such as an LED lighting fixture.
And step S20, modulating the interference information into a light intensity control signal, and sending the light intensity control signal to a lighting device so that the lighting device adjusts the light intensity according to the light intensity control signal.
In this embodiment, the above-mentioned interference information may be input in the form of digital information through the interference information input module to attack the face recognition device, i.e. the human recognition system.
And step S30, acquiring a face image detected by the camera, wherein the face image comprises the light intensity information of the lighting device.
In the technical scheme of this embodiment, the camera is an acquisition module, the camera is used to acquire a face image required by a face recognition system, and simultaneously receive a high-speed light and dark flashing light signal emitted by an LED lighting fixture, the acquired high-speed light and dark flashing light signal forms light and dark stripes on the image, and when the camera photographs a face, due to a rolling shutter effect, one frame of obtained picture includes signals at different times. Therefore, in the face image obtained by the camera, the implanted interference information appears as light and dark stripes superimposed on the face image, and since the interference information has been implanted in the above steps, the face image acquired by the camera in this step has been implanted with the interference information theoretically.
And step S40, performing face recognition according to the face image.
In this embodiment, the superimposed light and dark stripes generate additional gradient information on the face image, so that an interference effect is generated on a face detection unit, a feature extraction unit and the like of the face recognition system, the face detection unit can be caused to fail, thereby implementing denial of service attack, or the difference degree of the face image is reduced, thereby implementing escape attack.
In the technical scheme of the embodiment, interference information is acquired; modulating the interference information into a light intensity control signal, and sending the light intensity control signal to a lighting device so that the lighting device adjusts the light intensity according to the light intensity control signal; acquiring a face image detected by a camera, wherein the face image comprises light intensity information of the lighting device; and carrying out face recognition according to the face image. Because the human face recognition device implants the interference information in the stage of collecting the human face image before the human face recognition is carried out, so as to recognize the human face information in the human face image carrying the interference information, and the safety of the human face recognition system can be improved by overcoming the interference information.
Referring to fig. 3, fig. 3 is a second embodiment of the face recognition method of the present invention, and based on the first embodiment, the step S10 includes:
in step S11, the interference type is determined.
In the present embodiment, the types of interference include, but are not limited to, denial of service attacks and escape attacks, where an attacker completes an attack to spoof a target system by constructing a specific input sample without changing the target machine learning system. For example, an attacker may modify the non-critical features of a malware sample so that it is judged as benign by an antivirus system, thereby bypassing detection. The samples that an attacker purposely constructs in order to implement an escape attack are often referred to as "challenge samples". As long as a machine learning model does not perfectly learn the discriminant rules, it is possible for an attacker to construct countermeasures to fool the machine learning system. For example, researchers have attempted to mimic human visual functions on computers, but because the human visual mechanism is too complex, the rules on which the two systems rely in discriminating objects differ somewhat. The confrontational picture uses just these differences so that the machine learning model yields results that are distinct from human vision. One well-known escape sample is the example of the panda and gibbon classification used by Ian Goodfellow at the 2015 ICLR meeting. The attacked target is a deep learning research system from google. The system can accurately distinguish pictures such as pandas, gibbons and the like by utilizing the convolutional neural network. However, an attacker can add a small amount of interference to the panda picture, and the generated picture can still clearly judge that the panda is a panda for people, but the deep learning system can misunderstand that the panda is a gibbon.
And step S12, acquiring the interference information according to the interference type.
In this embodiment, attack information to be implanted is calculated according to a face recognition system requiring interference and an interference type to be implemented, where the interference type and the attack information are binding relationships associated in advance, and interference information can be generated quickly when determining the interference type.
In the technical solution of this embodiment, by determining to associate the interference type with the interference information, the interference information can be quickly generated after the interference type is determined, and the execution efficiency of the face recognition method of this embodiment is improved.
Referring to fig. 4, fig. 4 is a third embodiment of the face recognition method of the present invention, and based on the second embodiment, the step S12 includes:
and step S121, when the interference type is a coarse light and dark stripe, generating long-run interference information.
In the technical scheme of this embodiment, if denial of service attack is to be implemented, an attack information segment with more long-link 0 bits can be selected, even if long-run interference information is used, the type of interference information can enable a camera to implant thicker stripe information during imaging, so that thick stripes appear on an imaged face image, and compared with thin stripe information implanted by short-run interference information, the recognition difficulty is higher, if a face recognition system can recognize a long-run face image, the stability of the face recognition system can be considered to be higher, and by implanting long-run interference information which is more suitable for denial of service attack, the success rate of implementing denial of service attack on the face recognition system can be improved.
Referring to fig. 5, fig. 5 is a fourth embodiment of the face recognition method of the present invention, and based on the second embodiment, the step S12 includes:
and step S122, when the interference type is a fine light and shade stripe, generating short-run interference information.
In the technical scheme of this embodiment, when the interference type is a fine light and dark stripe, an attack information segment with a small number of short-link 0 bits is selected, and not only the short-run interference information is used, but also the interference information of this type can enable a camera to implant fine stripe information during imaging, so that a fine stripe appears in an imaged human face image, and compared with coarse stripe information implanted by long-run interference information, the identification difficulty is weak, and a condition required for implementing escape attack is met, thereby being beneficial to improving the success rate of implementing escape attack on a human face identification system.
Referring to fig. 6, fig. 6 is a fifth embodiment of the face recognition method of the present invention, and based on any one of the first to fourth embodiments, the step S20 includes:
and step S21, determining the curve change relation between the light intensity and the time point in the interference information according to the interference information.
And step S22, generating the light intensity control signal of high and low level according to the curve change relation.
In this embodiment, the interference information implanting and signal modulating module in the face recognition device converts the calculated interference information into high and low level modulating signals, which can be described by the following formula:
ldc×s (t)=LED (t)
wherein, idc in the above formula is the brightness value of the lighting lamp, i.e. the above light intensity, s (t) is the digital information to be implanted which changes with time t, and LED (t) is the composite signal of the two, is the modulation signal to be loaded on the LED lamp, and changes with time t; s (t) is a binary digital signal, and takes the value of "0" or "1", therefore, the value of LED (t) takes the value of "0" or "ldc。
The illumination driving module in the face recognition device is used for driving the LED illumination lamp to emit light by the synthesized modulation signal LED (t); the LED light-emitting chip of the LED lighting lamp is driven by the synthesized modulation signal to emit light, so that the lighting function is realized, and meanwhile, interference information is covertly broadcast by high-speed bright and dark flashing signals which cannot be detected by human eyes; the LED light-emitting chip of the LED lighting lamp is a light-emitting device supporting high-speed modulation, and is controlled by a synthesized modulation signal LED (t) to emit a lighting light signal with quick bright and dark flicker along with time t, and the quick bright and dark change of the lighting light signal is not perceived by naked eyes of people, so that the interference information carried by the lighting lamp is concealed and broadcast during lighting.
In the technical scheme of the embodiment, the interference information is sent to clearly and covertly broadcast, so that the interference can be carried out on the face recognition system under the condition that people are not in front of the interference information, the interference strength is improved, and the bottleneck of the face recognition safety of the embodiment is further improved.
Referring to fig. 7, fig. 7 is a sixth embodiment of the face recognition method according to the present invention, based on any one of the first to fifth embodiments, after step S40, the method further includes:
in step S50, a first image gradient of the face image is determined.
And step S60, comparing the first image gradient with a pre-stored second image gradient to obtain a comparison result.
And step S70, determining a face recognition result according to the comparison result.
The image gradient can be regarded as a two-dimensional discrete function, and the image gradient is actually the derivative of the two-dimensional discrete function:
G(x,y) = dx(i,j) + dy(i,j)；dx(i,j) = I(i+1,j) - I(i,j)
dy(i,j) = I(i,j+1) - I(i,j)
image gradients can also be generally differentiated by median:
dx(i,j) = [I(i+1,j) - I(i-1,j)]/2
dy(i,j) = [I(i,j+1) - I(i,j-1)]/2
wherein G (x, y) in the above formula is the image gradient, I is the value (e.g. RGB value) of the image pixel, and (I, j) is the coordinate of the pixel.
Image edges are typically realized by performing gradient operations on the image. While the above is a simple gradient definition, there are more complex gradient equations.
In this embodiment, the first image gradient refers to an image gradient of a face image corresponding to the acquired face information, and the second image gradient is an image gradient of a pre-stored sample face image carrying interference information.
In the technical scheme of the embodiment, the image gradient is processed, so that whether the face information processed by the face recognition system is extracted from the face image with the interference information can be quickly determined, the situation that the interference information is broken by confirming that the face image recognized by the face recognition system does not carry the interference information is avoided, and the accuracy of the face recognition of the embodiment is improved.
Referring to fig. 8, fig. 8 is a seventh embodiment of the face recognition method of the present invention, and based on the first to sixth embodiments, the step S70 includes:
and step S71, when the similarity between the first image gradient and the second image gradient is greater than or equal to a preset value, determining that the face recognition is successful.
And step S72, when the similarity between the first image gradient and the second image gradient is lower than the preset value, determining that the face recognition fails.
In the technical scheme of the embodiment, whether the face information processed by the face recognition system is extracted from the face image with the interference information can be quickly determined through the gradient value information degree with the sample face image, the situation that the interference information is broken when the face image recognized by the face recognition system is not carried with the interference information is avoided, and the accuracy of the face recognition of the embodiment is improved.
Referring to fig. 9, fig. 9 is a diagram of an eighth embodiment of the face recognition method according to the present invention, and based on the first to seventh embodiments, after step S40, the method further includes:
and step S80, outputting the face recognition result.
In the technical solution of this embodiment, the face recognition result includes but is not limited to prompt information of interference success, interference failure and safety factor generated to prompt the administrator of the face recognition system, so that the administrator can know the security of the face recognition system.
In order to achieve the above object, the present invention provides a face recognition apparatus, comprising: an acquisition module, a lighting module, a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the face recognition method as described above when executing the computer program.
To achieve the above object, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the face recognition method as described above.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A face recognition method is characterized by comprising the following steps:
acquiring interference information;
modulating the interference information into a light intensity control signal, and sending the light intensity control signal to a lighting device so that the lighting device adjusts the light intensity according to the light intensity control signal;
acquiring a face image detected by a camera, wherein the face image comprises light intensity information of the lighting device;
and carrying out face recognition according to the face image.
2. The face recognition method of claim 1, wherein the step of obtaining the interference information comprises:
determining an interference type;
and acquiring the interference information according to the interference type.
3. The face recognition method of claim 2, wherein the step of obtaining the interference information according to the interference type comprises:
and when the interference type is a thick light and dark stripe, generating long-run interference information.
4. The face recognition method of claim 2, wherein the step of obtaining the interference information according to the interference type comprises:
and when the interference type is the thin bright and dark stripe, generating short-run interference information.
5. The face recognition method of any one of claims 1 to 4, wherein the step of modulating the interference information into a light intensity control signal comprises:
determining a curve change relation between light intensity and time points in the interference information according to the interference information;
and generating the light intensity control signals of high and low levels according to the curve change relationship.
6. The face recognition method of claim 5, wherein after the step of performing face recognition based on the face image, further comprising:
determining a first image gradient of the face image;
comparing the first image gradient with a prestored second image gradient to obtain a comparison result;
and determining a face recognition result according to the comparison result.
7. The face recognition method of claim 6, wherein the step of determining the face recognition result according to the comparison result comprises:
when the similarity between the first image gradient and the second image gradient is greater than or equal to a preset value, determining that the face recognition is successful;
and when the similarity of the first image gradient and the second image gradient is lower than the preset value, determining that the face recognition fails.
8. The face recognition method according to claim 6 or 7, wherein after the step of determining the face recognition result according to the comparison result, further comprising:
and outputting the face recognition result.
9. A face recognition method device is characterized in that the face recognition method device comprises the following steps: an acquisition module, a lighting module, a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the face recognition method according to any one of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, carries out the steps of the face recognition method according to any one of claims 1 to 8.
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