Auxiliary detection system and detection method for local skin quality

Abstract

The present invention relates to the technical field of skin auxiliary detection, and specifically to an auxiliary detection system and method for local skin quality. The present invention first obtains a thermal histogram of a local skin area to be tested, and all thermal intervals of suspected defective skin therein; further, according to the distribution of pixels in the thermal map under each thermal interval of suspected defective skin, all pore areas and all acne areas in the thermal map are determined; then, according to the position distribution and area information of all pore areas, the local skin area to be tested is divided, and the oiliness coefficient of each skin partition is further evaluated in combination with the water loss rate and the distribution of pore acne in each skin partition. The present invention combines the characteristics of oily skin with vigorous oil secretion and the proneness to skin defects such as pore acne, and the characteristics that the temperature of the defective epidermis is slightly higher than the normal healthy epidermis temperature, to quantitatively analyze the oiliness coefficient of the local skin and improve the skin quality detection effect of the local skin.

Description

Technical Field

[0001] The present invention relates to the technical field of skin auxiliary detection, and in particular to an auxiliary detection system and a detection method for local skin quality. Background Art

[0002] Each person's skin type may be different due to factors such as genes, environment and lifestyle, and can be roughly divided into oily skin, dry skin and combination skin. Skin quality testing can help relevant testers analyze and evaluate the current facial skin condition, so as to develop a more scientific and personalized skin care plan. It can also help solve various skin problems and maintain skin health.

[0003] In the prior art, local skin images can be collected based on image processing technology, and pores in local skin images can be automatically identified based on machine learning and deep learning models. Then, the oil distribution in the local skin area can be analyzed and evaluated through skin reflection, pore size and distribution to assist in skin quality assessment. However, the training process of the pore recognition model is cumbersome, and the collection quality of local skin images and the training effect of the pore recognition model are not good, which will affect the accuracy of the evaluation results of the oil distribution, and thus affect the skin quality detection effect of the local skin. Summary of the invention

[0004] In order to solve the technical problem that the existing technology has poor detection effect on local skin quality, the purpose of the present invention is to provide an auxiliary detection system and detection method for local skin quality. The technical scheme adopted is as follows: An auxiliary detection method for local skin quality, the method comprising: Obtaining a thermal histogram of a thermal map of a local skin area to be tested, and screening out all thermal intervals of suspected defective skin from all thermal intervals of the thermal histogram; According to the distribution and aggregation of pixel points in the heat map under each suspected blemish skin thermal interval, the screening coefficient of each suspected blemish skin thermal interval as a pore thermal interval is obtained; according to the screening coefficient, the pore thermal interval is screened out from all suspected blemish skin thermal intervals, and all pore areas in the heat map are obtained; according to the pore thermal interval and the distribution information of pixel points in the heat map under the other suspected blemish skin thermal intervals, all acne areas in the heat map are obtained; According to the position distribution and area information of all the pore areas, the local skin area to be tested is divided to obtain all skin partitions; the water loss rate in each skin partition is obtained; according to the water loss rate in each skin partition, combined with the number of the pore areas and the number of the acne areas in the corresponding area of ​​each skin partition in the thermal map, the oiliness coefficient of each skin partition in the local skin area to be tested is obtained.

[0005] Furthermore, the method for obtaining the thermal interval of the suspected defective skin includes: In the thermal histogram, the product of the normalized result of the frequency corresponding to each thermal interval and the negative correlation mapping result of the thermal value corresponding to the right end point of each thermal interval is used as the confidence coefficient of each thermal interval being a normal skin temperature; and the normal skin thermal interval is selected from all thermal intervals according to the confidence coefficient; A thermal distribution curve is fitted based on the thermal histogram, and the thermal interval corresponding to each maximum point in the thermal distribution curve in the thermal histogram is taken as a suspected interval; in the thermal histogram, all suspected intervals located to the right of the normal skin thermal interval are taken as suspected defective skin thermal intervals.

[0006] Furthermore, the method for obtaining the normal skin thermal range includes: The thermal interval with the largest confidence coefficient in the thermal histogram is taken as the normal skin thermal interval.

[0007] Furthermore, the method for obtaining the screening coefficient includes: Taking any suspected blemish skin thermal interval as the target interval, performing regional connectivity detection on all pixel points corresponding to the target interval in the thermal map, and taking each connected domain as a suspected pore; constructing a preset neighborhood with each of the suspected pores as the center; In a preset neighborhood of each of the suspected pores, according to the uniformity of the spatial distribution of the suspected pores and the area similarity between different suspected pores, a screening sub-parameter whose target interval is the pore thermal interval is obtained; The preset neighborhoods of all the suspected pores correspond to the screening sub-parameters to obtain the screening coefficient whose target interval is the pore thermal interval.

[0008] Furthermore, the method for obtaining the screening sub-parameters includes: In a preset neighborhood of each of the suspected pores, the center of the preset neighborhood corresponding to the suspected pore is used as the target pore, and the remaining suspected pores are used as reference pores; The deviations of the distances between each reference pore and the target pore relative to the average level are accumulated and negatively correlated, and the negative correlation mapping result is used as the pore uniform distribution parameter; the negative correlation mapping result of the accumulated sum of the area differences between each reference pore and the target pore is used as the pore area similarity parameter; The pore uniform distribution parameter and the pore area similarity parameter are integrated to obtain a screening sub-parameter whose target interval is the pore thermal interval.

[0009] Furthermore, the method for obtaining the pore thermal interval and the pore area includes: The thermal interval with the largest screening coefficient is taken as the pore thermal interval; the pore thermal interval in the thermal map is subjected to regional connectivity detection corresponding to all pixel points, and each connected domain is taken as a pore area.

[0010] Furthermore, the method for obtaining the acne area includes: In the thermal histogram, all suspected intervals located to the right of the pore thermal interval are regarded as suspected acne thermal intervals; in each suspected acne thermal interval, all corresponding pixel points in the thermal map are subjected to regional connectivity detection, and each connected domain is regarded as a suspected acne; The suspected acne thermal interval corresponding to the maximum total area of ​​the suspected acne is used as the acne thermal interval; all the suspected acnes corresponding to the acne thermal interval are used as all acne areas in the heat map.

[0011] Furthermore, the method for obtaining skin partitions includes: Based on the area difference and spatial distance between the pore areas, the metric distance between different pore areas is obtained; based on the distance clustering algorithm and the metric distance, all the pore areas are clustered to obtain all clusters; the corresponding area of ​​each cluster in the heat map is taken as a divided area, and the corresponding area of ​​each divided area in the heat map in the local skin area to be measured is taken as a skin partition.

[0012] Furthermore, the method for obtaining the oiliness coefficient includes: In the area corresponding to each skin partition in the heat map, the total area of ​​all the pore areas is used as the first oiliness parameter, and the total number of all the acne areas plus a preset normal number is used as the second oiliness parameter; the negative correlation mapping result of the water loss rate of each skin partition is used as the third oiliness parameter; The first oiliness parameter, the second oiliness parameter and the third oiliness parameter are integrated to obtain the oiliness coefficient of the corresponding skin partition.

[0013] The present invention also proposes an auxiliary detection system for local skin quality, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the auxiliary detection method for local skin quality when executing the computer program.

[0014] The present invention has the following beneficial effects: The present invention obtains a thermal histogram of a thermal map of a local skin area to be tested, and screens out all suspected defective skin thermal zones from all thermal zones of the thermal histogram, so as to prepare for the subsequent determination of corresponding zones of skin defects such as pores and acne based on epidermal temperature difference characteristics; according to the distribution and aggregation of pixels in the thermal map under each suspected defective skin thermal zone, a screening coefficient of each suspected defective skin thermal zone as a pore thermal zone is obtained, and the screening coefficient reflects the possibility that the pixels in the thermal map under each suspected defective skin thermal zone are the pixels corresponding to the pores, and then the pore thermal zone can be screened out according to the screening coefficient, and all pore areas and all acne areas in the thermal map can be further accurately obtained; then, based on the similarity and uniform distribution characteristics of the local pore areas, the local skin area to be tested is divided, and all skin partitions are obtained, so as to prepare for the subsequent analysis and evaluation of the oiliness of the skin of each skin partition; the water loss rate in each skin partition is obtained, and then the oiliness coefficient of each skin partition in the local skin area to be tested is obtained in combination with the number of pore areas and the number of acne areas in the corresponding area of ​​each skin partition in the thermal map. The present invention combines the characteristics of oily skin that has vigorous oil secretion and is prone to skin defects such as pores and acne, and the characteristic that the temperature of the blemished epidermis is slightly higher than the temperature of normal healthy epidermis, quantitatively analyzes the oiliness coefficient of the local skin, and improves the skin quality detection effect of the local skin. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A flow chart of an auxiliary detection method for local skin quality provided by an embodiment of the present invention; Figure 2 A thermal histogram is provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the auxiliary detection system and detection method of local skin quality proposed by the present invention, its specific implementation, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The specific scheme of the auxiliary detection system and detection method of local skin quality provided by the present invention is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of an auxiliary detection method for local skin quality provided by an embodiment of the present invention, specifically comprising: Step S1, obtaining a thermal histogram of a thermal map of a local skin area to be tested, and screening out all thermal intervals of suspected defective skin from all thermal intervals of the thermal histogram.

[0021] It should be noted that the local skin targeted in the embodiment of the present invention is facial skin, so the local skin area to be tested is the entire facial area; in other embodiments, the implementer may also set the local skin to be tested to a local skin area of ​​other parts such as the back. The analysis and processing methods for all local skin areas are the same, and only the skin quality detection process of the facial skin is briefly described here.

[0022] Considering that the oil secretion of the skin is one of the main factors that determine skin quality, vigorous oil secretion of the skin usually leads to enlarged and clogged pores, and even causes skin inflammation such as acne; considering that the skin surface temperature of pores and acne inflammation is usually slightly higher than the normal healthy epidermal temperature due to factors such as vigorous secretion of local sebaceous glands or inflammatory infection; therefore, the embodiment of the present invention first obtains a thermal map of the local skin area to be tested based on the surface temperature difference characteristics of different skins, which facilitates the subsequent determination of skin defects such as pores and acne, and evaluates the local oil distribution of the local skin area to be tested, so as to assist in skin quality evaluation.

[0023] In one embodiment of the present invention, a professional skin detector is used to obtain a thermal map of the local skin area to be tested. Since this embodiment is to evaluate facial skin quality, relevant preparations need to be made before collection, such as keeping the skin surface clear and closing the eyes. The thermal map does not directly display skin defects such as pores and acne, but the pixel values ​​therein reflect the temperature information of the local skin area corresponding to each pixel point. The higher the pixel value, the higher the temperature, so that skin defects such as pores and acne can be indirectly determined.

[0024] It should be noted that obtaining a thermal map of a local skin area to be tested is already a prior art, and implementers may also use other methods to collect thermal maps, which will not be described in detail here.

[0025] Considering that histograms can help analyze the distribution status of data, thermal histograms can help analyze and evaluate the thermal distribution more clearly, so as to facilitate the subsequent screening of thermal intervals corresponding to blemished skin areas such as pores and acne; therefore, in one embodiment of the present invention, a thermal histogram of the thermal map is further obtained, specifically using the thermal value as the horizontal axis parameter and the frequency of occurrence of the thermal value as the vertical axis parameter, and dividing the range of the thermal value into a preset number such as 20 thermal intervals, and obtaining the frequency of occurrence of the thermal value in each thermal interval, that is, the frequency, thereby constructing a thermal histogram.

[0026] See also Figure 2 , which shows a thermal histogram provided by an embodiment of the present invention, wherein each column width corresponds to a thermal interval, and the column height corresponds to the frequency of thermal values ​​in the thermal interval. It should be noted that the construction of a thermal histogram is a prior art well known to those skilled in the art and will not be described in detail here; in other embodiments, the implementer may also set a preset number of thermal intervals or adaptively construct a thermal histogram.

[0027] Considering that under normal circumstances, the normal and healthy skin area in the local skin area to be tested should always be larger than the defective skin area such as pores and acne; and considering that the columns in the thermal histogram reflect the frequency information of the thermal values, and indirectly reflect the size of the thermal value area; based on this, all thermal intervals of suspected defective skin can be preliminarily screened out from all thermal intervals of the thermal histogram, in preparation for the subsequent screening of skin defect areas such as pores and acne.

[0028] Preferably, in one embodiment of the present invention, the method for obtaining the thermal interval of suspected defective skin includes: In the thermal histogram, the product of the normalized result of the frequency corresponding to each thermal interval and the negative correlation mapping result of the thermal value corresponding to the right end point of each thermal interval is used as the confidence coefficient of each thermal interval being the normal skin temperature; the normal skin thermal interval is selected from all thermal intervals according to the confidence coefficient; The thermal distribution curve is fitted based on the thermal histogram, and the thermal interval corresponding to each maximum point in the thermal distribution curve in the thermal histogram is taken as the suspected interval; in the thermal histogram, all the suspected intervals located to the right of the normal skin thermal interval are taken as the suspected defective skin thermal intervals.

[0029] It should be noted that, in this example, the thermal histogram uses the thermal value as the horizontal axis parameter, and the right side of the normal skin thermal interval refers to the thermal interval that is larger than the normal skin thermal interval. The same applies to the right side mentioned later in this embodiment and will not be repeated.

[0030] As an example, the confidence coefficient is calculated as: ; i is the serial number of the thermal interval in the thermal histogram; is the confidence coefficient that the ith thermal interval is normal skin temperature; is the frequency corresponding to the ith thermal interval; Y is the total number of thermal values ​​in the thermal map, which is also the sum of the frequencies of all thermal intervals; is the thermal value corresponding to the right endpoint of the i-th thermal interval.

[0031] In the above formula, the frequency of each thermal interval is divided by the total frequency for normalization, and then the right end point of each thermal interval is negatively correlated with the corresponding thermal value by taking the inverse, so that the thermal interval with a larger frequency normalization value and a relatively lower thermal value has a larger confidence coefficient, and the corresponding skin area is more likely to be a normal skin area; After obtaining the confidence coefficient, this example further uses the thermal interval with the largest confidence coefficient in the thermal histogram as the normal skin thermal interval; then fits the thermal distribution curve corresponding to the thermal histogram, that is, curves the histogram; Figure 2 , Figure 2 The curve in is the thermal distribution curve, which reflects the distribution information of thermal values ​​in the same way as the thermal histogram. Then, all the maximum values ​​in the thermal distribution curve are obtained. The maximum values ​​reflect the mutation of the local thermal distribution. Therefore, the thermal interval corresponding to the maximum value is initially taken as the suspected interval. Then, combined with the characteristics that the surface temperature of defective skin such as pores and acne is usually higher than the surface temperature of normal healthy skin, all the suspected intervals on the right side of the normal skin thermal interval are taken as the suspected defective skin thermal interval.

[0032] It should be noted that, in other examples, implementers may also adopt other normalization methods such as linear normalization, or negative correlation mapping to an exponential function, which are all existing technologies and will not be elaborated here; implementers may also select a preset number of intervals with a larger confidence coefficient, such as the first three consecutive thermal intervals as normal skin thermal intervals, or directly analyze all thermal intervals to the right of the normal thermal interval as suspected intervals.

[0033] Step S2, according to the distribution and aggregation of pixel points in the heat map under each suspected blemish skin thermal interval, obtain the screening coefficient of each suspected blemish skin thermal interval as the pore thermal interval; according to the screening coefficient, filter out the pore thermal interval from all suspected blemish skin thermal intervals, and obtain all pore areas in the heat map; according to the pore thermal interval, and the distribution information of pixel points in the heat map under the other suspected blemish skin thermal intervals, obtain all acne areas in the heat map.

[0034] Taking into account that the pores in a local area are usually distributed more evenly and the degree of pore expansion is relatively similar, the embodiment of the present invention will obtain the screening coefficient of each suspected defective skin thermal interval as a pore thermal interval according to the distribution and aggregation of pixel points in the heat map under each suspected defective skin thermal interval. The screening coefficient reflects the possibility that the pixel points in the heat map under each suspected defective skin thermal interval are the pixel points corresponding to the pores, thereby facilitating the subsequent screening of the pore thermal intervals, and further facilitating the acquisition of all pore areas in the heat map.

[0035] Preferably, in one embodiment of the present invention, the method for obtaining the screening coefficient includes: Taking any suspected blemish skin thermal interval as the target interval, perform regional connectivity detection on all pixels corresponding to the target interval in the thermal map, and regard each connected domain as a suspected pore; construct a preset neighborhood with each suspected pore as the center; In the preset neighborhood of each suspected pore, according to the uniformity of the spatial distribution of the suspected pores and the area similarity between different suspected pores, the screening sub-parameters whose target interval is the pore thermal interval are obtained; The preset neighborhood corresponding filter parameters of all suspected pores are integrated to obtain the screening coefficient whose target interval is the pore thermal interval.

[0036] As an example, in the thermal map, after obtaining all suspected pores under the target interval, a circular preset neighborhood with a radius of 50 is constructed with each suspected pore as the center, and then in the preset neighborhood of each suspected pore, the screening sub-parameters of the target interval being the pore thermal interval are analyzed and obtained; finally, the cumulative value of the screening sub-parameters analyzed and obtained in the preset neighborhood of all suspected pores is used as the screening coefficient of the target interval being the pore thermal interval; by changing the target interval, the screening coefficient of each suspected defective skin thermal interval being the pore thermal interval can be obtained; It should be noted that in other examples, the implementer may also set the neighborhood radius according to actual conditions, or directly construct a K-neighborhood, that is, taking a local area containing K suspected pores as a preset neighborhood; regional connectivity detection is already an existing technology and will not be described in detail.

[0037] Among them, in a preferred embodiment of the present invention, the method for obtaining the screening sub-parameters includes: In the preset neighborhood of each suspected pore, the suspected pore corresponding to the center of the preset neighborhood is taken as the target pore, and the remaining suspected pores are taken as reference pores; the deviations of the distances between each reference pore and the target pore relative to the average level are accumulated and negatively correlated, and the negative correlation mapping result is taken as the pore uniform distribution parameter; the negative correlation mapping result of the accumulated sum of the area differences between each reference pore and the target pore is taken as the pore area similarity parameter; the pore uniform distribution parameter and the pore area similarity parameter are integrated to obtain the screening sub-parameter whose target interval is the pore thermal interval; Taking any preset neighborhood of suspected pores in the target interval as an example, the calculation formula of the filter parameter is: ; Wherein, k is the symbol of the reference pore; v is the symbol of the target pore; is the preset neighborhood of the target pore, the target interval is the screening sub-parameter of the pore thermal interval; K is the total number of reference pores; is the Euclidean distance between the kth reference pore and the target pore; is the average Euclidean distance between all reference pores and the target pore; is a very small positive parameter. In this example, it is set to 0.001 to prevent the denominator from being 0. is the pore uniform distribution parameter; is the area of ​​the kth reference pore; is the area of ​​the target pore; is the similarity parameter of pore area; In the above formula, the absolute value of the difference is specifically used to evaluate the deviation of the distance between each reference pore and the target pore relative to the average level. The smaller the deviation, the more similar the distance between each reference pore and the target pore is, which to a certain extent reflects the more uniform the distribution of the suspected pores in the preset neighborhood. Then, the cumulative sum of the deviations is added to the minimum positive parameter and then the inverse operation is performed to adjust the logic, so that the smaller the cumulative sum of the deviations, the larger the pore uniform distribution parameter; at the same time, the absolute value of the difference is used to evaluate the area difference between the reference pore and the target pore, and then the cumulative value of the area difference is added to the minimum positive parameter and then the inverse operation is performed to adjust the logic, so that the smaller the cumulative value of the area difference, the larger the pore area similarity parameter; finally, the pore uniform distribution parameter and the pore area similarity parameter are multiplied and fused to obtain the screening sub-parameter whose target interval is the pore thermal interval; It should be noted that the area of ​​suspected pores can be evaluated using the total number of pixels therein; in other examples, implementers may also adopt other negative correlation mapping methods, such as using the cumulative sum as the x in the exponential function exp(-x) with the natural constant e as the base, or combining the pore uniform distribution parameters and the pore area similarity parameters through basic mathematical methods such as addition or weighted fusion. These are all commonly used technical methods and will not be elaborated on here.

[0038] In another embodiment of the present invention, the implementer may also evaluate the pore uniform distribution parameters based on the centroid method, firstly obtain the centroid and center of the preset neighborhood, calculate the Euclidean distance between the center and the corresponding position of the centroid, further perform negative correlation mapping on the Euclidean distance such as adding a minimum positive parameter and then performing an inverse operation, use the negative correlation mapping result as the pore uniform distribution parameter, further merge the pore uniform distribution parameter and the pore area similarity parameter to obtain the screening sub-parameter.

[0039] After obtaining the screening coefficient, the pore thermal interval can be screened out from all the suspected blemish skin thermal intervals according to the screening coefficient, and all the pore areas in the thermal map can be further obtained.

[0040] Preferably, in one embodiment of the present invention, the thermal interval with the largest screening coefficient is used as the pore thermal interval; regional connectivity detection is performed on all pixel points corresponding to the pore thermal interval in the thermal map, and each connected domain is used as a pore area, thereby obtaining all pore areas in the thermal map.

[0041] Considering that the surface temperature of the skin corresponding to acne is usually higher than the surface temperature of the skin corresponding to the pore area, and considering that the area corresponding to acne in the heat map is also relatively large; therefore, the embodiment of the present invention will obtain all acne areas in the heat map based on the distribution information of pixels in the heat map under the pore thermal interval and other suspected defective skin thermal intervals.

[0042] Preferably, in one embodiment of the present invention, considering that the skin surface temperature corresponding to acne is relatively higher than the skin surface temperature corresponding to pores, all suspected intervals located on the right side of the pores are taken as suspected acne thermal intervals; and considering that the skin temperature corresponding to acne is not only higher but also relatively larger in area, the distribution information of the pixel points in the heat map under each suspected acne thermal interval is further analyzed and evaluated to obtain the acne area; the method for obtaining the acne area includes: In the thermal histogram, all suspected intervals located to the right of the pore thermal interval are regarded as suspected acne thermal intervals; in each suspected acne thermal interval, all corresponding pixel points in the thermal map are subjected to regional connectivity detection, and each connected domain is regarded as a suspected acne; The suspected acne thermal interval corresponding to the maximum total area of ​​suspected acne is taken as the acne thermal interval; all suspected acnes corresponding to the acne thermal interval are taken as all acne areas in the heat map.

[0043] At this point, all pore areas and all acne areas in the thermal map of the local skin area to be tested are obtained.

[0044] Step S3, dividing the local skin area to be tested according to the position distribution and area information of all pore areas, and obtaining all skin partitions; obtaining the water loss rate in each skin partition; according to the water loss rate in each skin partition, combined with the number of pore areas and the number of acne areas in the corresponding area of ​​each skin partition in the thermal map, obtaining the oiliness coefficient of each skin partition in the local skin area to be tested.

[0045] Considering that the pores in a local skin area are usually distributed more evenly and have similar pore expansion degrees, while the pores in different local areas may be different, for example, the pores in a skin area with strong oil secretion are relatively large and are more likely to have oily skin, while the skin area with smaller pores is more likely to have dry skin; therefore, the embodiment of the present invention further divides the local skin area to be tested according to the position distribution and area information of all pore areas, obtains all skin partitions, and facilitates subsequent analysis and evaluation of the skin oiliness of each skin partition, preparing for subsequent skin quality evaluation.

[0046] Preferably, in one embodiment of the present invention, considering that clustering can cluster similar features into one cluster, the pore areas are clustered based on the area characteristics of the pore areas in the local skin area to be measured and the distribution positions in the heat map, so that the skin areas that may be of similar skin quality are divided into a skin partition; the method for obtaining the skin partition includes: Based on the area difference and spatial distance between pore areas, the metric distance between different pore areas is obtained; based on the distance clustering algorithm and the metric distance, all pore areas are clustered to obtain all clusters; the corresponding area of ​​each cluster in the heat map is taken as a partition area, and the corresponding area of ​​each partition area in the heat map in the local skin area to be tested is taken as a skin partition.

[0047] As an example, an image coordinate system is constructed with the center of the heat map as the coordinate origin, and the position coordinates of the center corresponding to each pore area are obtained. Then, between any two different pore areas, the absolute value of the area difference is used as the first parameter, and the Euclidean distance between the position coordinates is used as the second parameter. The first parameter and the second parameter are then fused to obtain the Euclidean norm between different pore areas, and the Euclidean norm is used as the metric distance between the corresponding different pore areas; then, all pore areas in the heat map are distanced based on a preset K value and a K-means algorithm, wherein the preset K value is obtained based on the elbow method; then, the local area corresponding to each cluster in the heat map is taken as a partition area, and then the skin partition corresponding to each partition area in the local skin area to be tested can be obtained through mask mapping.

[0048] It should be noted that the K-means algorithm, the elbow method and the mask mapping are all existing technologies; in other examples, the implementer may also use other clustering algorithms such as the DBSCAN algorithm to cluster all pore areas, which will not be described in detail.

[0049] It should be noted that the skin partitioning based on the clustering of relevant features of the pore area may not cover all local skin areas to be tested, so other skin areas that can participate in the clustering are regarded as one skin partition as a whole.

[0050] Considering the water loss on the skin surface can also help evaluate the skin quality. For example, oily skin has strong oil secretion on the skin surface, which can form an oil film protective layer to help lock in moisture, while dry skin is more prone to dryness and tightness, and its surface moisture is more easily lost. Therefore, in one embodiment of the present invention, a related transepidermal water loss meter is further used to obtain the water loss rate at the center of each skin partition, in preparation for the subsequent analysis of the oiliness coefficient of each skin partition.

[0051] In another embodiment of the present invention, the implementer may also directly divide the skin area to be tested into a preset number, such as 50 skin blocks, before dividing the skin area to be tested, and use a transepidermal water loss meter to obtain the water loss rate of each skin block. After obtaining all skin areas, the implementer may use the average water loss rate of all skin blocks in the skin area as the water loss rate of the skin area.

[0052] It should be noted that obtaining the water loss rate in each skin zone is an existing technology well known to those skilled in the art, and implementers may also use other relevant measurement methods to obtain it, which will not be described in detail.

[0053] After obtaining the water loss rate at each skin zone, we can further combine the number of pore areas and the number of acne areas in the corresponding area of ​​each skin zone in the thermal map to obtain the oiliness coefficient of each skin zone in the local skin area to be tested, in preparation for the subsequent evaluation of skin quality.

[0054] Preferably, in one embodiment of the present invention, considering that the more pores there are in each skin zone and the larger the area is, the slower the water loss rate is, and the more acne is present, the greater the possibility of vigorous oil secretion in the skin zone is, and the corresponding oiliness coefficient is also greater; therefore, the method for obtaining the oiliness coefficient includes: In the corresponding area of ​​each skin partition in the heat map, the total area of ​​all pore areas is used as the first oiliness parameter, and the total number of all acne areas plus a preset normal number is used as the second oiliness parameter; the negative correlation mapping result of the water loss rate of each skin partition is used as the third oiliness parameter; the first oiliness parameter, the second oiliness parameter and the third oiliness parameter are integrated to obtain the oiliness coefficient of the corresponding skin partition.

[0055] As an example, the oiliness coefficient is calculated as: ; Where u is the serial number of the skin partition; is the oiliness coefficient of the u-th skin partition; is the total area of ​​the pores in the corresponding area of ​​the u-th skin partition in the heat map, which is also the first oiliness parameter; is the total number of acne areas in the corresponding area of ​​the u-th skin partition in the heat map; a is a preset normal number, which is 1 in this example to avoid the calculation result being meaningless due to the number of acne areas being 0; is the second oiliness parameter of the u-th skin partition; is the water loss rate of the u-th skin partition; is the third oiliness parameter of the u-th skin partition; is a linear normalization function.

[0056] In the above formula, since the water loss rate of the skin partition cannot be 0, the water loss rate of the skin partition is specifically reciprocally calculated to adjust the logic with negative correlation mapping, so that the smaller the water loss rate, the larger the corresponding oiliness coefficient; finally, the first oiliness parameter, the second oiliness parameter and the third oiliness parameter are multiplied and normalized, so that the larger each parameter is, the larger the oiliness coefficient is.

[0057] In other examples, implementers may also use other negative correlation mapping methods, or use basic mathematical operations such as addition or weighted summation to combine the three parameters. All of these are known in the prior art and will not be described in detail here.

[0058] In one embodiment of the present invention, after obtaining the oiliness coefficient of each skin partition in the local skin area to be tested, the implementer can assist relevant testers in evaluating the skin quality of the local skin area to be tested based on the specific manifestations of oily skin, dry skin and mixed skin; for example, the oiliness coefficient of the skin partition corresponding to the T zone in the facial area corresponding to oily skin is large, and the oiliness coefficients of the other skin partitions are also relatively large, but slightly lower than the T zone; the oiliness coefficient corresponding to the entire facial area of ​​dry skin will be smaller; the oiliness coefficient of the skin partition corresponding to the T zone of mixed skin is large, but the oiliness coefficients of the other skin partitions will be relatively small; based on this feature and other test results of the local skin area to be tested, relevant testers can be assisted in evaluating skin quality.

[0059] It should be noted that the embodiments of the present invention only provide a reference for skin quality analysis and do not directly diagnose and evaluate skin quality.

[0060] In one embodiment of the present invention, after obtaining the skin type of the local skin area to be tested, the implementer can further assign relevant skin quality labels to the heat map of the local skin area to be tested, thereby obtaining a large number of annotated heat maps, and dividing all the annotated heat maps into training set, validation set and test set in preset proportions of 60%, 20%, and 20%; then select EfficientNet as the convolutional neural network architecture, select the cross entropy loss function as the loss function, and select the Adam optimizer to update the weights and biases of the model, so as to train the neural network model, and then input the newly acquired skin heat map into the trained neural network model, the model can directly output reference information for skin quality auxiliary detection, and improve the efficiency of skin quality auxiliary detection.

[0061] It should be noted that the training and application of convolutional neural network models are existing technologies and will not be described in detail.

[0062] The present invention also proposes an auxiliary detection system for local skin quality, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of an auxiliary detection method for local skin quality are implemented.

[0063] In summary, the present invention first obtains the thermal histogram of the local skin area to be tested, and all the thermal intervals of suspected defective skin therein; further, according to the distribution and aggregation of pixels in the thermal map under each suspected defective skin thermal interval, all pore areas and all acne areas in the thermal map are determined; then, according to the position distribution and area information of all pore areas, the local skin area to be tested is divided, and the oiliness coefficient of each skin partition is further evaluated in combination with the water loss rate and the distribution of pore acne in each skin partition. The present invention combines the characteristics of oily skin with vigorous oil secretion and prone to skin defects such as pore acne, and the characteristics that the defective epidermal temperature is slightly higher than the normal healthy epidermal temperature, quantitatively analyzes the oiliness coefficient of the local skin, and improves the skin quality detection effect of the local skin.

[0064] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0065] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. An auxiliary detection method for local skin quality, characterized in that: The method comprises: Obtaining a thermal histogram of a thermal map of a local skin area to be tested, and screening out all thermal intervals of suspected defective skin from all thermal intervals of the thermal histogram; According to the distribution and aggregation of pixel points in the heat map under each suspected blemish skin thermal interval, the screening coefficient of each suspected blemish skin thermal interval as a pore thermal interval is obtained; according to the screening coefficient, the pore thermal interval is screened out from all suspected blemish skin thermal intervals, and all pore areas in the heat map are obtained; according to the pore thermal interval and the distribution information of pixel points in the heat map under the other suspected blemish skin thermal intervals, all acne areas in the heat map are obtained; According to the position distribution and area information of all the pore areas, the local skin area to be tested is divided to obtain all skin partitions; the water loss rate in each skin partition is obtained; according to the water loss rate in each skin partition, combined with the number of the pore areas and the number of the acne areas in the corresponding area of ​​each skin partition in the thermal map, the oiliness coefficient of each skin partition in the local skin area to be tested is obtained.

2. The auxiliary detection method of local skin quality according to claim 1, characterized in that: The method for obtaining the thermal interval of the suspected defective skin includes: In the thermal histogram, the product of the normalized result of the frequency corresponding to each thermal interval and the negative correlation mapping result of the thermal value corresponding to the right end point of each thermal interval is used as the confidence coefficient of each thermal interval being a normal skin temperature; and the normal skin thermal interval is selected from all thermal intervals according to the confidence coefficient; A thermal distribution curve is fitted based on the thermal histogram, and the thermal interval corresponding to each maximum point in the thermal distribution curve in the thermal histogram is taken as a suspected interval; in the thermal histogram, all suspected intervals located to the right of the normal skin thermal interval are taken as suspected defective skin thermal intervals.

3. The auxiliary detection method of local skin quality according to claim 2, characterized in that: The method for obtaining the normal skin thermal range includes: The thermal interval with the largest confidence coefficient in the thermal histogram is taken as the normal skin thermal interval.

4. The auxiliary detection method of local skin quality according to claim 1, characterized in that: The method for obtaining the screening coefficient includes: Taking any suspected blemish skin thermal interval as the target interval, performing regional connectivity detection on all pixel points corresponding to the target interval in the thermal map, and taking each connected domain as a suspected pore; constructing a preset neighborhood with each of the suspected pores as the center; In a preset neighborhood of each of the suspected pores, according to the uniformity of the spatial distribution of the suspected pores and the area similarity between different suspected pores, a screening sub-parameter whose target interval is the pore thermal interval is obtained; The preset neighborhoods of all the suspected pores correspond to the screening sub-parameters to obtain the screening coefficient whose target interval is the pore thermal interval.

5. The auxiliary detection method of local skin quality according to claim 4, characterized in that: The method for obtaining the screening sub-parameters includes: In a preset neighborhood of each of the suspected pores, the center of the preset neighborhood corresponding to the suspected pore is used as the target pore, and the remaining suspected pores are used as reference pores; The deviations of the distances between each reference pore and the target pore relative to the average level are accumulated and negatively correlated, and the negative correlation mapping result is used as the pore uniform distribution parameter; the negative correlation mapping result of the accumulated sum of the area differences between each reference pore and the target pore is used as the pore area similarity parameter; The pore uniform distribution parameter and the pore area similarity parameter are integrated to obtain a screening sub-parameter whose target interval is the pore thermal interval.

6. The auxiliary detection method of local skin quality according to claim 1, characterized in that: The method for obtaining the pore thermal interval and the pore area includes: The thermal interval with the largest screening coefficient is taken as the pore thermal interval; the pore thermal interval in the thermal map is subjected to regional connectivity detection corresponding to all pixel points, and each connected domain is taken as a pore area.

7. The auxiliary detection method of local skin quality according to claim 2, characterized in that: The method for obtaining the acne area comprises: In the thermal histogram, all suspected intervals located to the right of the pore thermal interval are regarded as suspected acne thermal intervals; in each suspected acne thermal interval, all corresponding pixel points in the thermal map are subjected to regional connectivity detection, and each connected domain is regarded as a suspected acne; The suspected acne thermal interval corresponding to the maximum total area of ​​the suspected acne is used as the acne thermal interval; all the suspected acnes corresponding to the acne thermal interval are used as all acne areas in the heat map.

8. The auxiliary detection method of local skin quality according to claim 1, characterized in that: The method for obtaining skin partitions comprises: Based on the area difference and spatial distance between the pore areas, the metric distance between different pore areas is obtained; based on the distance clustering algorithm and the metric distance, all the pore areas are clustered to obtain all clusters; the corresponding area of ​​each cluster in the heat map is taken as a divided area, and the corresponding area of ​​each divided area in the heat map in the local skin area to be measured is taken as a skin partition.

9. The auxiliary detection method of local skin quality according to claim 1, characterized in that: The method for obtaining the oiliness coefficient includes: In the area corresponding to each skin partition in the heat map, the total area of ​​all the pore areas is used as the first oiliness parameter, and the total number of all the acne areas plus a preset normal number is used as the second oiliness parameter; the negative correlation mapping result of the water loss rate of each skin partition is used as the third oiliness parameter; The first oiliness parameter, the second oiliness parameter and the third oiliness parameter are integrated to obtain the oiliness coefficient of the corresponding skin partition.

10. An auxiliary detection system for local skin quality, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the auxiliary detection method for local skin quality as described in any one of claims 1 to 9 are implemented.

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