Living-body detection information generation method and apparatus, and device, medium and program product

Abstract

Disclosed in the embodiments of the present disclosure are a living-body detection information generation method and apparatus, and a device, a medium and a program product. One specific implementation of the method comprises: in response to a living-body detection request being detected, determining, on the basis of preset light ray mode information, a light ray mode corresponding to a target detection object; on the basis of the light ray mode, controlling an associated light source device to project a light ray to the target detection object, wherein the light ray mode comprises at least one of the following: light ray projection region information, light ray projection time information, light ray projection intensity information and real-time light ray change information; acquiring image collection data corresponding to the target detection object; and on the basis of the image collection data, generating living-body detection information of the target detection object.

Description

Method, device, equipment, medium and program product for generating liveness detection information

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent application claims priority to the Chinese patent application filed on August 15, 2023, with application number 202311030210.0 and invention name “Liveness detection information generation method, device, equipment, medium and program product”. The full text of the application is incorporated into this application by reference. Technical Field

[0003] Embodiments of the present disclosure relate to the field of computer technology, and more particularly to methods, devices, equipment, media, and program products for generating liveness detection information. Background Art

[0004] Liveness detection can be performed on the subject to determine whether the subject is alive. Currently, the following methods are commonly used for liveness detection: using motion liveness detection, which requires the subject to perform a random action sequence; using silent liveness detection, which directly detects the subject; and using color liveness detection, which only detects the light intensity and color of the subject's image.

[0005] However, the inventors have found that when using the above-mentioned method to perform liveness detection on the detection object, the following technical problems often exist: only the color and light intensity of the detection object image are analyzed, and no consideration is given to improving the randomness of the projected light, resulting in poor defense capabilities of the liveness detection method; only detection is performed through the movement of the detection object or the detection object is directly detected, which makes it difficult to resist attacks from video or image sequences and the steps of collecting detection object data are relatively cumbersome, resulting in low accuracy of the liveness detection method and long detection time.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art.

[0007] Summary of the Invention

[0008] The content of this disclosure is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this disclosure is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0009] Some embodiments of the present disclosure provide methods, devices, electronic devices, computer-readable media, and computer program products for generating liveness detection information to solve one or more of the technical problems mentioned in the above background technology section.

[0010] In one or more embodiments, a method for generating liveness detection information is provided, the method comprising: in response to detecting a liveness detection request, determining a light pattern corresponding to a target detection object based on preset light pattern information; controlling an associated light source device to project light onto the target detection object based on the light pattern; acquiring image acquisition data corresponding to the target detection object; and generating liveness detection information for the target detection object based on the image acquisition data.

[0011] In one or more embodiments, a device for generating liveness detection information is provided, comprising: a determination unit configured to determine, in response to detecting a liveness detection request, a light pattern corresponding to a target detection object according to preset light pattern information; a control unit configured to control an associated light source device to project light onto the target detection object according to the light pattern; an acquisition unit configured to acquire image acquisition data corresponding to the target detection object; and a generation unit configured to generate liveness detection information of the target detection object based on the image acquisition data.

[0012] In one or more embodiments, an electronic device is provided, comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation manner of the first aspect above.

[0013] In one or more embodiments, a non-transitory computer-readable medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method described in any implementation of the first aspect.

[0014] In one or more embodiments, a computer program product is provided, including a computer program, which implements the method described in any implementation of the first aspect when executed by a processor.

[0015] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0017] FIG1 is a schematic diagram of an application scenario of the method for generating liveness detection information disclosed herein;

[0018] FIG2 is a flow chart of some embodiments of a method for generating liveness detection information according to the present disclosure;

[0019] FIG3 is a flowchart of another embodiment of a method for generating liveness detection information according to the present disclosure;

[0020] FIG4 is a schematic structural diagram of some embodiments of a living body detection information generating device according to the present disclosure;

[0021] FIG5 is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0022] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0023] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

[0024] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0025] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0026] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0027] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0028] FIG1 is a schematic diagram of an application scenario of a method for generating liveness detection information according to some embodiments of the present disclosure.

[0029] In the application scenario of Figure 1 , computing device 101 may first detect a liveness detection request 102. Then, in response to detecting the liveness detection request, computing device 101 may determine a light pattern 104 based on preset light pattern information 103. Computing device 101 may then control an associated light source device 105 to project light based on light pattern 104. Next, computing device 101 may acquire image data 106 corresponding to the target detection object. Finally, computing device 101 may generate liveness detection information 107 based on image data 106.

[0030] It should be noted that the computing device 101 can be either hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, or as a single software or software module. No specific limitations are given here.

[0031] It should be understood that the number of computing devices in FIG1 is merely illustrative and any number of computing devices may be provided according to implementation requirements.

[0032] 2, a process 200 of some embodiments of the method for generating liveness detection information according to the present disclosure is shown. The method for generating liveness detection information includes the following steps:

[0033] Step 201 : In response to detecting a liveness detection request, a light pattern corresponding to a target detection object is determined according to preset light pattern information.

[0034] In some embodiments, the execution entity of the liveness detection information generation method (e.g., the computing device shown in FIG. 1 ) may, in response to detecting a liveness detection request, determine a light pattern corresponding to a target detection object based on preset light pattern information. The liveness detection request may represent a user-initiated request to determine whether the target detection object is live. The preset light pattern information may represent pre-set light pattern characteristics. For example, the preset light pattern information may represent "light color change characteristics or light area shape change characteristics." The target detection object may represent any object to be detected. For example, the target detection object may be a human face. The light pattern may represent light characteristics. The light pattern may be a light pattern randomly determined based on the preset light pattern information and corresponding to the preset light pattern information. In practice, the execution entity may first obtain the preset light pattern information. Then, the light pattern corresponding to the preset light pattern information may be randomly determined. It should be noted that randomly determining the light pattern corresponding to the preset light pattern information based on the preset light pattern information reflects the randomness of the light pattern, thereby improving defense against prosthetic attacks. Thus, a randomly determined light pattern may be obtained.

[0035] Step 202: Control the associated light source device to project light onto the target detection object according to the light pattern.

[0036] In some embodiments, the execution entity can control an associated light source device to project light toward a target detection object based on a light pattern. The light source device can be a camera device. The light pattern can include at least one of the following: light projection area information, light projection duration information, light projection intensity information, and real-time light change information. The light projection area information can represent, but is not limited to, one of the following: the number of light projection areas, the color of the light projection areas, the shape of the light projection areas, the recognition areas of the light projection, and the non-recognition areas of the light projection. The recognition areas of the light projection can represent areas where the detection object is recognized. The non-recognition areas of the light projection can represent areas where the detection object is not recognized. The light projection duration information can represent the duration of the projection light. The light projection intensity information can represent the intensity of the projected light. The real-time light change information can represent characteristics of the light that change over time. For example, the shape of the light projection area can be circular or square. For example, the real-time light change information can indicate that the shape of the light area projected at one moment is circular, and the shape of the light area projected at a second moment is square. In practice, the execution entity can control the light source device to project light according to the corresponding light pattern. For example, the light source device can be a mobile phone camera.

[0037] As an example, the light source device may be a device that decodes and verifies the light pattern. It should be noted that projecting light according to the randomly generated light pattern improves the defense capability against prosthetic attacks.

[0038] Step 203: Acquire image acquisition data corresponding to the target detection object.

[0039] In some embodiments, the execution entity may obtain image acquisition data corresponding to the target detection object. The image acquisition data may be a video or at least one image of the target detection object. The image acquisition data may be image data obtained by the execution entity from a database. In practice, the execution entity may project light onto the target detection object and then obtain individual images or videos corresponding to the target detection object from individual images or videos captured by a camera as the image acquisition data corresponding to the target detection object.

[0040] Step 204: Generate liveness detection information of the target detection object based on the image acquisition data.

[0041] In some embodiments, the execution entity may generate liveness detection information of the target detection object based on the image acquisition data. The liveness detection information may represent the detection result of whether the target detection object is alive. In practice, first, the execution entity may determine whether there is reflected light in each image area of ​​the single image in response to determining that the image acquisition data is a single image. The execution entity may input the single image into a reflected light detection model to obtain whether there is reflected light in each image area in the single image. The reflected light detection model may be a model for detecting whether there is reflected light in each image area in the image. The reflected light detection model may be a convolutional neural network model.

[0042] Then, in response to determining that reflected light exists in each image region of the single image, a determination can be made as to whether the region color corresponding to each image region of the single image is consistent with the light color of the light region corresponding to the image region included in the light representation information. The image region in each image region corresponds to the light region in each light region. The light representation information may include each light region and each light color corresponding to each light region.

[0043] Secondly, in response to determining that the area color corresponding to each image area of ​​the above-mentioned single image is consistent with the light color of the light area corresponding to the above-mentioned image area included in the above-mentioned light display information, the liveness detection information corresponding to the above-mentioned target detection object can be determined as liveness information.

[0044] Afterwards, in response to determining that the area color corresponding to each image area of ​​the above-mentioned single image is inconsistent with the light color of the light area corresponding to the above-mentioned image area included in the above-mentioned light display information, it can be determined that the liveness detection information corresponding to the above-mentioned target detection object is prosthesis information.

[0045] Next, the execution entity may determine the target images corresponding to the video in response to determining that the image acquisition data is a video or an image sequence. In practice, first, the execution entity may obtain a video image sequence corresponding to the video. The video image sequence may represent the images in the video. Then, the images that meet the preset selection conditions may be selected from the video image sequence as the target images. The preset selection condition may be that the sequence number of the image is less than the preset sequence number. There is no specific limitation on the specific value of the preset sequence number. For example, the preset sequence number may be the fifth frame.

[0046] Next, for each image in the above image sequence or each target image in each target image, the following steps may be performed:

[0047] The first sub-step is to determine the time information corresponding to the image or the target image, wherein the time information may represent the time when the image or the target image was acquired.

[0048] The second sub-step is to determine the regional color of each image area in each image area of ​​the above-mentioned image or target image based on the above-mentioned time information. In practice, the above-mentioned execution entity can first determine the shooting time of the target image. Then, the regional color corresponding to each image area in each image area of ​​the above-mentioned target image corresponding to the above-mentioned shooting time can be determined. It should be noted that when determining the regional color of each image area in each image area of ​​the above-mentioned image or target image, the color of the projected light corresponding to each shooting time can be different, so the regional color of the image at each shooting time can be different.

[0049] The third sub-step is to determine whether the region color of each of the above-mentioned image regions is consistent with the light color of the corresponding image region.

[0050] In a fourth sub-step, in response to determining that the area color of each of the image areas is consistent with the light color of the corresponding image area, the living body information is determined as target detection information.

[0051] A fifth sub-step is determining the prosthesis information as target detection information in response to determining that the area color of each of the above-mentioned image areas is inconsistent with the light color of the corresponding image area.

[0052] Then, in response to determining whether each piece of target detection information corresponding to the video or image sequence is living body information, the living body detection information corresponding to the target detection object is determined to be living body information.

[0053] Finally, in response to determining that there is target detection information that is prosthetic information in each target detection information corresponding to the above video or image sequence, it is determined that the liveness detection information corresponding to the above target detection object is prosthetic information.

[0054] As an example, the prosthesis information may indicate that the target detection object is a prosthesis. The living body information may indicate that the target detection object is living. The time information may indicate the target image or the time at which the image was captured. The target detection information may indicate the detection result of the image or the target image.

[0055] The above-described embodiments of the present disclosure have the following beneficial effects: The liveness detection information generation methods of some embodiments of the present disclosure enhance the defense capabilities of liveness detection, improve the accuracy of liveness detection, and shorten the detection time. Specifically, the reasons for the poor defense capabilities, low accuracy, and long detection time of the liveness detection methods are: when performing liveness detection on the detection object, only the color and light intensity of the detection object image are analyzed, without considering improving the randomness of the projected light, resulting in poor defense capabilities of the liveness detection method; and only detecting the detection object based on the detection object's movements or directly detecting the detection object makes it more difficult to defend against attacks from video or image sequences and the steps for collecting detection object data are more cumbersome, resulting in low accuracy and long detection time of the liveness detection method. Based on this, the liveness detection information generation methods of some embodiments of the present disclosure first determine a light pattern corresponding to the target detection object based on preset light pattern information in response to detecting a liveness detection request. Thus, a light pattern corresponding to the preset light pattern information can be obtained. Then, based on the light pattern, the associated light source device is controlled to project light onto the target detection object. In this way, light corresponding to the light pattern can be projected onto the target detection object. Thereafter, image acquisition data corresponding to the target detection object is obtained. Thus, data information representing the image of the target detection object can be obtained. Finally, based on the image acquisition data, liveness detection information of the target detection object is generated. Thus, information representing the detection result of the target detection object can be obtained. Also, because the light pattern is randomly generated based on the preset light pattern information, the light pattern has randomness, which improves the defense capability of the liveness detection method. Also, because the liveness detection information is generated based on the image acquisition data and the light pattern, rather than solely based on the movement of the detection object, the accuracy of liveness detection is improved. Moreover, there is no need to collect movement information of the target detection object, thereby simplifying the steps of collecting detection object data and shortening the time required for liveness detection. Thus, the accuracy of the liveness detection method can be improved, the defense capability of the liveness detection method can be improved, and the time required for liveness detection can be shortened.

[0056] Further referring to FIG3 , which shows a process 300 of another embodiment of a method for generating liveness detection information. The process 300 of the method for generating liveness detection information includes the following steps:

[0057] Step 301 : In response to detecting a liveness detection request, a light pattern corresponding to a target detection object is determined according to preset light pattern information.

[0058] In some embodiments, the specific implementation of step 301 and the technical effects brought about can refer to step 201 in the embodiments corresponding to FIG2 , and will not be repeated here.

[0059] Step 302: Determine light display information according to the light pattern.

[0060] In some embodiments, the execution entity may determine the light display information based on the light pattern. The light display information may represent a specific form of the light display. For example, the light display information may represent, but is not limited to, one of the following: light color, light area, or light area shape. In practice, the execution entity may first verify and decode the light pattern. Then, the verified and decoded light pattern is determined as the light display information. In practice, the execution entity may verify and decode the light pattern using a decryption algorithm. For example, the decryption algorithm may be the Advanced Encryption Standard algorithm.

[0061] Step 303: Control the associated light source device to project light corresponding to the light display information onto the target detection object.

[0062] In some embodiments, the execution subject may control an associated light source device to project light corresponding to the light display information onto the target detection object. The light source device may be any camera device. Thus, light corresponding to the light display information may be projected.

[0063] Step 304: Acquire image acquisition data corresponding to the target detection object.

[0064] In some embodiments, the specific implementation of step 304 and the technical effects brought about can refer to step 203 in the embodiments corresponding to FIG2 , and will not be repeated here.

[0065] Step 305: Generate liveness detection information of the target detection object based on the image acquisition data.

[0066] In some optional implementations of some embodiments, the execution entity may generate liveness detection information of the target detection object based on the image acquisition data through the following steps:

[0067] The first step is to determine a target image sequence corresponding to the image acquisition data based on the image acquisition data. The target image sequence may represent a sequence of target images having a temporal relationship. The initial value of the target image sequence may be empty. Each target image in the target image sequence may represent a respective image of the corresponding image acquisition data.

[0068] The second step is to generate liveness detection information of the target detection object according to the target image sequence.

[0069] In some optional implementations of some embodiments, the execution entity may determine a target image sequence corresponding to the image acquisition data according to the image acquisition data through the following steps:

[0070] In the first step, in response to determining that the image acquisition data is a single image, the single image is added to the target image sequence, wherein the single image may represent an image containing the target detection object.

[0071] In the second step, in response to determining that the image acquisition data is an image sequence, the image sequence is determined as a target image sequence. The image sequence can represent each image of the target detection object.

[0072] In the third step, in response to determining that the above-mentioned image acquisition data is a video, each image in the above-mentioned video that meets the preset image selection condition is determined as a target image sequence. The above-mentioned each image can be each image included in the video. The above-mentioned preset image selection condition can be that the frame sequence of the image in the video is less than the preset image frame sequence. For example, the preset image frame sequence can be the fifth frame. There is no specific limitation on the specific value of the above-mentioned preset image frame sequence. In practice, first, the above-mentioned execution entity can determine each image whose frame sequence is less than the preset image frame sequence from the above-mentioned video. Then, each image whose frame sequence is less than the preset image frame sequence in the above-mentioned video is determined as the target image sequence.

[0073] In some optional implementations of some embodiments, the execution entity may generate the liveness detection information of the target detection object according to the target image sequence through the following steps:

[0074] In the first step, for each target image in the above target image sequence, the following detection steps are performed:

[0075] The first sub-step is to determine whether reflected light exists in the target image. In practice, the execution entity may input the target image into an image reflected light detection model to determine whether reflected light exists in the target image. The image reflected light detection model may be a model that predicts whether reflected light exists in an image. For example, the image reflected light detection model may be a convolutional neural network model.

[0076] The second sub-step is, in response to determining that reflected light exists in the target image, determining whether reflected light exists in each image region of the target image. In practice, the execution entity may input the target image into a regional reflected light detection model to determine whether reflected light exists in each image region of the target image. The regional reflected light detection model may be a model that predicts whether reflected light exists in each region of an image. For example, the regional reflected light detection model may be a convolutional neural network model.

[0077] In the second step, in response to determining that reflected light exists in each image area of ​​each target image in the target image sequence, liveness detection information of the target detection object is generated according to the target image sequence and the light display information.

[0078] In some optional implementations of some embodiments, the execution entity may, in response to determining that the target image sequence includes a target image without reflected light, or includes a target image with at least one image region without reflected light, determine the preset prosthesis information as the liveness detection information of the target detection object. The preset prosthesis information may indicate that the target detection object is a prosthesis.

[0079] In some optional implementations of some embodiments, the execution entity may generate liveness detection information of the target detection object according to the target image sequence and the light display information through the following steps:

[0080] In the first step, in response to determining that the image acquisition data is a single image, the following test steps are performed:

[0081] The first sub-step is to determine whether the single image satisfies a first preset prosthetic condition based on the region colors of each image region corresponding to the single image and the respective light colors corresponding to each light region. The light display information may include each light region and the respective light colors and shapes corresponding to each light region. The image regions in each image region of each target image included in the target image sequence correspond to the light regions in each light region. The first preset prosthetic condition may be that, among the image regions corresponding to the single image, there are image regions that meet a preset color difference condition. The preset color difference condition may be that the region color corresponding to the image region and the corresponding light color are different.

[0082] A second sub-step is to determine whether the single image satisfies a second preset prosthesis condition based on the image area shapes of each image area corresponding to the single image and the shapes of each light area corresponding to each light area. The second preset prosthesis condition may be that the image area corresponding to the single image contains an image area that satisfies a preset shape difference condition. The preset shape difference condition may be that the area shape corresponding to the image area and the shape of the corresponding light area are different.

[0083] In the second step, in response to determining that the single image satisfies the first preset prosthesis condition or the second preset prosthesis condition, the preset prosthesis information is determined as the liveness detection information of the target detection object. Thus, it can be determined that the target detection object is a prosthesis.

[0084] Optionally, in the above-mentioned test step, the above-mentioned execution subject may also determine the image feature information corresponding to the above-mentioned single image in response to determining that the above-mentioned single image does not satisfy the above-mentioned first preset prosthesis condition and the above-mentioned second preset prosthesis condition. The above-mentioned image feature information may characterize the image area feature corresponding to the above-mentioned single image. For example, the above-mentioned image feature information may characterize, but is not limited to, one of the following: texture feature information of the image, particle feature information of the image. The above-mentioned image texture feature information may characterize the texture feature of the image. The above-mentioned particle feature information of the image may characterize the granularity of the image. In practice, in response to determining that the above-mentioned single image does not satisfy the above-mentioned first preset prosthesis condition and the above-mentioned second preset prosthesis condition, the above-mentioned execution subject may input the above-mentioned single image into an image extraction model to obtain the image feature information corresponding to the above-mentioned single image. The above-mentioned image extraction model may be a model for extracting image features. For example, the image extraction model may be a convolutional neural network model.

[0085] In some optional implementations of some embodiments, the execution entity may generate liveness detection information of the target detection object according to the target image sequence and the light display information through the following steps:

[0086] In the first step, in response to determining that the image acquisition data is an image sequence or a video, for each target image in the target image sequence, the following verification steps are performed:

[0087] In the first inspection step, for each image region in the target image, the following steps are performed:

[0088] The first sub-step is to determine the acquisition time corresponding to the target image. The acquisition time may represent the acquisition time of the target image. The execution subject may obtain the acquisition time corresponding to the target image.

[0089] The second sub-step is to determine, based on the acquisition time, the light color of the light area corresponding to the acquisition time and the image area from the light display information. In practice, the execution entity may first determine the light area of ​​the image area. Then, the execution entity may determine the light color corresponding to the light area and the acquisition time. It should be noted that the determination of the light color of the light area corresponding to the acquisition time and the image area refers to the color of the light projected in the physical space. The projected light color corresponding to each acquisition time may be different.

[0090] The third sub-step is to determine whether the area color of the image area and the corresponding light color meet the preset color difference condition. In this way, it can be determined whether the area color is consistent with the light color.

[0091] In a second verification step, in response to determining that the regional color of each image region of the target image and the corresponding light color do not satisfy the preset color difference condition, image feature information of the single image is determined. In practice, the execution entity may input the single image into an image extraction model to obtain image feature information corresponding to the single image. The image extraction model may be a model for extracting image features. For example, the image extraction model may be a convolutional neural network model.

[0092] In the second step, in response to determining that the target image sequence satisfies the third preset prosthetic condition, the preset prosthetic information is determined as liveness detection information corresponding to the target detection object. The third preset prosthetic condition can be a target image in which the color of an image region in the target image sequence and the corresponding light color meet the preset color difference condition. It should be noted that the color of each light region varies over time, reflecting the randomness of light in the temporal dimension. This enhances the defensive capabilities of the liveness detection method.

[0093] In some optional implementations of some embodiments, in response to determining that reflected light exists in each image region of each image in the target image sequence, the execution entity may generate liveness detection information of the target detection object based on the target image sequence and the light display information through the following steps:

[0094] The first step is to determine that the image acquisition data is a single image and that the image feature information of the single image does not meet the attack trace characteristic condition, and to determine the preset liveness information as the liveness detection information of the target detection object. The preset liveness information can represent that the target detection object is alive. The attack trace characteristic condition can be that the image feature information is the same as the image feature information of the prosthetic image. The image feature information can represent the feature information of the target detection object image. In practice, the execution entity can extract the image feature information of the preset prosthetic image, and then determine whether the extracted image feature information of the preset prosthetic image is the same as the image feature information of the single image. In this way, the detection information of the target detection object can be obtained. It should be noted that the method of extracting the image feature information of the preset prosthetic image is the same as the method of extracting the image feature information of the single image, so it will not be repeated here.

[0095] In the second step, in response to determining that the image acquisition data is a single image and that the image feature information of the single image meets the attack trace feature condition, the preset prosthesis information is determined as the liveness detection information of the target detection object. In this way, the detection information of the target detection object can be obtained.

[0096] In some optional implementations of some embodiments, the execution entity may generate liveness detection information of the target detection object according to the target image sequence and the light display information through the following steps:

[0097] In the first step, in response to determining that the above-mentioned image acquisition data is an image sequence or a video, and the image feature information of each image corresponding to the above-mentioned image sequence or the above-mentioned video does not meet the attack trace feature conditions, the preset living body information is determined as the living body detection information of the above-mentioned target detection object.

[0098] In the second step, in response to determining that the above-mentioned image acquisition data is an image sequence or a video, and that there is a target image whose image feature information meets the above-mentioned attack trace feature conditions in the target image sequence corresponding to the above-mentioned image sequence or the above-mentioned video, the preset prosthesis information is determined as the liveness detection information of the above-mentioned target detection object.

[0099] In some optional implementations of some embodiments, the execution entity may generate liveness detection information of the target detection object according to the target image sequence and the light display information through the following steps:

[0100] The first step is to determine whether the environmental data of the target detection object meets the environmental data condition in response to determining that the above-mentioned image acquisition data is a single image and the image feature information of the above-mentioned single image does not meet the attack trace feature condition. The above-mentioned image acquisition data may include environmental data. The above-mentioned environmental data may characterize the background data characteristics in the acquired image. For example, the above-mentioned environmental data may characterize the texture feature information and particle feature information of the background image. The above-mentioned texture feature information may characterize the texture features of the image. The above-mentioned particle feature information may characterize the granularity of the image. The above-mentioned environmental data condition may characterize that the above-mentioned background data characteristics are the same as the background data characteristics of the real scene. In practice, the above-mentioned execution entity may extract the background data characteristics of the preset real scene image, and then determine whether the environmental data of the target detection object is the same as the background data characteristics of the preset real scene image. The method of extracting features for the preset real scene image is the same as the method of extracting features for the image feature information of the above-mentioned single image, and therefore, it will not be repeated here. It should be noted that after determining that the image acquisition data does not meet the attack trace characteristic conditions, further judgment can be made by detecting whether the background data of the image meets the environmental data conditions. Then, judging the background data is only an auxiliary to liveness detection. The liveness detection result can also be directly obtained by simply determining that the image acquisition data does not meet the attack trace characteristic conditions.

[0101] The second step is to determine whether the above-mentioned environmental data of the above-mentioned target detection object meets the above-mentioned environmental data conditions in response to determining that the above-mentioned image acquisition data is an image sequence or a video, and the image feature information corresponding to each image of the above-mentioned image sequence or the above-mentioned video does not meet the above-mentioned trace feature conditions of the attack.

[0102] In some optional implementations of some embodiments, the execution entity may generate liveness detection information of the target detection object according to the target image sequence and the light display information through the following steps:

[0103] In the first step, in response to determining that the environmental data of the target detection object meets the environmental data condition, the preset living body information is determined as the living body detection information of the target detection object.

[0104] In the second step, in response to determining that the environmental data of the target detection object does not meet the environmental data condition, the preset prosthesis information is determined as the liveness detection information of the target detection object.

[0105] As can be seen from Figure 3, compared with the description of some embodiments corresponding to Figure 2, the process 300 of the liveness detection information generation method in some embodiments corresponding to Figure 3 reflects the light display information determined according to the generated light pattern, and projects light to the detection object according to the light display information. The light display information can represent that the color and shape of each area of ​​the screen are different, and each area projects different light to the detection object at the same time, reflecting the randomness of the projected light in the spatial dimension. Therefore, the detection object can be detected by a randomly generated light pattern instead of using a fixed light pattern, so the accuracy of identifying video or picture sequence prosthesis attacks is improved, thereby improving the ability to resist video or picture sequence attacks.

[0106] Further referring to FIG4 , as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a liveness detection information generating device. These device embodiments correspond to the method embodiments shown in FIG2 , and the device can be specifically applied to various electronic devices.

[0107] As shown in FIG4 , a liveness detection information generating apparatus 400 in some embodiments includes: a determination unit 401, a control unit 402, an acquisition unit 403, and a generation unit 404. The determination unit 401 is configured to, in response to detecting a liveness detection request, determine a light pattern corresponding to a target detection object based on preset light pattern information; the control unit 402 is configured to control an associated light source device to project light onto the target detection object based on the light pattern, wherein the light pattern includes at least one of the following: light projection area information, light projection time information, light projection intensity information, and real-time light change information; the acquisition unit 403 is configured to acquire image acquisition data corresponding to the target detection object; and the generation unit 404 is configured to generate liveness detection information for the target detection object based on the image acquisition data.

[0108] Optionally, the control unit 402 may be further configured to: determine light display information according to the light pattern; and control an associated light source device to project light corresponding to the light display information onto the target detection object.

[0109] Optionally, the generating unit 404 may be further configured to: determine a target image sequence corresponding to the image acquisition data according to the image acquisition data; and generate liveness detection information of the target detection object according to the target image sequence.

[0110] Optionally, the generation unit 404 can be further configured to: in response to the above-mentioned image acquisition data being a single image, add the above-mentioned single image to the target image sequence; in response to determining that the above-mentioned image acquisition data is an image sequence, determine the above-mentioned image sequence as a target image sequence; in response to determining that the above-mentioned image acquisition data is a video, determine each image in the above-mentioned video that meets the preset image selection conditions as a target image sequence.

[0111] Optionally, the generation unit 404 can be further configured to: for each target image in the above-mentioned target image sequence, perform the following detection steps: determine whether there is reflected light in the above-mentioned target image; in response to determining that there is reflected light in the above-mentioned target image, determine whether there is reflected light in each image area in the above-mentioned target image; in response to determining that there is reflected light in each image area of ​​each target image in the above-mentioned target image sequence, generate the liveness detection information of the above-mentioned target detection object according to the above-mentioned target image sequence and the above-mentioned light display information.

[0112] Optionally, the generation unit 404 can be further configured to: in response to determining that the corresponding target image sequence includes a target image without reflected light, or includes a target image with at least one image area without reflected light, determine the preset prosthesis information as the liveness detection information of the target detection object.

[0113] Optionally, the generation unit 404 can be further configured as follows: the above-mentioned light display information includes each light area and each light color and each light area shape corresponding to the above-mentioned each light area, and the image area in each image area of ​​each target image included in the above-mentioned target image sequence corresponds to the light area in the above-mentioned each light area.

[0114] Optionally, the generation unit 404 can be further configured to: in response to determining that reflected light exists in each image area of ​​each image in the above-mentioned target image sequence, generate the liveness detection information of the above-mentioned target detection object according to the above-mentioned target image sequence and the above-mentioned light display information, including: in response to determining that the above-mentioned image acquisition data is a single image, perform the following test steps: determine whether the above-mentioned single image meets the first preset prosthesis condition according to the area color of each image area corresponding to the above-mentioned single image and the each light color corresponding to the above-mentioned each light area; determine whether the above-mentioned single image meets the second preset prosthesis condition according to the image area shape of each image area corresponding to the above-mentioned single image and the each light area shape corresponding to the above-mentioned each light area; in response to determining that the above-mentioned single image meets the first preset prosthesis condition or the above-mentioned second preset prosthesis condition, determine the preset prosthesis information as the liveness detection information of the above-mentioned target detection object.

[0115] Optionally, the generating unit 404 may be further configured to: in response to determining that the single image does not satisfy the first preset prosthesis condition and the second preset prosthesis condition, determine image feature information corresponding to the single image.

[0116] Optionally, the generation unit 404 can be further configured to: in response to determining that the above-mentioned image acquisition data is an image sequence or a video, for each target image in the above-mentioned target image sequence, perform the following inspection steps: for each image area in the above-mentioned target image, perform the following steps: determine the acquisition time corresponding to the above-mentioned target image; based on the above-mentioned acquisition time, determine the light color of the light area corresponding to the above-mentioned acquisition time and the above-mentioned image area from the above-mentioned light display information; determine whether the area color of the above-mentioned image area and the corresponding light color meet the above-mentioned preset color difference condition; in response to determining that the area color of each image area of ​​the above-mentioned target image and the corresponding light color do not meet the above-mentioned preset color difference condition, determine the image feature information of the above-mentioned single image; in response to determining that the above-mentioned target image sequence meets the third preset prosthesis condition, determine the above-mentioned preset prosthesis information as the liveness detection information corresponding to the above-mentioned target detection object.

[0117] Optionally, the generating unit 404 may be further configured to: in response to determining that the image acquisition data is a single image and the image feature information of the single image does not meet the attack trace feature condition, determine the preset liveness information as the liveness detection information of the target detection object;

[0118] In response to determining that the image acquisition data is a single image and that the image feature information of the single image meets the attack trace feature condition, the preset prosthesis information is determined as the liveness detection information of the target detection object.

[0119] Optionally, the generation unit 404 can be further configured to: in response to determining that the above-mentioned image acquisition data is an image sequence or video, and the image feature information of each image corresponding to the above-mentioned image sequence or the above-mentioned video does not meet the attack trace characteristic conditions, determine the preset liveness information as the liveness detection information of the above-mentioned target detection object; in response to determining that the above-mentioned image acquisition data is an image sequence or video, and there is a target image whose image feature information meets the above-mentioned attack trace characteristic conditions in the target image sequence corresponding to the above-mentioned image sequence or the above-mentioned video, determine the preset prosthesis information as the liveness detection information of the above-mentioned target detection object.

[0120] Optionally, the generating unit 404 may be further configured to: in response to determining that the image acquisition data is a single image and the image feature information of the single image does not meet the attack trace feature condition, determine whether the environmental data of the target detection object meets the environmental data condition;

[0121] In response to determining that the above-mentioned image acquisition data is an image sequence or a video, and the image feature information corresponding to each image of the above-mentioned image sequence or the above-mentioned video does not meet the above-mentioned trace feature conditions of the attack, it is determined whether the above-mentioned environmental data of the above-mentioned target detection object meets the above-mentioned environmental data conditions.

[0122] Optionally, the generation unit 404 can be further configured to: in response to determining that the above-mentioned environmental data of the above-mentioned target detection object meets the above-mentioned environmental data conditions, determine the above-mentioned preset liveness information as the liveness detection information of the above-mentioned target detection object; in response to determining that the above-mentioned environmental data of the above-mentioned target detection object does not meet the above-mentioned environmental data conditions, determine the above-mentioned preset prosthesis information as the liveness detection information of the above-mentioned target detection object.

[0123] It is understood that the units described in the apparatus 400 correspond to the steps in the method described with reference to Figure 2. Therefore, the operations, features, and beneficial effects described above for the method are also applicable to the apparatus 400 and the units contained therein, and will not be repeated here.

[0124] Referring now to FIG5 , a schematic diagram illustrating the structure of an electronic device 500 (e.g., the computing device in FIG1 ) suitable for implementing some embodiments of the present disclosure is shown. The electronic device shown in FIG5 is merely an example and should not limit the functionality and scope of use of the embodiments of the present disclosure.

[0125] As shown in Figure 5, the electronic device 500 may include a processing device 501 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. Various programs and data required for the operation of the electronic device 500 are also stored in the RAM 503. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0126] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although FIG. 5 shows an electronic device 500 with various devices, it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or provided alternatively. Each block shown in FIG. 5 may represent one device, or may represent multiple devices as needed.

[0127] In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from a network via the communication device 509, or installed from the storage device 508, or installed from the ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of some embodiments of the present disclosure are performed.

[0128] It should be noted that the computer-readable medium described in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0129] In some embodiments, the client and server can communicate using any currently known or later developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or later developed network.

[0130] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: in response to detecting a liveness detection request, determines a light pattern corresponding to the target detection object based on preset light pattern information; controls the associated light source device to project light onto the target detection object based on the light pattern, wherein the light pattern includes at least one of the following: light projection area information, light projection time information, light projection intensity information, and real-time light change information; obtains image acquisition data corresponding to the target detection object; and generates liveness detection information for the target detection object based on the image acquisition data.

[0131] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0132] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0133] The units described in some embodiments of the present disclosure may be implemented in software or hardware. The units described may also be provided in a processor. For example, they may be described as follows: a processor includes an input unit and a generation unit. The names of these units do not, in some cases, limit the units themselves. For example, the generation unit may also be described as "a unit that generates the liveness detection information of the target detection object based on the above-mentioned image acquisition data."

[0134] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0135] Some embodiments of the present disclosure further provide a computer program product, including a computer program, which implements any of the above-mentioned methods for generating living body detection information when executed by a processor.

[0136] The above description is only an illustration of some preferred embodiments of the present disclosure and the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A method for generating liveness detection information, comprising: In response to detecting a liveness detection request, determining a light pattern corresponding to a target detection object according to preset light pattern information; According to the light pattern, controlling the associated light source device to project light to the target detection object, wherein the light pattern includes at least one of the following: light projection area information, light projection time information, light projection intensity information, and light real-time change information; Acquire image acquisition data corresponding to the target detection object; Generate liveness detection information of the target detection object according to the image acquisition data.

2. The method according to claim 1, wherein: The step of controlling the associated light source device to project light to the target detection object according to the light pattern includes: Determining light display information according to the light pattern; Control the associated light source device to project light corresponding to the light display information to the target detection object.

3. The method according to claim 2, wherein: The step of generating the liveness detection information of the target detection object according to the image acquisition data includes: Determining a target image sequence corresponding to the image acquisition data according to the image acquisition data; Generate liveness detection information of the target detection object according to the target image sequence.

4. The method according to claim 3, wherein: The step of determining a target image sequence corresponding to the image acquisition data according to the image acquisition data comprises: In response to determining that the image acquisition data is a single image, adding the single image to a target image sequence; In response to determining that the image acquisition data is an image sequence, determining the image sequence as a target image sequence; In response to determining that the image acquisition data is a video, each image in the video that meets a preset image selection condition is determined as a target image sequence.

5. The method according to claim 3, wherein: The step of generating the liveness detection information of the target detection object according to the target image sequence includes: For each target image in the target image sequence, the following detection steps are performed: Determining whether there is reflected light in the target image; In response to determining that reflected light exists in the target image, determining whether reflected light exists in each image area in the target image; In response to determining that reflected light exists in each image area of ​​each target image in the target image sequence, liveness detection information of the target detection object is generated according to the target image sequence and the light display information.

6. The method according to claim 5, wherein: The step of generating the liveness detection information of the target detection object according to the target image sequence includes: In response to determining that the target image sequence includes a target image without reflected light, or includes a target image with at least one image area without reflected light, the preset prosthesis information is determined as the liveness detection information of the target detection object.

7. The method according to claim 5, wherein: The light display information includes each light area and each light color and each light area shape corresponding to each light area, and the image area in each image area of ​​each target image included in the target image sequence corresponds to the light area in each light area; as well as The step of generating the liveness detection information of the target detection object according to the target image sequence and the light display information includes: In response to determining that the image acquisition data is a single image, performing the following test steps: Determining whether the single image satisfies a first preset prosthesis condition according to the region colors of each image region corresponding to the single image and the respective light colors corresponding to each light region; determining whether the single image satisfies a second preset prosthesis condition according to image area shapes corresponding to each image area of ​​the single image and each light area shape corresponding to each light area; In response to determining that the single image satisfies the first preset prosthesis condition or the second preset prosthesis condition, the preset prosthesis information is determined as the living body detection information of the target detection object.

8. The method according to claim 7, wherein: The testing step also includes: In response to determining that the single image does not satisfy the first preset prosthesis condition and the second preset prosthesis condition, image feature information corresponding to the single image is determined.

9. The method according to claim 7, wherein: The step of generating the liveness detection information of the target detection object according to the target image sequence and the light display information includes: In response to determining that the image acquisition data is an image sequence or a video, for each target image in the target image sequence, performing the following verification steps: For each image region in the target image, the following steps are performed: Determine the acquisition time corresponding to the target image; Determining, from the light display information, according to the acquisition time, a light color of a light area corresponding to the acquisition time and the image area; Determining whether the area color of the image area and the corresponding light color meet the preset color difference condition; In response to determining that the area color of each image area of ​​the target image and the corresponding light color do not satisfy the preset color difference condition, determining the image feature information of the single image; In response to determining that the target image sequence satisfies a third preset prosthesis condition, the preset prosthesis information is determined as living body detection information corresponding to the target detection object.

10. The method according to claim 7, wherein: The step of generating the liveness detection information of the target detection object according to the target image sequence and the light display information includes: In response to determining that the image acquisition data is a single image, and image feature information of the single image does not meet an attack trace feature condition, determining preset liveness information as liveness detection information of the target detection object; In response to determining that the image acquisition data is a single image, and that image feature information of the single image meets an attack trace feature condition, preset prosthesis information is determined as liveness detection information of the target detection object.

11. The method according to claim 9, wherein: The step of generating the liveness detection information of the target detection object according to the target image sequence and the light display information includes: In response to determining that the image acquisition data is an image sequence or a video, and image feature information corresponding to each image of the image sequence or the video does not meet an attack trace feature condition, determining preset liveness information as liveness detection information of the target detection object; In response to determining that the image acquisition data is an image sequence or a video, and that there is a target image whose image feature information satisfies the attack trace feature condition in the target image sequence corresponding to the image sequence or the video, the preset prosthesis information is determined as the liveness detection information of the target detection object.

12. The method according to any one of claims 8 to 9, wherein: The image acquisition data includes environmental data; and The step of generating the liveness detection information of the target detection object according to the target image sequence and the light display information includes: In response to determining that the image acquisition data is a single image, and the image feature information of the single image does not meet the attack trace feature condition, determining whether the environmental data of the target detection object meets the environmental data condition; In response to determining that the image acquisition data is an image sequence or a video, and the image feature information corresponding to each image of the image sequence or the video does not satisfy the attack trace feature condition, it is determined whether the environmental data of the target detection object satisfies the environmental data condition.

13. The method according to claim 12, wherein: The step of generating the liveness detection information of the target detection object according to the target image sequence and the light display information includes: In response to determining that the environmental data of the target detection object meets the environmental data condition, determining the preset living body information as the living body detection information of the target detection object; In response to determining that the environmental data of the target detection object does not satisfy the environmental data condition, the preset prosthesis information is determined as the living body detection information of the target detection object.

14. A living body detection information generating device, comprising: a determination unit configured to determine, in response to detecting a liveness detection request, a light pattern corresponding to a target detection object according to preset light pattern information; A control unit is configured to control the associated light source device to project light to the target detection object according to the light pattern, wherein the light pattern includes at least one of the following: light projection area information, light projection time information, light projection intensity information, and light real-time change information; An acquisition unit is configured to acquire image acquisition data corresponding to the target detection object; The generating unit is configured to generate the living body detection information of the target detection object according to the image acquisition data.

15. An electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 13.

16. A non-transitory computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 13 is implemented.

17. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method according to any one of claims 1 to 13.

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