Automatic identification method and system based on portrait analysis

Abstract

The invention relates to an automatic identification method and system based on portrait analysis, and relates to the technical field of automatic identification, and the method comprises the steps: collecting image detection information; performing illumination analysis based on the image detection information to obtain environment illumination information; determining whether the environment illumination information meets preset illumination demand information or not; if yes, recognition result information is generated and output based on the image detection information; if not, determining illumination brightness deviation information, illumination uniformity deviation information and illumination color temperature deviation information based on the environment illumination information and preset illumination demand information; and generating an illumination adjustment scheme based on the illumination brightness deviation information, the illumination uniformity deviation information and the illumination color temperature deviation information, and executing the illumination adjustment scheme to collect the image detection information again. The method has the effect of improving the accuracy of automatic identification.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic identification, and in particular to an automatic identification method and system based on portrait analysis. Background Art

[0002] Automatic identification refers to the process by which a system or device can automatically acquire, process, and identify the information of a target object through means such as computer technology, sensors, algorithms, etc., without manual intervention, so as to achieve the classification, matching, or understanding of things.

[0003] Currently, when performing automatic identification, generally, a camera is used to collect images of an object, and key geometric features such as the distance between eyes, the height of the nose bridge, and the contour of the lower jaw are extracted from the collected images. Then, the extracted key geometric features are compared with a pre-input template, and the final result is identified based on the similarity.

[0004] During the process of collecting images of an object by the camera, the image collection situation is easily affected by the environment where the camera is located, resulting in errors in automatic identification. Summary of the Invention

[0005] In order to improve the accuracy of automatic identification, the present invention provides an automatic identification method and system based on portrait analysis.

[0006] In a first aspect, the present invention provides an automatic identification method based on portrait analysis, adopting the following technical solution: An automatic identification method based on portrait analysis, comprising: S1: Collect image detection information; S2: Perform light analysis based on the image detection information to obtain environmental light information; S3: Determine whether the environmental light information meets the preset light requirement information; S4: If so, generate and output recognition result information based on the image detection information; S5: If not, determine light intensity deviation information, light uniformity deviation information, and light color temperature deviation information based on the environmental light information and the preset light requirement information; S6: Generate a light adjustment plan based on the light intensity deviation information, the light uniformity deviation information, and the light color temperature deviation information, and execute the light adjustment plan to re-collect the image detection information.

[0007] Optionally, the method for generating the light adjustment plan includes: S61: Retrieve the light intensity deviation value and the brightness deviation position information based on the light intensity deviation information; S62: Retrieve the light uniformity deviation value and the uniformity deviation position information based on the light uniformity deviation information; S63: Retrieve the light color temperature deviation value and the color temperature deviation position information based on the light color temperature deviation information; S64: Determine the deviation position coincidence situation information based on the brightness deviation position information, the uniformity deviation position information, and the color temperature deviation position information; S65: Generate a coincidence adjustment plan based on the deviation position coincidence situation information, the light color temperature deviation value, the light uniformity deviation value, and the light color temperature deviation value, and use the coincidence adjustment plan as the light adjustment plan.

[0008] Optionally, the method for generating the coincidence adjustment plan includes: S651: Retrieve the coincidence type situation information and the coincidence area value based on the deviation position coincidence situation information; S652: Select from the light color temperature deviation value, the light uniformity deviation value, and the light color temperature deviation value based on the coincidence type situation information to form the coincidence type deviation information; S653: Generate the selected device information and the coincidence type adjustment value based on the coincidence type situation information, the coincidence type deviation information, and the coincidence area value; S654: Generate the required number of operating values for the coincidence area based on the coincidence area value and the selected device information; S655: Generate the coincidence area illumination adjustment information based on the selected device information, the coincidence type adjustment value, and the required number of operating values for the coincidence area, and use the coincidence area illumination adjustment information as the coincidence adjustment plan.

[0009] Optionally, the method for generating the selected device information and the coincidence type adjustment value includes: S6531: Determine the type-satisfied device information based on the coincidence type situation information; S6532: Determine the area-satisfied device information based on the coincidence area value; S6533: Determine the comprehensive-satisfied device information based on the type-satisfied device information and the area-satisfied device information; S6534: Determine the satisfied device unit value and the satisfied device influence value based on the comprehensive-satisfied device information; S6535: Determine the satisfied device area achievement value based on the satisfied device unit value and the coincidence area value; S6536: Generate the type deviation requirement value based on the coincidence type deviation information and the satisfied device influence value; S6537: Calculate the difference between the device area achievement value and the category deviation demand value and use it as the area achievement deviation value; S6538: Sort the area achievement deviation values from smallest to largest, and use the device information corresponding to the area achievement deviation value ranked first as the selected device information, and use the corresponding category deviation demand value as the overlapping category adjustment value.

[0010] Optionally, the method for generating the category deviation demand value includes: S65361: Retrieve the single-category deviation value and the category quantity value based on the overlapping category deviation information; S65362: Calculate the average value between the single-category deviation values and use it as the deviation average value; S65363: Determine the quantity influence value based on the category quantity value; S65364: Calculate the product value between the deviation average value and the quantity influence value and use it as the deviation adjustment value; S65365: Calculate the sum value between the deviation adjustment value and the satisfied device influence value and use it as the category deviation demand value.

[0011] Optionally, the method for generating the overlapping area demand running quantity value includes: S6541: Retrieve the selected device specification information based on the selected device information; S6542: Determine the specification unit area value and the specification unit parameter value based on the selected device specification information; S6543: Calculate the quotient value between the overlapping area value and the specification unit area value and use it as the specification initial quantity value; S6544: Determine the specification unit reference value based on the overlapping category adjustment value; S6545: Calculate the difference between the specification unit parameter value and the specification unit reference value and use it as the specification unit deviation value; S6546: Determine the unit deviation influence value based on the specification unit deviation value; S6547: Calculate the product value between the specification initial quantity value and the unit deviation influence value and use it as the overlapping area demand running quantity value.

[0012] Optionally, the method for generating the overlapping area illumination adjustment information includes: S6551: Retrieve the selected device position point based on the selected device information; S6552: Determine the selected device angle value based on the selected device position point and the preset recognition position point; S6553: Determine the number of running control information based on the required overlapping area; S6554: Determine the angle reference influence value based on the overlapping type information; S6555: Calculate the product of the angle reference influence value and the selected device angle value as the actual angle influence value; S6556: Generate brightness output control information based on the selected device angle value, the actual angle influence value, and the overlapping type adjustment value; S6557: Combine the number running control information and the brightness output control information to form the overlapping area illumination adjustment information.

[0013] Optionally, the method for generating the brightness output control information includes: S65561: Determine the brightness adjustment output value based on the actual angle influence value and the overlapping type adjustment value; S65562: Determine the specification reference output value based on the selected device specification information; S65563: Determine whether the brightness adjustment output value is less than the specification reference output value; S65564: If yes, determine the brightness adjustment output control information based on the brightness adjustment output value and use the brightness adjustment output control information as the brightness output control information; S65565: If no, calculate the difference between the brightness adjustment output value and the specification reference output value as the brightness output anomaly value; S65566: Determine the angle adjustment control information based on the brightness output anomaly value and combine the angle adjustment control information with the preset brightness reference output control information as the brightness output control information.

[0014] Optionally, the method for generating the recognition result information includes: S41: Perform human feature recognition based on the image detection information to form image feature information; S42: Determine the feature difference information based on the image feature information and the preset reference feature information; S43: Retrieve the feature type information and the feature number value based on the feature difference information; S44: Retrieve the feature type human information based on the feature type information; S45: Retrieve the human feature reference number value and the human result information based on the feature type human information; S46: Calculate the difference between the human feature reference number value and the feature number value as the feature number deviation value; S47: Sort in ascending order based on the feature number deviation value, and use the person result information corresponding to the feature number deviation value ranked first as the recognition result information.

[0015] In a second aspect, the present invention provides an automatic recognition system based on portrait analysis, adopting the following technical solutions: An automatic recognition system based on portrait analysis, comprising: An acquisition module for acquiring image detection information; A memory for storing an automatic recognition method based on portrait analysis as described in any item of the first aspect; A processor for loading and executing the program in the memory.

[0016] In summary, the present invention includes at least one of the following beneficial technical effects: 1. By collecting image detection information and retrieving ambient light information to determine whether the preset light requirement information is met, when it is met, directly generate and output the recognition result information through the image detection information; when it is not met, determine the light brightness deviation information, light uniformity deviation information, and light color temperature deviation information, generate a light adjustment plan and execute it to re-collect the image detection information, thereby reducing the influence of the environment where the acquisition device is located, and further improving the accuracy of automatic recognition; 2. By retrieving the light brightness deviation value and brightness deviation position information from the light brightness deviation information, retrieving the light uniformity deviation value and uniformity deviation position information from the light uniformity deviation information, retrieving the light color temperature deviation value and color temperature deviation position information from the light color temperature deviation information, and determining the deviation position coincidence situation information through the brightness deviation position information, uniformity deviation position information, and color temperature deviation position information, and then generating a coincidence adjustment plan as the light adjustment plan, thereby improving the accuracy of the obtained light adjustment plan; 3. By performing person feature recognition on the image detection information to form image feature information, determining the feature difference information to retrieve the feature type information and feature value, retrieving the feature type person information from the feature type information, then retrieving the person feature reference value and person result information, calculating the feature number deviation value, and using the person result information corresponding to the feature number deviation value ranked first in ascending order as the recognition result information, thereby improving the accuracy of the obtained recognition result information. Description of the Drawings

[0017] Figure 1 is the flowchart of the method for automatic recognition based on portrait analysis according to an embodiment of the present invention; Figure 2 is the flowchart of the method for generating a light adjustment plan according to an embodiment of the present invention; Figure 3Flowchart of the method for generating the coincidence adjustment scheme according to the embodiments of the present invention; Figure 4 Flowchart of the method for generating the device information selection and the coincidence type adjustment value according to the embodiments of the present invention; Figure 5 Flowchart of the method for generating the type deviation requirement value according to the embodiments of the present invention. Detailed implementation manners

[0018] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0019] An automatic recognition method based on portrait analysis, which detects the illumination condition after collecting image detection information, and directly performs recognition when the illumination condition is satisfied. When the illumination condition is not satisfied, the image is collected again after adjustment according to the illumination deviation condition for recognition, so as to reduce the influence generated by the environment where the collection device is located, and further improve the accuracy of automatic recognition.

[0020] Refer to Figure 1 , the embodiments of the present invention disclose an automatic recognition method based on portrait analysis, which includes: S1: Collect image detection information.

[0021] Among them, the image detection information refers to the image information collected for the object to be recognized during automatic recognition. The image detection information is collected and obtained through a preset collection device, and the collection device can be a camera.

[0022] S2: Perform illumination analysis based on the image detection information to obtain environmental illumination information.

[0023] Among them, the environmental illumination information refers to information such as the illumination brightness, color temperature, and uniformity corresponding to the environment where the collection device is located.

[0024] The image in the image detection information is segmented into multiple sub-regions, and then the gray values of the sub-regions are calculated respectively. The illumination brightness condition is determined based on the gray values of each sub-region, and the gray value of the sub-region where the recognizable reference object is located is used as the color temperature reference and compared with other sub-regions to obtain the illumination color temperature condition. The change condition of the gray value is used as the illumination uniformity condition, and then the illumination brightness condition, illumination color temperature condition, and illumination uniformity condition are combined to obtain environmental illumination information for convenient subsequent use.

[0025] S3: Determine whether the environmental illumination information meets the preset illumination requirement information. If yes, execute S4; if not, execute S5.

[0026] Among them, the light requirement information refers to information such as the light brightness, color temperature, and uniformity corresponding to accurate automatic recognition. The light requirement information is obtained through pre-input.

[0027] By judging whether the ambient light information meets the preset light requirement information, it is determined whether it is necessary to adjust the acquisition of the acquisition device.

[0028] S4: Generate and output recognition result information based on the image detection information.

[0029] Among them, the recognition result information refers to the result information obtained after automatic recognition.

[0030] When the ambient light information meets the preset light requirement information, it means that it is not necessary to adjust the acquisition of the acquisition device at this time. Therefore, by analyzing the image detection information, the recognition result information is generated and output, reducing the influence of the environment where the acquisition device is located, and thus improving the accuracy of automatic recognition.

[0031] S5: Determine the light brightness deviation information, light uniformity deviation information, and light color temperature deviation information based on the ambient light information and the preset light requirement information.

[0032] Among them, the light brightness deviation information refers to the deviation information corresponding to the deviation of the light brightness, the light uniformity deviation information refers to the deviation information corresponding to the deviation of the light uniformity, and the light color temperature deviation information refers to the deviation information corresponding to the deviation of the light color temperature.

[0033] When the ambient light information does not meet the preset light requirement information, it means that it is necessary to adjust the acquisition of the acquisition device at this time. Therefore, by comparing the light brightness, color temperature, and uniformity in the ambient light information with the light brightness, color temperature, and uniformity in the preset light requirement information one by one, and taking the deviation of the light brightness as the light brightness deviation information, the deviation of the uniformity as the light uniformity deviation information, and the deviation of the color temperature as the light color temperature deviation information, which is convenient for subsequent use.

[0034] S6: Generate a light adjustment plan based on the light brightness deviation information, light uniformity deviation information, and light color temperature deviation information, and execute the light adjustment plan to re-acquire the image detection information.

[0035] Among them, the light adjustment plan refers to the plan corresponding to the light adjustment of the environment around the acquisition device.

[0036] By analyzing the illumination brightness deviation information, illumination uniformity deviation information, and illumination color temperature deviation information, an illumination adjustment plan is generated, and the illumination adjustment plan is executed to re-collect the image detection information, reduce the influence of the environment where the acquisition device is located, and thus improve the accuracy of automatic recognition.

[0037] In step S6, in order to further ensure the rationality of the illumination adjustment plan, it is necessary to perform a further separate analysis and calculation on the illumination adjustment plan, which will be described in detail through the following steps.

[0038] Refer to Figure 2 , the method for generating the illumination adjustment plan includes the following steps: S61: Retrieve the illumination brightness deviation value and the brightness deviation position information based on the illumination brightness deviation information.

[0039] Among them, the illumination brightness deviation value refers to the deviation value corresponding to the situation where there is a deviation in the illumination brightness, and the brightness deviation position information refers to the position coverage range information where the illumination brightness situation has a deviation.

[0040] The illumination brightness deviation information includes the illumination brightness deviation value and the brightness deviation position information. By retrieving the illumination brightness deviation value and the brightness deviation position information through the illumination brightness deviation information, it is convenient for subsequent use.

[0041] S62: Retrieve the illumination uniformity deviation value and the uniformity deviation position information based on the illumination uniformity deviation information.

[0042] Among them, the illumination uniformity deviation value refers to the deviation value corresponding to the situation where there is a deviation in the illumination uniformity, and the uniformity deviation position information refers to the position coverage range information where the illumination uniformity situation has a deviation.

[0043] The illumination uniformity deviation information includes the illumination uniformity deviation value and the uniformity deviation position information. By retrieving the illumination uniformity deviation value and the uniformity deviation position information through the illumination uniformity deviation information, it is convenient for subsequent use.

[0044] S63: Retrieve the illumination color temperature deviation value and the color temperature deviation position information based on the illumination color temperature deviation information.

[0045] Among them, the illumination color temperature deviation value refers to the deviation value corresponding to the situation where there is a deviation in the illumination color temperature, and the color temperature deviation position information refers to the position coverage range information where the illumination color temperature situation has a deviation.

[0046] The illumination color temperature deviation information includes the illumination color temperature deviation value and the color temperature deviation position information. By retrieving the illumination color temperature deviation value and the color temperature deviation position information through the illumination color temperature deviation information, it is convenient for subsequent use.

[0047] S64: Determine the deviation position coincidence information based on the luminance deviation position information, uniformity deviation position information, and color temperature deviation position information.

[0048] Among them, the deviation position coincidence information refers to the coincidence types and the coincidence coverage range information corresponding to the coincidence of the luminance situation, uniformity situation, and color temperature situation of the illumination.

[0049] By comparing the positions corresponding to the luminance deviation position information, uniformity deviation position information, and color temperature deviation position information, and then combining according to the coincidence position situation and the corresponding types as the deviation position coincidence information, which is convenient for subsequent use.

[0050] For example, when there is a coincidence in the positions corresponding to the luminance deviation position information and the uniformity deviation position information, at this time, the coincident positions and the luminance and uniformity types are combined as the deviation position coincidence information.

[0051] S65: Generate a coincidence adjustment plan based on the deviation position coincidence information, illumination color temperature deviation value, illumination uniformity deviation value, and illumination color temperature deviation value, and use the coincidence adjustment plan as the illumination adjustment plan.

[0052] Among them, the coincidence adjustment plan refers to the adjustment plan corresponding to the illumination adjustment of the surrounding environment of the acquisition device according to the coincidence position.

[0053] By analyzing the deviation position coincidence information, illumination color temperature deviation value, illumination uniformity deviation value, and illumination color temperature deviation value, a coincidence adjustment plan is generated and used as the illumination adjustment plan, improving the accuracy of the obtained illumination adjustment plan.

[0054] In step S65, in order to further ensure the rationality of the coincidence adjustment plan, it is necessary to perform a further separate analysis and calculation on the coincidence adjustment plan, which is specifically described in detail through the following steps.

[0055] Refer to Figure 3 , the generation method of the coincidence adjustment plan includes the following steps: S651: Retrieve the coincidence type situation information and the coincidence area value based on the deviation position coincidence information.

[0056] Among them, the coincidence type situation information refers to the deviation type information with a coincidence coverage range, and the coincidence area value refers to the area value corresponding to the coincidence coverage range.

[0057] The deviation position coincidence information includes the coincidence type situation information and the coincidence area value. Retrieving the coincidence type situation information and the coincidence area value through the deviation position coincidence information is convenient for subsequent use.

[0058] S652: Select from the light color temperature deviation value, the light uniformity deviation value, and the light color temperature deviation value based on the coincidence type situation information to form the coincidence type deviation information.

[0059] Among them, the coincidence type deviation information refers to the set information of the deviation values corresponding to the deviation types with overlapping coverage ranges.

[0060] Select from the light color temperature deviation value, the light uniformity deviation value, and the light color temperature deviation value through the coincidence type situation information, so as to form the coincidence type deviation information, which is convenient for subsequent use.

[0061] S653: Generate the selection device information and the coincidence type adjustment value based on the coincidence type situation information, the coincidence type deviation information, and the coincidence area value.

[0062] Among them, the selection device information refers to the device information corresponding to the selection of the device for adjusting the light of the surrounding environment of the acquisition device, and the coincidence type adjustment value refers to the adjustment value corresponding to controlling the selection device to adjust the light of the surrounding environment of the acquisition device.

[0063] Analyze the coincidence type situation information, the coincidence type deviation information, and the coincidence area value to generate the selection device information and the coincidence type adjustment value, which is convenient for subsequent use. The specific generation steps of the selection device information and the coincidence type adjustment value refer to S6531 to S6538.

[0064] S654: Generate the running number value required for the coincidence area illumination based on the coincidence area value and the selection device information.

[0065] Among them, the running number value required for the coincidence area illumination refers to the number value corresponding to controlling the single light-emitting part in the selection device to run.

[0066] Analyze the coincidence area value and the selection device information to generate the running number value required for the coincidence area illumination, which is convenient for subsequent use. The selection device is the selected light-emitting panel, and a plurality of single light-emitting parts for illumination are preset on the light-emitting panel. The single light-emitting part for illumination can be a light-emitting diode. The specific generation steps of the running number value required for the coincidence area illumination refer to S6541 to S6547.

[0067] S655: Generate the coincidence area illumination adjustment information based on the selection device information, the coincidence type adjustment value, and the running number value required for the coincidence area illumination, and use the coincidence area illumination adjustment information as the coincidence adjustment plan.

[0068] Among them, the coincidence area illumination adjustment information refers to the adjustment information corresponding to controlling the selection device to perform illumination adjustment according to the coincidence area.

[0069] By analyzing the selected device information, the coincidence type adjustment value, and the running values of the coincidence area requirements, the coincidence area illumination adjustment information is generated and used as the coincidence adjustment plan, thereby improving the accuracy of the obtained coincidence adjustment plan. The specific generation steps of the coincidence area illumination adjustment information refer to S6551 to S6557.

[0070] In step S653, in order to further ensure the rationality of the selected device information and the coincidence type adjustment value, it is necessary to perform a further separate analysis and calculation on the selected device information and the coincidence type adjustment value, which will be specifically described in detail through the following steps.

[0071] Refer to Figure 4 , the generation method of the selected device information and the coincidence type adjustment value includes the following steps: S6531: Determine the type-satisfied device information based on the coincidence type situation information.

[0072] Among them, the type-satisfied device information refers to the device information selected according to the coincidence type situation information, and different coincidence type situation information corresponds to different type-satisfied device information.

[0073] By inputting the coincidence type situation information into the preset type-satisfied device database to match and obtain the type-satisfied device information, it is convenient for subsequent use. The type-satisfied device database pre-stores a comparison table of different coincidence type situation information and the corresponding type-satisfied device information, and the type-satisfied device database is obtained through pre-input.

[0074] S6532: Determine the area-satisfied device information based on the coincidence area value.

[0075] Among them, the area-satisfied device information refers to the device information selected to meet the coincidence area, and different coincidence area values correspond to different area-satisfied device information.

[0076] By inputting the coincidence area value into the preset area-satisfied device database to match and obtain the area-satisfied device information, it is convenient for subsequent use. The area-satisfied device database pre-stores a comparison table of different coincidence area values and the corresponding area-satisfied device information, and the area-satisfied device database is obtained through pre-input.

[0077] S6533: Determine the comprehensive-satisfied device information based on the type-satisfied device information and the area-satisfied device information.

[0078] Among them, the comprehensive-satisfied device information refers to the device information corresponding to comprehensively satisfying the type and the area.

[0079] By comparing the consistency between the device information meeting the type requirements and the device information meeting the area requirements, the devices with consistency between the device information meeting the type requirements and the device information meeting the area requirements are used as the comprehensive device information meeting the requirements, which is convenient for subsequent use.

[0080] S6534: Determine the device unit value meeting the requirements and the device influence value meeting the requirements based on the comprehensive device information meeting the requirements.

[0081] Among them, the device unit value meeting the requirements refers to the adjustment value that the device meeting the requirements can reach per unit area, and the device influence value meeting the requirements refers to the influence value on the adjustment when the device meeting the requirements makes adjustments. Different comprehensive device information meeting the requirements corresponds to different device unit values meeting the requirements and device influence values meeting the requirements.

[0082] By inputting the comprehensive device information meeting the requirements into a preset comprehensive device information database to match and obtain the device unit value meeting the requirements and the device influence value meeting the requirements, it is convenient for subsequent use. The comprehensive device information database prestores a comparison table of different comprehensive device information meeting the requirements and the corresponding device unit values meeting the requirements and device influence values meeting the requirements, and the comprehensive device information database is obtained through pre-input.

[0083] S6535: Determine the device area achievement value based on the device unit value meeting the requirements and the overlapping area value.

[0084] Among them, the device area achievement value refers to the adjustment value that the device meeting the requirements can reach under the overlapping area.

[0085] By calculating the product value between the device unit value meeting the requirements and the overlapping area value, and taking the calculated product value as the device area achievement value, it is convenient for subsequent use.

[0086] S6536: Generate the type deviation requirement value based on the overlapping type deviation information and the device influence value meeting the requirements.

[0087] Among them, the type deviation requirement value refers to the adjustment value that needs to be reached based on the overlapping type deviation information.

[0088] By analyzing the overlapping type deviation information and the device influence value meeting the requirements, the type deviation requirement value is generated, which is convenient for subsequent use. The specific determination steps of the type deviation requirement value refer to S65361 to S65365.

[0089] S6537: Calculate the difference between the device area achievement value and the type deviation requirement value and take it as the area achievement deviation value.

[0090] Among them, the area achievement deviation value refers to the deviation value corresponding to the situation where there is a deviation in the adjustment value that the device meeting the requirements can reach.

[0091] By calculating the difference between the device area achievement value and the type deviation requirement value and using it as the area achievement deviation value, it is convenient for subsequent use.

[0092] S6538: Sort the area achievement deviation values from smallest to largest, and use the device information that meets the type corresponding to the smallest area achievement deviation value as the selected device information, and use the corresponding type deviation requirement value as the overlapping type adjustment value.

[0093] Among them, by sorting the area achievement deviation values from smallest to largest, and using the device information that meets the type corresponding to the smallest area achievement deviation value as the selected device information, and using the corresponding type deviation requirement value as the overlapping type adjustment value, the accuracy of the obtained selected device information and overlapping type adjustment value can be improved.

[0094] In step S6536, in order to further ensure the rationality of the type deviation requirement value, it is necessary to perform a more detailed separate analysis and calculation on the type deviation requirement value, which is specifically described in detail through the following steps.

[0095] Refer to Figure 5 , the generation method of the type deviation requirement value includes the following steps: S65361: Retrieve the single type deviation value and the type number value based on the overlapping type deviation information.

[0096] Among them, the single type deviation value refers to the deviation value corresponding to a single overlapping type, and the type number value refers to the number value corresponding to the overlapping type.

[0097] The overlapping type deviation information includes the single type deviation value and the type number value. Retrieving the single type deviation value and the type number value through the overlapping type deviation information is convenient for subsequent use.

[0098] S65362: Calculate the average value between the single type deviation values and use it as the deviation average value.

[0099] Among them, the deviation average value refers to the average value corresponding to each single type deviation value. By calculating the average value between each single type deviation value and using it as the deviation average value, it is convenient for subsequent use.

[0100] S65363: Determine the number influence value based on the type number value.

[0101] Among them, the number influence value refers to the influence value of the number on the average value, and different type number values correspond to different number influence values.

[0102] By inputting the number of types of values into a preset number impact database for matching to obtain a number impact value, it is convenient for subsequent use. The number impact database pre-stores a comparison table of different numbers of types of values and the corresponding number impact values, and the number impact database is obtained through pre-input.

[0103] S65364: Calculate the product value between the deviation average value and the number impact value and use it as the deviation adjustment value.

[0104] Among them, the deviation adjustment value refers to the adjustment value corresponding to the deviation average value after adjustment.

[0105] By calculating the product value between the deviation average value and the number impact value and using it as the deviation adjustment value, it is convenient for subsequent use.

[0106] S65365: Calculate the sum value between the deviation adjustment value and the device impact value that meets the requirements and use it as the type deviation requirement value.

[0107] Among them, by calculating the sum value between the deviation adjustment value and the device impact value that meets the requirements and using it as the type deviation requirement value, the accuracy of the obtained type deviation requirement value is improved.

[0108] In step S654, in order to further ensure the rationality of the number of running values required for the overlapping area, it is necessary to perform a further separate analysis and calculation on the number of running values required for the overlapping area. The specific details are described in the following steps.

[0109] The generation method of the number of running values required for the overlapping area includes the following steps: S6541: Retrieve the selected device specification information based on the selected device information.

[0110] Among them, the selected device specification information refers to the specification information corresponding to the selected device, and the selected device information includes the selected device specification information. Retrieving the selected device specification information through the selected device information is convenient for subsequent use.

[0111] S6542: Determine the specification unit area value and the specification unit parameter value based on the selected device specification information.

[0112] Among them, the specification unit area value refers to the area value corresponding to a single illuminating part of the light of the selected device's specification, and the specification unit parameter value refers to the operating value corresponding to the operation of a single illuminating part of the light of the selected device's specification. Different selected device specification information corresponds to different specification unit area values and specification unit parameter values.

[0113] By inputting the selected device specification information into a preset selected device specification database to match and obtain the specification unit area value and the specification unit parameter value, it is convenient for subsequent use. The selected device specification database pre-stores a comparison table of different selected device specification information and the corresponding specification unit area value and specification unit parameter value, and the selected device specification database is obtained through pre-input.

[0114] S6543: Calculate the quotient value between the overlapping area value and the specification unit area value and use it as the specification initial number value.

[0115] Among them, the specification initial number value refers to the initial number value that requires the light single illuminating parts of the device specification to operate. By calculating the quotient value between the overlapping area value and the specification unit area value and using it as the specification initial number value, it is convenient for subsequent use.

[0116] S6544: Determine the specification unit reference value based on the overlapping type adjustment value.

[0117] Among them, the specification unit reference value refers to the reference operating value corresponding to the operation of the light single illuminating part according to the overlapping type adjustment value. Different overlapping type adjustment values correspond to different specification unit reference values.

[0118] By inputting the overlapping type adjustment value into a preset specification unit reference database to match and obtain the specification unit reference value, it is convenient for subsequent use. The specification unit reference database pre-stores a comparison table of different overlapping type adjustment values and the corresponding specification unit reference values, and the specification unit reference database is obtained through pre-input.

[0119] S6545: Calculate the difference between the specification unit parameter value and the specification unit reference value and use it as the specification unit deviation value.

[0120] Among them, the specification unit deviation value refers to the deviation value corresponding to the deviation when the light single illuminating part operates.

[0121] By calculating the difference between the specification unit parameter value and the specification unit reference value and using it as the specification unit deviation value, it is convenient for subsequent use.

[0122] S6546: Determine the unit deviation influence value based on the specification unit deviation value.

[0123] Among them, the unit deviation influence value refers to the influence value on the quantity according to the specification unit deviation value. Different specification unit deviation values correspond to different unit deviation influence values.

[0124] By inputting different specification unit deviation values into a preset unit deviation impact database to obtain the unit deviation impact values, it is convenient for subsequent use. The unit deviation impact database pre-stores a comparison table of different specification unit deviation values and the corresponding unit deviation impact values, and the unit deviation impact database is obtained through pre-input.

[0125] S6547: Calculate the product value between the initial specification value and the unit deviation impact value and use it as the running value of the coincidence area requirement.

[0126] Among them, by calculating the product value between the initial specification value and the unit deviation impact value and using it as the running value of the coincidence area requirement, the accuracy of the obtained running value of the coincidence area requirement is improved.

[0127] In step S655, in order to further ensure the rationality of the coincidence area illumination adjustment information, it is necessary to perform a further separate analysis and calculation on the coincidence area illumination adjustment information, which is specifically described in detail through the following steps.

[0128] The method for generating the coincidence area illumination adjustment information includes the following steps: S6551: Retrieve the selected device position point based on the selected device information.

[0129] Among them, the selected device position point refers to the position point where the selection is located, and the selected device information includes the selected device position point. Retrieving the selected device position point through the selected device information is convenient for subsequent use.

[0130] S6552: Determine the selected device angle value based on the selected device position point and the preset recognition position point.

[0131] Among them, the recognition position point refers to the position point corresponding to the area where the acquisition device performs recognition, and the recognition position point is obtained through pre-input. The selected device angle value refers to the angle value between the selected device and the recognition area.

[0132] By calculating the angle between the selected device position point and the preset recognition position point as the selected device angle value, it is convenient for subsequent use.

[0133] S6553: Determine the number running control information based on the running value of the coincidence area requirement.

[0134] Among them, the number running control information refers to the number control information for controlling the operation of a single illumination element. Different running values of the coincidence area requirement correspond to different number running control information.

[0135] By inputting the running value of the overlapping area requirement into a preset running control database for the number to match and obtain the running control information for the number, it is convenient for subsequent use. The running control database for the number pre-stores a comparison table of different running values of the overlapping area requirement and the corresponding running control information for the number, and the running control database for the number is obtained through pre-input.

[0136] S6554: Determine the angular reference influence value based on the overlapping type situation information.

[0137] Among them, the angular reference influence value refers to the reference influence value of the unit angle on the brightness according to the overlapping type situation information, and different overlapping type situation information corresponds to different angular reference influence values.

[0138] By inputting the overlapping type situation information into a preset angular reference influence database to match and obtain the angular reference influence value, it is convenient for subsequent use. The angular reference influence database pre-stores a comparison table of different overlapping type situation information and the corresponding angular reference influence values, and the angular reference influence database is obtained through pre-input.

[0139] S6555: Calculate the product value between the angular reference influence value and the selected device angle value and use it as the actual angular influence value.

[0140] Among them, the actual angular influence value refers to the actual influence value of the angle size on the brightness.

[0141] By calculating the product value between the angular reference influence value and the selected device angle value and using it as the actual angular influence value, it is convenient for subsequent use.

[0142] S6556: Generate brightness output control information based on the selected device angle value, the actual angular influence value, and the overlapping type adjustment value.

[0143] Among them, the brightness output control information refers to the brightness control information for controlling the operation of a single illuminating component of the light.

[0144] By analyzing the selected device angle value, the actual angular influence value, and the overlapping type adjustment value, the brightness output control information is generated, which is convenient for subsequent use. The specific generation steps of the brightness output control information refer to S65561 to S65566.

[0145] S6557: Combine the running control information for the number and the brightness output control information to form overlapping area illumination adjustment information.

[0146] Among them, by combining the running control information for the number and the brightness output control information, the overlapping area illumination adjustment information is formed, improving the accuracy of the obtained overlapping area illumination adjustment information.

[0147] In step S6556, in order to further ensure the rationality of the brightness output control information, it is necessary to perform a further separate analysis and calculation on the brightness output control information, which will be specifically described in detail through the steps shown below.

[0148] The method for generating the brightness output control information includes the following steps: S65561: Determine the brightness adjustment output value based on the actual angle influence value and the coincidence type adjustment value.

[0149] Among them, the brightness adjustment output value refers to the output value corresponding to the adjustment of the coincidence type adjustment value according to the angle.

[0150] By calculating the sum value between the actual angle influence value and the coincidence type adjustment value as the brightness adjustment output value, it is convenient for subsequent use.

[0151] S65562: Determine the specification reference output value based on the selected device specification information.

[0152] Among them, the specification reference output value refers to the maximum value corresponding to the output when the specification to which the selected device belongs is output, and different selected device specification information corresponds to different specification reference output values.

[0153] By inputting the selected device specification information into the preset specification reference output database to match and obtain the specification reference output value, it is convenient for subsequent use. The specification reference output database pre-stores a comparison table of different selected device specification information and the corresponding specification reference output values, and the specification reference output database is obtained through pre-input.

[0154] S65563: Determine whether the brightness adjustment output value is less than the specification reference output value. If it is, execute S65564; if not, execute S65565.

[0155] Among them, by judging whether the brightness adjustment output value is less than the specification reference output value, it is possible to judge whether it is necessary to adjust the angle.

[0156] S65564: Determine the brightness adjustment output control information based on the brightness adjustment output value, and use the brightness adjustment output control information as the brightness output control information.

[0157] Among them, the brightness adjustment output control information refers to the control information for controlling the operation of a single illuminating part of the light according to the brightness adjustment output value. Different brightness adjustment output values correspond to different brightness adjustment output control information.

[0158] When the brightness adjustment output value is less than the specification reference output value, it indicates that there is no need to adjust the angle at this time. Therefore, the brightness adjustment output value is input into a preset brightness adjustment output control database to obtain the brightness adjustment output control information through matching, and the brightness adjustment output control information is used as the brightness output control information, thereby improving the accuracy of the obtained brightness output control information.

[0159] The brightness adjustment output control database pre-stores a comparison table of different brightness adjustment output values and the corresponding brightness adjustment output control information, and the brightness adjustment output control database is obtained through pre-input.

[0160] S65565: Calculate the difference between the brightness adjustment output value and the specification reference output value and use it as the brightness output anomaly value.

[0161] Among them, the brightness output anomaly value refers to the anomaly value corresponding to the situation where there is an insufficient anomaly during brightness output.

[0162] When the brightness adjustment output value is not less than the specification reference output value, it indicates that the angle needs to be adjusted at this time. Therefore, the difference between the brightness adjustment output value and the specification reference output value is calculated and used as the brightness output anomaly value for subsequent use.

[0163] S65566: Determine the angle adjustment control information based on the brightness output anomaly value, and combine the angle adjustment control information with the preset brightness reference output control information as the brightness output control information.

[0164] Among them, the angle adjustment control information refers to the control information for controlling the operation of a single illuminating component according to the brightness output anomaly value. The brightness reference output control information refers to the control information corresponding to the brightness output at the maximum power, and the brightness reference output control information is obtained through pre-input.

[0165] Different brightness output anomaly values correspond to different angle adjustment control information. By inputting the brightness output anomaly value into a preset angle adjustment control database to obtain the angle adjustment control information through matching, and combining the angle adjustment control information with the preset brightness reference output control information as the brightness output control information, the accuracy of the obtained brightness output control information is improved.

[0166] The angle adjustment control database pre-stores a comparison table of different brightness output anomaly values and the corresponding angle adjustment control information, and the angle adjustment control database is obtained through pre-input.

[0167] In step S4, in order to further ensure the rationality of the recognition result information, it is necessary to perform a further separate analysis and calculation on the recognition result information, which is specifically described in detail through the following steps.

[0168] The method for generating recognition result information includes the following steps: S41: Perform human feature recognition based on image detection information to form image feature information.

[0169] Among them, the image feature information refers to the feature information related to the person in the collected image.

[0170] By performing human feature recognition on the image detection information, image feature information is formed, which is convenient for subsequent use. Performing human feature recognition on images belongs to the prior art and will not be elaborated here.

[0171] S42: Determine feature difference information based on the image feature information and the preset reference feature information.

[0172] Among them, the reference feature information refers to the reference information in each feature of the person, and the reference feature information is obtained through pre-input. The feature difference information refers to the difference information corresponding to the differences in the human features in the collected image.

[0173] By comparing the image feature information with the preset reference feature information, when there are differences, the type of the feature and the number of differences are used as the feature difference information, which is convenient for subsequent use.

[0174] S43: Retrieve the feature type information and the feature value based on the feature difference information.

[0175] Among them, the feature type information refers to the type information of the features with differences, and the feature value refers to the value corresponding to the differences in the features with differences. The feature difference information includes the feature type information and the feature value. Retrieving the feature type information and the feature value through the feature difference information is convenient for subsequent use.

[0176] S44: Retrieve the feature type person information based on the feature type information.

[0177] Among them, the feature type person information refers to the person information of the person with the difference feature type, and different feature type information corresponds to different feature type person information.

[0178] By inputting the feature type information into the preset feature type person database to match and obtain the feature type person information, which is convenient for subsequent use. The feature type person database pre-stores a comparison table of different feature type information and the corresponding feature type person information, and the feature type person database is obtained through pre-input.

[0179] S45: Retrieve the person feature reference value and the person result information based on the feature type person information.

[0180] Among them, the reference value of the number of personal characteristics refers to the value of the number of characteristics corresponding to the person with this type of distinguishing feature, and the personal result information refers to the result information corresponding to the person identified as having this type of distinguishing feature. Different types of characteristic person information correspond to different reference values of the number of personal characteristics and personal result information.

[0181] By inputting the information of the characteristic type of person into the preset database of the characteristic type of person to match and obtain the reference value of the number of personal characteristics and the personal result information, the database of the characteristic type of person also stores a comparison table of different types of characteristic person information and the corresponding reference values of the number of personal characteristics and personal result information, and the database of the characteristic type of person is obtained through pre-input.

[0182] S46: Calculate the difference between the reference value of the number of personal characteristics and the value of the number of characteristics, and use it as the deviation value of the number of characteristics.

[0183] Among them, the deviation value of the number of characteristics refers to the deviation value corresponding to the deviation of the number of characteristics with differences.

[0184] By calculating the difference between the reference value of the number of personal characteristics and the value of the number of characteristics and using it as the deviation value of the number of characteristics, it is convenient for subsequent use.

[0185] S47: Sort the deviation values of the number of characteristics from small to large, and use the personal result information corresponding to the deviation value of the number of characteristics ranked first as the recognition result information.

[0186] Among them, by sorting the deviation values of the number of characteristics from small to large and using the personal result information corresponding to the deviation value of the number of characteristics ranked first as the recognition result information, the accuracy of the obtained recognition result information is improved.

[0187] Based on the same inventive concept, an embodiment of the present invention provides an automatic recognition system based on portrait analysis, including: An acquisition module for acquiring image detection information; A memory for storing an automatic recognition method based on portrait analysis as described above; A processor for loading and executing the program in the memory.

[0188] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above division of each functional module is used as an example. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working processes of the system, device, and unit described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0189] The above is only the preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements should also be regarded as within the protection scope of the present invention.

Claims

1. An automatic recognition method based on portrait analysis, characterized in that Including: S1: Collect image detection information; S2: Perform light analysis based on the image detection information to obtain ambient light information; S3: Determine whether the ambient light information meets the preset light requirement information; S4: If yes, generate recognition result information based on the image detection information and output it; S5: If not, determine the light brightness deviation information, light uniformity deviation information, and light color temperature deviation information based on the ambient light information and the preset light requirement information; S6: Generate a light adjustment plan based on the light brightness deviation information, the light uniformity deviation information, and the light color temperature deviation information, and execute the light adjustment plan to re-collect the image detection information.

2. The automatic recognition method based on portrait analysis according to claim 1, characterized in that, The method for generating the light adjustment plan includes: S61: Retrieve the light brightness deviation value and the brightness deviation position information based on the light brightness deviation information; S62: Retrieve the light uniformity deviation value and the uniformity deviation position information based on the light uniformity deviation information; S63: Retrieve the light color temperature deviation value and the color temperature deviation position information based on the light color temperature deviation information; S64: Determine the deviation position coincidence situation information based on the brightness deviation position information, the uniformity deviation position information, and the color temperature deviation position information; S65: Generate a coincidence adjustment plan based on the deviation position coincidence situation information, the light color temperature deviation value, the light uniformity deviation value, and the light color temperature deviation value, and use the coincidence adjustment plan as the light adjustment plan.

3. The automatic recognition method based on portrait analysis according to claim 2, characterized in that, The method for generating the coincidence adjustment plan includes: S651: Retrieve the coincidence type situation information and the coincidence area value based on the deviation position coincidence situation information; S652: Select from the light color temperature deviation value, the light uniformity deviation value, and the light color temperature deviation value based on the coincidence type situation information to form the coincidence type deviation information; S653: Generate the selected device information and the coincidence type adjustment value based on the coincidence type situation information, the coincidence type deviation information, and the coincidence area value; S654: Generate the coincidence area required operation number value based on the coincidence area value and the selected device information; S655: Generate the coincidence area illumination adjustment information based on the selected device information, the coincidence type adjustment value, and the coincidence area required operation number value, and use the coincidence area illumination adjustment information as the coincidence adjustment plan.

4. The automatic recognition method based on portrait analysis according to claim 3, characterized in that, The method for generating the selected device information and the coincidence type adjustment value includes: S6531: Determine the type satisfaction device information based on the coincidence type situation information; S6532: Determine the area satisfaction device information based on the coincidence area value; S6533: Determine the comprehensive satisfaction device information based on the type satisfaction device information and the area satisfaction device information; S6534: Determine the satisfaction device unit value and the satisfaction device influence value based on the comprehensive satisfaction device information; S6535: Determine the satisfaction device area achievement value based on the satisfaction device unit value and the coincidence area value; S6536: Generate the type deviation requirement value based on the coincidence type deviation information and the satisfaction device influence value; S6537: Calculate the difference between the achieved device area value and the variety deviation requirement value and use it as the area achievement deviation value; S6538: Sort in ascending order based on the area achievement deviation value, and use the device information of the variety corresponding to the first sorted area achievement deviation value as the selected device information, and use the corresponding variety deviation requirement value as the overlapping variety adjustment value.

5. The automatic recognition method based on portrait analysis according to claim 4, characterized in that, The generation method of the variety deviation requirement value includes: S65361: Retrieve the single variety deviation value and the variety quantity value based on the overlapping variety deviation information; S65362: Calculate the average value between the single variety deviation values and use it as the deviation average value; S65363: Determine the quantity influence value based on the variety quantity value; S65364: Calculate the product value between the deviation average value and the quantity influence value and use it as the deviation adjustment value; S65365: Calculate the sum value between the deviation adjustment value and the satisfied device influence value and use it as the variety deviation requirement value.

6. The automatic recognition method based on portrait analysis according to claim 3, characterized in that, The generation method of the overlapping area demand operation quantity value includes: S6541: Retrieve the selected device specification information based on the selected device information; S6542: Determine the specification unit area value and the specification unit parameter value based on the selected device specification information; S6543: Calculate the quotient value between the overlapping area value and the specification unit area value and use it as the specification initial quantity value; S6544: Determine the specification unit reference value based on the overlapping variety adjustment value; S6545: Calculate the difference between the specification unit parameter value and the specification unit reference value and use it as the specification unit deviation value; S6546: Determine the unit deviation influence value based on the specification unit deviation value; S6547: Calculate the product value between the specification initial quantity value and the unit deviation influence value and use it as the overlapping area demand operation quantity value.

7. The automatic recognition method based on portrait analysis according to claim 6, wherein The generation method of the overlapping area illumination adjustment information includes: S6551: Retrieve the selected device position point based on the selected device information; S6552: Determine the selected device angle value based on the selected device position point and the preset recognition position point; S6553: Determine the quantity operation control information based on the overlapping area demand operation quantity value; S6554: Determine the angle reference influence value based on the overlapping variety situation information; S6555: Calculate the product value between the angle reference influence value and the selected device angle value and use it as the angle actual influence value; S6556: Generate the brightness output control information based on the selected device angle value, the angle actual influence value, and the overlapping variety adjustment value; S6557: Combine the quantity operation control information and the brightness output control information to form the overlapping area illumination adjustment information.

8. The automatic recognition method based on portrait analysis according to claim 7, wherein The generation method of the brightness output control information includes: S65561: Determine the brightness adjustment output value based on the angle actual influence value and the overlapping variety adjustment value; S65562: Determine the specification reference output value based on the selected device specification information; S65563: Determine whether the brightness adjustment output value is less than the specification reference output value; S65564: If it is yes, determine the brightness adjustment output control information based on the brightness adjustment output value, and use the brightness adjustment output control information as the brightness output control information; S65565: If it is no, calculate the difference between the brightness adjustment output value and the specification reference output value as the brightness output anomaly value; S65566: Determine the angle adjustment control information based on the brightness output anomaly value, and combine the angle adjustment control information with the preset brightness reference output control information as the brightness output control information.

9. An automatic recognition method based on portrait analysis according to claim 1, characterized in that, The method for generating the recognition result information includes: S41: Perform human feature recognition based on the image detection information to form image feature information; S42: Determine the feature difference information based on the image feature information and the preset reference feature information; S43: Retrieve the feature type information and the feature value based on the feature difference information; S44: Retrieve the feature type human information based on the feature type information; S45: Retrieve the human feature reference value and the human result information based on the feature type human information; S46: Calculate the difference between the human feature reference value and the feature value as the feature number deviation value; S47: Sort the feature number deviation values from small to large, and use the human result information corresponding to the first sorted feature number deviation value as the recognition result information.

10. An automatic recognition system based on portrait analysis, characterized in that, It includes: An acquisition module for acquiring image detection information; A memory for storing an automatic recognition method according to any one of claims 1 to 9; A processor for loading and executing the program in the memory.

Images