Skin texture information analyzing and processing method and device

Abstract

The present invention relates to a skin texture information analyzing and processing method and device. The device comprises a housing, and a main control unit, a storage unit, a light-emitting unit, a camera unit, a sensor and a scoring unit which are mounted in the housing. The method comprises: acquiring a white-light image of a skin texture region, performing image processing on the white-light image to obtain skin color information, and comparing the skin color information with a preset standard to obtain a skin color score, the skin color score comprising a whitening score and a wrinkle score; acquiring an ultraviolet image of the skin texture region, and processing the ultraviolet image to obtain a pore score, a spot score, and a blackhead score; acquiring a moisture score of the skin texture region; and comparing the skin color score, the moisture score, the pore score, the spot score, and the blackhead score with corresponding preset standards according to a preset priority scheme to obtain a comprehensive score. In the present invention, one's skin texture can be accurately known and determined, thereby facilitating the selection of an appropriate skin-care product and skin-care scheme.

Description

Method and device for analyzing and processing skin information TECHNICAL FIELD

[0001] The present application relates to the field of analyzing and processing skin information, and more particularly, to a method and device for analyzing and processing skin information. BACKGROUND

[0002] With the development of economy, people's demand for beauty and health is increasing, and more and more attention is paid to skin care. Skin care products are increasingly concerned. There are more and more skin care products or skin care programs on the market. Different skin care products and skin care programs are often suitable for different skin. However, most people do not have the ability to understand their own skin, so it is difficult to choose the right skin care products and skin care programs. Therefore, a solution is urgently needed. TECHNICAL PROBLEM

[0003] The purpose of the present application is to provide a technical solution for analyzing and processing skin information to help users understand their own skin. TECHNICAL SOLUTION

[0004] In one aspect, the present application provides a method for analyzing and processing skin information, comprising the following steps:

[0005] Obtaining a white light image of a skin area, performing image processing on the white light image to obtain skin color information, comparing the skin color information with a preset standard to obtain a skin color score;

[0006] Obtaining an ultraviolet image of the skin area, processing the ultraviolet image to obtain a pore score, a color spot score and a blackhead score, wherein:

[0007] The step of obtaining the pore score includes: sequentially performing BGR transformation, noise removal and binaryzation processing on the ultraviolet image to obtain pore information; comparing the pore information with a preset standard to obtain a pore score;

[0008] The step of obtaining the color spot score includes: sequentially performing HSV transformation, noise removal, color decomposition, and binaryzation processing based on a specific color component after color decomposition to obtain color spot information; comparing the color spot information with a preset standard to obtain a color spot score;

[0009] The step of obtaining the blackhead score includes: sequentially performing HSV transformation, noise removal, color decomposition, and binaryzation processing based on a specific color component after color decomposition to obtain blackhead information; comparing the blackhead information with a preset standard to obtain a blackhead score;

[0010] The skin color score, the pore score, the spot score and the blackhead score are compared with preset standards according to a preset priority scheme to obtain a comprehensive score.

[0011] As a preferred scheme of the present application, the step of obtaining the pore score further comprises: after the binarization processing, performing contraction and expansion processing on the image to identify the number of pores.

[0012] As a preferred scheme of the present application, the step of obtaining the spot score further comprises: after the binarization processing, performing contraction and expansion processing on the image to identify the number of spots and the area of spots.

[0013] As a preferred scheme of the present application, the step of obtaining the blackhead score further comprises: after the binarization processing, performing contraction and expansion processing on the image to identify the number of blackheads.

[0014] As a preferred scheme of the present application, the step of obtaining the skin color score comprises a step of obtaining a whitening score, wherein the step of obtaining the whitening score comprises: taking the product of the total number of pixels in the white light image and a preset parameter as a first parameter, taking the sum of the product of each pixel and its brightness in the white light image as a second parameter, and calculating the whitening score according to the ratio of the second parameter to the first parameter.

[0015] As a preferred scheme of the present application, the step of obtaining the skin color score comprises a step of obtaining a wrinkle score, wherein the step of obtaining the wrinkle score comprises: identifying the white light image to obtain the number of skin papillae and the number of skin furrows, and calculating the wrinkle score according to the number of skin papillae and the number of skin furrows.

[0016] As a preferred scheme of the present application, the method of analyzing and processing the skin quality information further comprises a step of obtaining a moisture score: collecting moisture information of the skin quality area through a sensor, comparing the moisture information with a preset standard to obtain a moisture score; and the step of obtaining a comprehensive score comprises: comparing the moisture score, the skin color score, the pore score, the spot score and the blackhead score with preset standards according to a preset priority scheme to obtain a comprehensive score.

[0017] As a preferred scheme of the present application, in the step of obtaining a comprehensive score, the skin color score or the spot score is taken as a first priority.

[0018] On the other hand, the present application provides a device for analyzing and processing skin quality information, comprising a housing, a master control unit, a storage unit, a light emitting unit, a camera unit and a scoring unit installed in the housing, wherein:

[0019] The storage unit stores preset standards and a preset priority scheme;

[0020] The light emitting unit is configured to emit white light and ultraviolet light to the skin area;

[0021] The camera unit is configured to acquire a white light image and an ultraviolet image of the skin area;

[0022] The main control unit is configured to perform image processing on the white light image to obtain skin color information, and perform processing on the ultraviolet image to obtain pore information, color spot information and blackhead information; the processing to obtain the pore information comprises sequentially performing BGR conversion, noise removal and binaryzation processing on the ultraviolet image; the processing to obtain the color spot information comprises sequentially performing HSV conversion, noise removal, color decomposition and binaryzation processing based on a specific color component after the color decomposition on the ultraviolet image; the processing to obtain the blackhead information comprises sequentially performing HSV conversion, noise removal, color decomposition and binaryzation processing based on a specific color component after the color decomposition on the ultraviolet image;

[0023] The scoring unit is configured to compare the skin color information with a preset standard to obtain a skin color score, compare the pore information with a preset standard to obtain a pore score, compare the color spot information with a preset standard to obtain a color spot score, compare the color spot information with a preset standard to obtain a color spot score, and compare the blackhead information with a preset standard to obtain a blackhead score; and compare the skin color score, the pore score, the color spot score and the blackhead score with a preset standard according to a preset priority scheme to obtain a comprehensive score.

[0024] As a preferred scheme of the present application, a sensor is further included, the sensor is connected with the main control unit and is configured to collect moisture information of the skin area, the scoring unit further compares the moisture information with a preset standard to obtain a moisture score, and compares the moisture score, the skin color score, the pore score, the color spot score and the blackhead score with a preset standard according to a preset priority scheme to obtain a comprehensive score. Advantages

[0025] By implementing the present application, a user can more accurately understand and judge his / her skin quality, so as to select appropriate skin care products and skin care schemes. BRIEF DESCRIPTION OF DRAWINGS

[0026] To further disclose the technical contents of the present application, first, please refer to the drawings, in which:

[0027] Fig. 1 is a perspective view of a device for analyzing and processing skin quality information according to an embodiment of the present application;

[0028] Fig. 2 is an exploded view of the device for analyzing and processing skin quality information shown in Fig. 1;

[0029] Fig. 3 is a schematic block diagram of functional modules of the device for analyzing skin information shown in Fig. 1;

[0030] Fig. 4 is a flow chart of a method for analyzing skin information of the device for analyzing skin information shown in Fig. 1;

[0031] Fig. 5 is a detailed schematic diagram of step S120 of Fig. 4;

[0032] Figs. 6(a), 6(b) and 6(c) are schematic diagrams of binaryzation processing, shrinkage processing and expansion processing of an image, respectively. Embodiments of the present application

[0033] The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings.

[0034] Referring to Figs. 1 and 2, the shell of the device 100 for analyzing skin information provided by an embodiment of the present application is formed by clamping half shells 11 and 13. The shell formed by clamping the half shells 11 and 13 forms a hand-held portion 17 and a working head 19 at one end of the hand-held portion 17. A middle shell 15 is arranged in the hand-held portion 17, and the middle shell 15 is used to mount a main board 21, which is provided with a chip 23 and a battery 25. Preferably, the battery 25 is a rechargeable battery.

[0035] Referring to Figs. 1 to 3, from the perspective of functional modules, the main board 21 and the chip 23 form a main control unit 31, a storage unit 37 and a scoring unit 61 in communication connection with the main control unit 31; and one end of the working head 19 is further provided with a light-emitting unit 33, a sensor 53 and a camera unit 35, which are all in communication connection with the main control unit 31. Among them:

[0036] The storage unit 37 stores preset standards and preset priority schemes;

[0037] The scoring unit 61 scores related parameters of the skin quality based on the preset standards and comprehensively scores the skin quality based on the preset priority schemes;

[0038] The light-emitting unit 33 is used to emit white light and ultraviolet light to the skin area;

[0039] The camera unit 35 is used to acquire white light images and ultraviolet images of the skin area;

[0040] The main control unit 31 is configured to perform image processing on the white light image to obtain skin color information, and perform processing on the ultraviolet image to obtain pore information, spot information and blackhead information; the processing to obtain the pore information comprises sequentially performing BGR conversion, noise removal and binarization processing on the ultraviolet image; the processing to obtain the spot information comprises sequentially performing HSV conversion, noise removal, color decomposition and binarization processing based on a specific color component after the color decomposition on the ultraviolet image; the processing to obtain the blackhead information comprises sequentially performing HSV conversion, noise removal, color decomposition and binarization processing based on a specific color component after the color decomposition on the ultraviolet image.

[0041] The scoring unit 61 is configured to compare the skin color information with a preset standard to obtain a skin color score, compare the pore information with a preset standard to obtain a pore score, compare the spot information with a preset standard to obtain a spot score, compare the spot information with a preset standard to obtain a spot score, and compare the blackhead information with a preset standard to obtain a blackhead score; and compare the skin color score, the pore score, the spot score and the blackhead score with a preset standard according to a preset priority scheme to obtain a comprehensive score.

[0042] In use, a user holds the handheld part and starts the device 100. The light emitting unit 33 emits white light to the skin quality area under the control of the main control unit 31, and then the camera unit 35 obtains a white light image of the skin quality area under the control of the main control unit 31; then, the light emitting unit 33 emits ultraviolet light to the skin quality area under the control of the main control unit 31, and then the camera unit 35 obtains an ultraviolet image of the skin quality area under the control of the main control unit 31. Then, the main control unit 31 and the scoring unit 61 of the device 100 process the white light image and the ultraviolet image. The analysis and processing procedure of the skin quality information is shown in FIG. 4:

[0043] In step S110, a white light image of the skin quality area is obtained, image processing is performed on the white light image to obtain skin color information, and the skin color information is compared with a preset standard to obtain a skin color score.

[0044] In step S120, an ultraviolet image of the skin quality area is obtained, and processing is performed on the ultraviolet image to obtain a pore score, a spot score and a blackhead score.

[0045] In step S130, the skin color score, the pore score, the spot score and the blackhead score are compared with a preset standard according to a preset priority scheme to obtain a comprehensive score.

[0046] It should be noted that the execution order of the aforementioned step S110 and step S120 can be exchanged.

[0047] In step S120, the ultraviolet image is processed to obtain the pore score, the spot score and the blackhead score.

[0048] In step S121, an ultraviolet image of the skin quality area is acquired;

[0049] In step S123, the ultraviolet image is sequentially subjected to BGR conversion, noise removal, and binarization processing to acquire pore information; the pore information is compared with a preset standard to acquire a pore score; wherein the BGR conversion refers to converting the color space of the image into BGR (B for blue, G for green, and R for red). The noise removal, also referred to as noise reduction processing, is to make the signal-to-noise ratio of the image not less than 38, so as to improve the accuracy of pore judgment. The binarization processing refers to converting the color of the image into only black and white, so as to further improve the accuracy of recognition.

[0050] In step S125, the ultraviolet image is sequentially subjected to HSV conversion, noise removal, color decomposition, and binarization processing based on a specific color component after the color decomposition to acquire a color spot information; the color spot information is compared with a preset standard to acquire a color spot score; wherein the HSV conversion refers to performing HSV value conversion on each pixel of the image, wherein H refers to hue, S refers to saturation, and V refers to value. The noise removal, also referred to as noise reduction processing, is to make the signal-to-noise ratio of the image not less than 41 in this embodiment, so as to improve the accuracy of color spot judgment. The color decomposition refers to decomposing the color into its component colors, for example, into a blue component (B), a green component (G), and a red component (R), so as to facilitate recognition and processing under a certain color component. In this embodiment, the green component is used for subsequent image processing. The binarization processing refers to converting the color of the image of the green component into only black and white, so as to further improve the accuracy of recognition.

[0051] In step S127, the ultraviolet image is sequentially subjected to HSV conversion, noise removal, color decomposition, and binarization processing based on a specific color component after the color decomposition to acquire a blackhead information; the blackhead information is compared with a preset standard to acquire a blackhead score; wherein the HSV conversion refers to performing HSV value conversion on each pixel of the image, wherein H refers to hue, S refers to saturation, and V refers to value. The noise removal, also referred to as noise reduction processing, is to make the signal-to-noise ratio of the image not less than 41 in this embodiment, so as to improve the accuracy of color spot judgment. The color decomposition refers to decomposing the color into its component colors, for example, into a blue component (B), a green component (G), and a red component (R), so as to facilitate recognition and processing under a certain color component. In this embodiment, the green component is used for subsequent image processing. The binarization processing refers to converting the color of the image of the green component into only black and white, so as to further improve the accuracy of recognition.

[0052] It should be noted that the order of the foregoing steps S123, S125 and S127 can be changed; and the processing procedure of the ultraviolet image in each step has a sequence.

[0053] In a preferred embodiment, in step S123, the step of obtaining the pore score further comprises: after the binarization processing, performing the shrinkage and expansion processing of the image to identify the number of pores. After the binarization processing of the image, the black-and-white image obtained can have many burrs and small dots, as shown in (6a) of FIG. 6. Therefore, after the binarization processing of the image, the present application first performs the shrinkage processing of the image to remove the burrs and small dots to leave the expected content, as shown in (6b) of FIG. 6. Then, the expansion processing of the image is performed to restore the remaining content to the expected size, and the processing result is shown in (6c) of FIG. 6. In this embodiment, the convolution kernel adopted in the shrinkage processing of the image is 30, and the convolution kernel adopted in the expansion processing of the image is 25. After the binarization processing, shrinkage and expansion processing of the image, the true circularity of the color blocks in the image is calculated, and the color blocks with an area of 50-80 pixels and a true circularity not lower than 0.30 are identified as pores, so that the number of pores can be automatically identified. Further, the pores can be classified according to the area size, the number of pores falling into each size level is multiplied by the corresponding coefficient, and then the relevant products are summed to serve as an important basis for the pore score. The preset standard of the pore score can be based on the number of pores or the value of the foregoing relevant sum.

[0054] In a preferred embodiment, in step S125, the step of obtaining the color spot score further comprises: after the binarization processing, performing the shrinkage and expansion processing of the image to identify the number and area of color spots. After the binarization processing of the image, the black-and-white image obtained can have many burrs and small dots. Therefore, after the binarization processing of the image, the present application first performs the shrinkage processing of the image to remove the burrs and small dots to leave the expected content. Then, the expansion processing of the image is performed to restore the remaining content to the expected size. In this embodiment, the convolution kernel adopted in the shrinkage processing of the image is 10, and the convolution kernel adopted in the expansion processing of the image is 10. After the binarization processing, shrinkage and expansion processing of the image, the true circularity of the color blocks in the image is calculated, and the color blocks with an area and true circularity meeting the preset conditions are identified as color spots, for example, the color spots with an area in the range of 1000-2000 pixels and a true circularity not lower than 0.20 and not greater than 0.93 are identified as color spots, so that the number and area of color spots can be automatically identified. Further, the total area ratio of the color spots can be used as an important basis for the color spot score. For example, the preset standard of the color spot score can be based on the total area ratio of the color spots.

[0055] In a preferred embodiment, in step S127, the step of obtaining the blackhead score further comprises: after the binarization processing, performing shrinkage and expansion processing on the image to identify the number and area of the color patches. After the binarization processing of the image, the black-and-white image obtained can have many burrs and small dots. Therefore, the application first performs shrinkage processing on the image after the binarization processing of the image to remove the burrs and small dots to leave the expected content. Then, expansion processing is performed on the image to restore the remaining content to the expected size. In this embodiment, a convolution kernel of 10 is adopted in the shrinkage processing of the image, and a convolution kernel of 7 is adopted in the expansion processing of the image. After the binarization processing, shrinkage and expansion processing of the image, the true circularity of the color patches in the image is calculated, and the color patches whose area, true circularity and brightness meet the preset conditions are identified as blackheads, for example, the color patches whose area is in the range of 80-1000 pixels, whose true circularity is not less than 0.40 and not greater than 0.9, and whose brightness is not greater than 140 are identified as blackheads, so that the number of blackheads can be automatically identified. Further, the number of blackheads can also be used as an important basis for the blackhead score. For example, the preset standard of the blackhead score can be divided according to the number of blackheads. In this embodiment, the step of obtaining the skin color score includes the step of obtaining the whitening score and the step of obtaining the wrinkle score. The step of obtaining the whitening score includes: taking the product of the total number of pixels in the white light image and a preset parameter as a first parameter, taking the sum of the product of each pixel and its brightness in the white light image as a second parameter, and calculating the whitening score according to the ratio of the second parameter to the first parameter. The step of obtaining the skin color score includes the step of obtaining the wrinkle score, which includes: identifying the white light image to obtain the number of skin ridges and the number of skin furrows, and calculating the wrinkle score according to the number of skin ridges and the number of skin furrows.

[0056] In this embodiment, the method of analyzing and processing the skin quality information further includes the step of obtaining a moisture score: collecting moisture information of the skin quality area through the sensor, and comparing the moisture information with the preset standard to obtain the moisture score. Referring to FIG. 4, the step of obtaining the moisture score should be before step S130. Accordingly, in step S130, the step of obtaining the comprehensive score includes: comparing the moisture score, the skin color score, the pore score, the color patch score and the blackhead score with the preset standard according to the preset priority scheme to obtain the comprehensive score.

[0057] In an alternative embodiment, in the step of obtaining the comprehensive score, the skin color score or the color patch score is taken as the first priority.

[0058] The above-described embodiments are merely illustrative of several embodiments of the present application, which are described in more detail and in a specific manner, but should not be construed as limiting the scope of the patent of the present application. It should be noted that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method of analyzing skin condition information, characterized by, The method comprises the following steps: acquiring a white light image of a skin area, processing the white light image to obtain skin color information, comparing the skin color information with a preset standard to obtain a skin color score; acquiring an ultraviolet image of the skin area, processing the ultraviolet image to obtain a pore score, a color spot score and a blackhead score, wherein: the step of acquiring the pore score comprises sequentially performing BGR conversion, noise removal and binarization processing on the ultraviolet image to obtain pore information; and comparing the pore information with a preset standard to obtain the pore score; the step of acquiring the color spot score comprises sequentially performing HSV conversion, noise removal, color decomposition and binarization processing based on a specific color component after color decomposition on the ultraviolet image to obtain color spot information; and comparing the color spot information with a preset standard to obtain the color spot score; the step of acquiring the blackhead score comprises sequentially performing HSV conversion, noise removal, color decomposition and binarization processing based on a specific color component after color decomposition on the ultraviolet image to obtain blackhead information; and comparing the blackhead information with a preset standard to obtain the blackhead score; comparing the skin color score, the pore score, the color spot score and the blackhead score with a preset standard according to a preset priority scheme to obtain a comprehensive score; the step of acquiring the pore score further comprises, after the binarization processing, performing contraction and expansion processing on the image to identify the number of pores; the step of acquiring the color spot score further comprises, after the binarization processing, performing contraction and expansion processing on the image to identify the number and area of color spots; the step of acquiring the blackhead score further comprises, after the binarization processing, performing contraction and expansion processing on the image to identify the number of blackheads; the contraction and expansion processing on the image comprises removing burrs and small dots and then restoring the remaining content to an expected size.

2. The method of claim 1, wherein the skin quality information is analyzed by the skin quality analysis server. the step of acquiring the skin color score comprises a step of acquiring a whitening score, wherein the step of acquiring the whitening score comprises taking the product of the total number of pixels in the white light image and a preset parameter as a first parameter, taking the sum of the product of each pixel and its brightness in the white light image as a second parameter, and calculating the whitening score according to the ratio of the second parameter to the first parameter.

3. The method of claim 1, wherein the skin quality information is analyzed by a skin quality analysis program. the step of acquiring the skin color score comprises a step of acquiring a wrinkle score, wherein the step of acquiring the wrinkle score comprises identifying the white light image, acquiring the number of skin ridges and the number of skin furrows, and calculating the wrinkle score according to the number of skin ridges and the number of skin furrows.

4. The method for analyzing and processing skin information according to claim 1, wherein: the method for analyzing and processing skin information further comprises a step of acquiring a moisture score: collecting moisture information of the skin area through a sensor, and comparing the moisture information with a preset standard to obtain a moisture score; the step of acquiring the comprehensive score comprises comparing the moisture score, the skin color score, the pore score, the color spot score and the blackhead score with a preset standard according to a preset priority scheme to obtain the comprehensive score.

5. The method of claim 1, wherein the skin quality information is analyzed by using a skin quality analysis program. in the step of acquiring the comprehensive score, the skin color score or the color spot score is taken as a first priority.

6. A device for analyzing skin condition information, comprising a housing, characterized in that The device further comprises a master control unit, a storage unit, a light emitting unit, a camera unit and a scoring unit installed in the housing, wherein: The storage unit stores preset standards and preset priority schemes; The light emitting unit is configured to emit white light and ultraviolet light to the skin area; The camera unit is configured to acquire white light images and ultraviolet images of the skin area; The master control unit is configured to perform image processing on the white light images to obtain skin color information, and perform processing on the ultraviolet images to obtain pore information, spot information and blackhead information; the processing to obtain pore information comprises sequentially performing BGR conversion, noise removal and binaryzation processing on the ultraviolet images; the processing to obtain spot information comprises sequentially performing HSV conversion, noise removal, color decomposition and binaryzation processing based on a specific color component after color decomposition on the ultraviolet images; the processing to obtain blackhead information comprises sequentially performing HSV conversion, noise removal, color decomposition and binaryzation processing based on a specific color component after color decomposition on the ultraviolet images; The processing to obtain pore information further comprises, after the binaryzation processing, performing contraction and expansion processing on the images to identify the number of pores; the processing to obtain spot information further comprises, after the binaryzation processing, performing contraction and expansion processing on the images to identify the number and area of spots; the processing to obtain blackhead information further comprises, after the binaryzation processing, performing contraction and expansion processing on the images to identify the number of blackheads; The contraction and expansion processing on the images comprises removing burrs and small dots, and then restoring the remaining contents to the expected size; The scoring unit is configured to compare the skin color information with the preset standards to obtain a skin color score, compare the pore information with the preset standards to obtain a pore score, compare the spot information with the preset standards to obtain a spot score, compare the spot information with the preset standards to obtain a spot score, and compare the blackhead information with the preset standards to obtain a blackhead score; and compare the skin color score, pore score, spot score and blackhead score with the preset standards according to the preset priority schemes to obtain a comprehensive score.

7. The device for analyzing skin information according to claim 6, wherein The device further comprises a sensor connected to the master control unit for collecting moisture information of the skin area; the scoring unit further compares the moisture information with the preset standards to obtain a moisture score, and compares the moisture score, the skin color score, the pore score, the spot score and the blackhead score with the preset standards according to the preset priority schemes to obtain a comprehensive score.

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