WO2022173056A1 - 肌状態推定方法、装置、プログラム、システム、学習済みモデル生成方法、および学習済みモデル - Google Patents

肌状態推定方法、装置、プログラム、システム、学習済みモデル生成方法、および学習済みモデル Download PDF

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Publication number
WO2022173056A1
WO2022173056A1 PCT/JP2022/005909 JP2022005909W WO2022173056A1 WO 2022173056 A1 WO2022173056 A1 WO 2022173056A1 JP 2022005909 W JP2022005909 W JP 2022005909W WO 2022173056 A1 WO2022173056 A1 WO 2022173056A1
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WIPO (PCT)
Prior art keywords
nose
skin condition
user
features
skin
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Ceased
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PCT/JP2022/005909
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English (en)
French (fr)
Japanese (ja)
Inventor
典子 長谷川
祐輔 原
拓馬 星野
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Shiseido Co Ltd
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Shiseido Co Ltd
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Priority to US18/262,620 priority Critical patent/US20240074694A1/en
Priority to JP2022580719A priority patent/JP7779487B2/ja
Priority to CN202280010218.8A priority patent/CN116801800A/zh
Publication of WO2022173056A1 publication Critical patent/WO2022173056A1/ja
Anticipated expiration legal-status Critical
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A61B5/0077—Devices for viewing the surface of the body, e.g. camera, magnifying lens
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/44—Detecting, measuring or recording for evaluating the integumentary system, e.g. skin, hair or nails
    • A61B5/441—Skin evaluation, e.g. for skin disorder diagnosis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/44—Detecting, measuring or recording for evaluating the integumentary system, e.g. skin, hair or nails
    • A61B5/441—Skin evaluation, e.g. for skin disorder diagnosis
    • A61B5/442—Evaluating skin mechanical properties, e.g. elasticity, hardness, texture, wrinkle assessment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/1032—Determining colour of tissue for diagnostic purposes

Definitions

  • the present invention relates to a skin condition estimation method, device, program, system, learned model generation method, and learned model.
  • Patent Literature 1 predicts future wrinkle formation and wrinkle levels around the eyes and mouth from ultrasound images.
  • Patent Document 1 an ultrasonic diagnostic device is required, and it was not easy to easily predict skin conditions that are likely to occur in the future.
  • an object of the present invention is to easily obtain the skin condition.
  • a method includes identifying features of a user's nose and estimating the skin condition of the user based on the features of the user's nose.
  • the skin condition can be easily estimated from the characteristics of the nose.
  • FIG. 4 is a flow chart showing the flow of skin condition estimation processing according to an embodiment of the present invention.
  • FIG. 4 is a diagram for explaining features of a nose according to one embodiment of the present invention.
  • FIG. 4 is a diagram for explaining extraction of a nose region according to one embodiment of the present invention;
  • FIG. 4 is a diagram for explaining extraction of a nose region according to one embodiment of the present invention.
  • 4 is a diagram for explaining calculation of a nose feature amount according to one embodiment of the present invention.
  • 4 is an example of nose features for each face type according to one embodiment of the present invention;
  • 4 is an example of a face estimated from nose features according to an embodiment of the present invention; It is a figure which shows the hardware constitutions of the skin condition estimation apparatus which concerns on one Embodiment of this invention.
  • the “skin condition” is at least one of wrinkles, spots, sagging, dark circles, nasolabial folds, dullness, firmness, moisture, sebum, melanin, blood circulation, blood vessels, blood, pores, and skin color.
  • skin condition includes wrinkles, spots, sagging, dark circles, nasolabial folds, dullness, firmness, moisture, sebum, melanin, blood circulation, blood vessels, blood, pores, and skin color. It is the presence/absence and degree of elements.
  • the “skin condition” is the condition of the skin in any one of a part of the face, the entire face, and a plurality of parts of the face.
  • the “skin condition” may be the future skin condition of the user or the current skin condition of the user. In the present invention, the skin condition is estimated from the nose characteristics based on the correlation between the nose characteristics and the skin condition.
  • FIG. 1 is a diagram showing the overall configuration according to one embodiment of the present invention.
  • the skin condition estimation device 10 estimates the skin condition of the user 20 from the features of the user's 20 nose.
  • the skin condition estimation device 10 is a smart phone or the like having a camera function.
  • the skin condition estimation device 10 will be described in detail later with reference to FIG.
  • the skin condition estimation device 10 is one device (for example, a smartphone having a camera function, etc.) will be described. device and a digital camera). Further, the camera function may be a function of photographing the skin three-dimensionally or a function of photographing the skin two-dimensionally. Also, a device (such as a server) other than the skin condition estimation device 10 may execute a part of the processing executed by the skin condition estimation device 10 described herein.
  • FIG. 2 is a diagram showing functional blocks of the skin condition estimation device 10 according to one embodiment of the present invention.
  • the skin condition estimation device 10 can include an image acquisition unit 101 , a nose feature identification unit 102 , a skin condition estimation unit 103 , a skeleton estimation unit 104 and an output unit 105 . Further, the skin condition estimation apparatus 10 can function as an image acquisition unit 101, a nose feature identification unit 102, a skin condition estimation unit 103, a skeleton estimation unit 104, and an output unit 105 by executing programs. Each of these will be described below.
  • the image acquisition unit 101 acquires an image including the user's 20 nose.
  • the image including the nose may be an image in which the nose and other parts are photographed (for example, an image in which the entire face is photographed), or an image in which only the nose is photographed (for example, a display of the skin condition estimation device 10).
  • An image photographed so that the nose area of the user 20 fits within a predetermined area displayed on the device) may also be used.
  • the image acquisition unit 101 is not required if the features of the nose are identified from other than the image.
  • the nose feature identification unit 102 identifies the features of the user's 20 nose. For example, the nose feature identification unit 102 identifies the features of the nose of the user 20 from the image information (for example, pixel values of the image) of the image including the nose of the user 20 acquired by the image acquisition unit 101 .
  • image information for example, pixel values of the image
  • the skin condition estimation unit 103 estimates the skin condition of the user 20 based on the nose features of the user 20 identified by the nose feature identification unit 102 . For example, the skin condition estimation unit 103 classifies the skin condition of the user 20 based on the features of the nose of the user 20 specified by the nose feature specifying unit 102 .
  • the skin condition estimating unit 103 estimates the skin condition of the user 20 (for example, the skin condition caused by the shape of the facial skeleton) based on the shape of the facial skeleton of the user 20 estimated by the skeleton estimating unit 104. ) can also be estimated.
  • the skeleton estimation unit 104 estimates the shape of the facial skeleton of the user 20 based on the features of the nose of the user 20 specified by the nose feature specifying unit 102 . For example, the skeleton estimation unit 104 classifies the shape of the facial skeleton of the user 20 based on the features of the nose of the user 20 specified by the nose feature specifying unit 102 .
  • the output unit 105 outputs (for example, displays) information on the skin condition of the user 20 estimated by the skin condition estimation unit 103 .
  • the skin condition is at least one of wrinkles, spots, sagging, dark circles, nasolabial folds, dullness, firmness, moisture, sebum, melanin, blood circulation, blood vessels, blood, texture, pores, and skin color.
  • the skin condition includes wrinkles at the corners of the eyes, wrinkles under the eyes, wrinkles on the forehead, wrinkles on the eye holes, sagging eye bags, dark circles in the eyes, nasolabial folds (nasolabial folds, around the mouth), and nose.
  • the skin condition estimation unit 103 estimates the skin condition from the nose characteristics based on the correlation between the nose characteristics and the skin condition.
  • the skin condition estimating unit 103 estimates the skin condition based on the correspondence relationship between the nose features and the skin condition stored in advance in the skin condition estimating device 10 or the like. It should be noted that the skin condition may be estimated based not only on the features of the nose, but also on the features of the nose and part of the features of the face.
  • the correspondence may be a pre-determined database or a machine-learned model.
  • the characteristics of the nose (which may be a part of the characteristics of the nose and facial features) and the skin condition are associated with each other based on the results of experiments conducted on subjects.
  • the trained model is a prediction model that outputs information on skin condition when information on nose features (which may be part of nose features and facial features) is input.
  • a computer such as skin condition estimation device 10 can generate a trained model.
  • a computer such as the skin condition estimating apparatus 10 obtains teacher data whose input data is the nose feature (which may be part of the nose feature and facial features) and whose output data is the skin condition. Then, machine learning is performed using the teacher data to generate a trained model that outputs the skin condition when the nose features (the nose features and part of the facial features are acceptable) are input. can. In this way, machine learning is performed using training data in which the input data is nose features (which may be nose features and part of facial features) and the output data is skin conditions. nose features and some facial features) are input, a trained model is generated that outputs the skin condition.
  • the skin condition estimating unit 103 may also estimate the skin condition based on the correspondence relationship between the shape of the facial skeleton and the skin condition stored in advance in the skin condition estimating device 10 or the like. can.
  • the correspondence may be a pre-determined database or a machine-learned model.
  • the database associates the shape of the facial skeleton with the skin condition based on the results of experiments conducted on the subject.
  • the learned model is a prediction model that outputs skin condition information when shape information relating to the skeleton of the face is input.
  • a computer such as skin condition estimation device 10 can generate a trained model.
  • a computer such as the skin condition estimating apparatus 10 acquires teacher data whose input data is a shape related to the skeleton of the face and whose output data is the condition of the skin, and performs machine learning using the teacher data. , it is possible to generate a trained model that outputs the skin condition when the shape of the facial skeleton is input. In this way, machine learning is performed using training data in which the input data is the shape related to the facial skeleton and the output data is the skin condition, so that when the shape related to the facial skeleton is input, the skin condition is output.
  • a trained model is generated.
  • the estimated skin condition may be the future skin condition of the user 20 or the current skin condition of the user 20 .
  • the correspondence between the nose features (or the shape of the facial skeleton estimated from the nose features) and the skin condition is created based on the data of a person older than the actual age of the user 20 ( For example, if the age of the test subject in the experiment or the age of the person whose learning data is used for machine learning is higher than the actual age of the user 20, the future skin of the user 20 is estimated.
  • the correspondence relationship between the features of the nose (or the shape of the facial skeleton estimated from the features of the nose) and the skin condition is created based on the data of a person of the same age as the real age of the user 20.
  • the current skin of the user 20 is estimated. be.
  • the skin condition may be estimated based not only on the features of the nose, but also on the features of the nose and part of the features of the face.
  • the skin condition estimating unit 103 can estimate that wrinkles are likely to appear at the corners of the eyes when the root of the nose and the bridge of the nose are high. Further, for example, the skin condition estimation unit 103 estimates that if the shape of the cheek is such that the high position of the cheekbone is at the top, there is a wrinkle at the corner of the eye or there is a possibility that it will become wrinkled in the future (turn ON/OFF judgment) can be made.
  • the skin condition estimating unit 103 can estimate that wrinkles are more likely to occur under the eyes when the wings of the nose are more rounded or, for example, when the eyes are larger.
  • the skin condition estimating unit 103 can estimate that the eye socket has a shape characteristic such as being horizontally long or small, but that the eye socket is large and the vertical and horizontal widths are similar, and there are many wrinkles under the eyes. Also, for example, the skin condition estimation unit 103 can estimate wrinkles under the eyes based on the facial contour. Also, for example, the skin condition estimation unit 103 can estimate that the wider the distance between the eyes, the less wrinkles under the eyes.
  • the skin condition estimation unit 103 can estimate sagging eye bags based on the roundness of the nasal alar and the height of the bridge of the nose. Specifically, the skin condition estimation unit 103 can estimate that the larger the sum of the roundness of the nasal alar and the height of the bridge of the nose, the looser the eye bags.
  • the skin condition estimation unit 103 can estimate that the eye bags are likely to sag when the face contour is oval and the face is long.
  • the skin condition estimation unit 103 can estimate HbCO2 (reduced hemoglobin) based on the height of the bridge of the nose and the roundness of the alar.
  • the skin condition estimation unit 103 can estimate HbSO2 (oxygen saturation) based on the facial contour.
  • the skin condition estimating unit 103 can estimate that the water content is lower as the bridge of the nose is lower, the wings of the nose are rounder, or the distance between the eyes is greater.
  • the skin condition estimation unit 103 can estimate the skin moisture content based on the height of the skull index and the aspect ratio of the face.
  • the skin condition estimation unit 103 can estimate sebum based on the roundness of the nasal alar.
  • the skin condition estimation unit 103 can estimate sebum based on the facial contour.
  • the skin condition estimating unit 103 can estimate that the melanin index is higher when the nasal alar is rounder and the nasal bridge is higher, the melanin amount is higher, and the melanin index is lower when the nasal bridge is lower and the distance between the eyes is narrower.
  • the skin condition estimation unit 103 can estimate that the thicker the upper and lower lips, the higher the melanin index and the greater the amount of melanin. Also, for example, the skin condition estimation unit 103 can estimate that the thinner the upper and lower lips are, the lower the melanin index is.
  • the skin condition estimating unit 103 can estimate that dark circles are likely to appear when the alar of the nose is rounded.
  • the skin condition estimation unit 103 can estimate that the face line tends to sag when the bridge of the nose is low and the distance between the eyes is wide, or when the angle of the chin is rounded.
  • the skin condition estimation unit 103 can estimate that the higher the bridge of the nose, the higher the oxygen content in the blood.
  • the skin condition estimating unit 103 can estimate the blood vessel density from the position of change in the size of the nasal alar or the height of the root of the nose, and the larger the nasal alar, the higher the blood vessel density.
  • the skin condition estimation unit 103 can estimate the epidermal thickness from the size of the nasal alar.
  • the skin condition estimating unit 103 can estimate the number of blood vessels branching from the position of change in the height of the root of the nose.
  • the skin condition estimating unit 103 comprehensively determines wrinkles, spots, sagging, dark circles, nasolabial folds, dullness, firmness, moisture
  • Skin conditions can be expressed as sebum, melanin, blood circulation, blood vessels, blood, pores, and skin color.
  • - Wrinkles represented by one or more items of wrinkles at the corners of the eyes, under the eyes, forehead, and eyeholes.
  • Blemishes represented by one or more items of uneven brown color, uneven red color, and melanin.
  • Sagging represented by one or more items of eye bags, chin, and marionette lines.
  • ⁇ Bears One or two items of brown eyes and blue eyes.
  • - Nasolabial folds represented by one or two items of nasolabial folds nasolabial folds and mouth nasolabial folds.
  • Dullness represented by one or more items of transparency, melanin, color unevenness, skin color, oxygen saturation, water content, number of skin bumps.
  • Firmness represented by one or more of moisture, sebum, sagging, and skin viscoelasticity.
  • Moisture represented by one or two items of water content, water retention capacity (TEWL), number of skin bumps, and pH.
  • TEWL water retention capacity
  • ⁇ Texture represented by one or more items such as the number of skin mounds and water content.
  • ⁇ Skin color expressed by one or more of the following items: skin tone, skin brightness, melanin, blood oxygen content, and HbO2 (oxygenated hemoglobin content).
  • - Sebum expressed from one or two items of sebum amount and pores.
  • normal skin, dry skin, oily skin, and mixed skin may be classified based on moisture and sebum.
  • Melanin Melanin index, amount of melanin, uneven color, one or two items.
  • Blood circulation expressed by at least one or two items of HbSO2 Index (blood oxygen saturation index), Hb Index (hemoglobin amount), HbO2 (oxygenated hemoglobin amount), blood oxygen amount, and skin color.
  • Blood vessel represented by one or more items of density of blood vessels, number of capillaries, number of blood vessel branches, distance between blood vessels and epidermis, and epidermis thickness.
  • ⁇ Blood HDL cholesterol
  • the skin condition estimating unit 103 can express skin characteristics such as skin strength and skin weakness from the characteristics of the nose. For example, when the characteristic of the nose is type 1, the evaluation value of wrinkles at the corners of the eyes is lower than the average evaluation value, so it is represented as the strength of the skin. When the characteristic of the nose is type 2, the evaluation value of wrinkles at the corners of the eyes is higher than the average evaluation value, so it is expressed as weak skin. The strengths and weaknesses of the skin can be expressed for each part of the face.
  • the strengths of the skin are wrinkles and blemishes on the corners of the eyes and forehead, and the weaknesses of the skin are dark circles, nasolabial folds, nasolabial folds, sagging around the mouth, and water retention.
  • the skin condition estimating unit 103 can estimate a general skin index (in this case, loose skin) from these skin conditions.
  • the strengths of the skin are sagging cheeks, moisture retention, blood circulation, and blemishes, and the weaknesses are wrinkles and blemishes on the corners of the eyes and forehead.
  • the skin condition estimating unit 103 can estimate a comprehensive skin index (in this case, wrinkle-type skin) from these skin conditions.
  • the skeleton estimating unit 104 estimates the shape of the facial skeleton from the features of the nose based on the correlation between the features of the nose and the shape of the facial skeleton.
  • the shapes related to the skeleton of the face include orbits, cheekbones, nasal bones, piriform mouth (mouth that opens toward the face of the nasal cavity), cranial index, maxilla, mandible, lips, corners of the mouth, Eyes, Mongolian folds (folds of the skin where the upper eyelids cover the inner corners of the eyes), facial contours, and the positional relationship between the eyes and eyebrows (for example, the eyes and eyebrows are far apart, close, etc.) At least one of the characteristics of the shape of each bone, the positional relationship of the skeleton, the angle, and the like.
  • An example of the shape related to the skeleton of the face is shown below. Note that the contents in parentheses are an example of estimated specific contents.
  • the skeleton estimation unit 104 estimates the shape of the facial skeleton based on the correspondence relationship between the features of the nose and the shape of the facial skeleton stored in advance in the skin condition estimating device 10 or the like. Note that the shape of the facial skeleton may be estimated based on not only the features of the nose but also the features of the nose and part of the features of the face.
  • the correspondence may be a pre-determined database or a machine-learned model. Based on the results of experiments conducted on subjects, the database associates features of the nose (which may be part of the features of the nose and facial features) with shapes related to the skeleton of the face. . Also, the trained model is a prediction model that outputs shape information related to the skeleton of the face when nose feature information (which may be part of the nose feature and facial features) is input. Note that the correspondence between the features of the nose and the shape of the facial skeleton may be created for each group classified based on factors that can affect the skeleton (for example, Caucasoid, Mongoloid, Negroid, Australoid, etc.). .
  • a computer such as skin condition estimation device 10 can generate a trained model.
  • a computer such as the skin condition estimating apparatus 10 receives teacher data whose input data is the features of the nose (which may be part of the features of the nose and facial features) and whose output data is the shape related to the skeleton of the face. is obtained, and machine learning is performed using the teacher data to generate a trained model that outputs a shape related to the skeleton of the face when the nose feature (a part of the nose feature and facial features) is input. can be generated.
  • machine learning is performed using training data in which the input data is the features of the nose (the features of the nose and part of the features of the face may be used), and the output data is the shape of the facial skeleton.
  • a trained model is generated that outputs a shape related to the skeleton of the face when the features (which may be the features of the nose and part of the facial features) are input.
  • the skeleton estimating unit 104 can estimate the skull index based on the height or lowness of the nasal root or the position of change in the height of the nasal root, and the height or lowness of the bridge of the nose. Specifically, the skeleton estimation unit 104 estimates that the higher the nasal root and/or the nasal bridge, the lower the skull index.
  • the skeleton estimation unit 104 can estimate whether the corners of the mouth are raised or lowered based on the width of the bridge of the nose. Specifically, the skeleton estimation unit 104 estimates that the wider the bridge of the nose, the lower the corners of the mouth.
  • the skeleton estimating unit 104 determines the size and thickness of the lips (1. Large and thick upper and lower lips, 2. Thick lower lip, 3. Thin small) can be estimated.
  • the skeleton estimation unit 104 can estimate the presence or absence of Mongolian folds based on the root of the nose. Specifically, the skeleton estimation unit 104 estimates that there is a Mongolian fold when it is determined that the root of the nose is low.
  • the skeleton estimation unit 104 classifies the shape of the mandible (for example, classifies into three) based on the height or height of the bridge of the nose, the height of the root of the nose, and the roundness and size of the alar. can do.
  • the skeleton estimation unit 104 can estimate the pyriform mouth based on the height of the bridge of the nose.
  • the skeleton estimation unit 104 can estimate the inter-eye distance based on the height of the bridge of the nose. Specifically, the skeleton estimation unit 104 estimates that the lower the bridge of the nose, the wider the distance between the eyes.
  • the skeleton estimation unit 104 can estimate the roundness of the forehead based on the height of the root of the nose and the height of the bridge of the nose.
  • the skeleton estimation unit 104 can estimate the distance between the eyes and the eyebrows and the shape of the eyebrows based on the height and depth of the bridge of the nose, the size of the wings of the nose, and the position of change in the height of the root of the nose.
  • FIG. 3 is a flowchart showing the flow of skin condition estimation processing according to one embodiment of the present invention.
  • step 1 (S1) the nose feature identification unit 102 extracts feature points (for example, feature points of the inner corners of the eyebrows, the inner corners of the eyes, and the tip of the nose) from the image including the nose.
  • feature points for example, feature points of the inner corners of the eyebrows, the inner corners of the eyes, and the tip of the nose
  • step 2 (S2) the nose feature identification unit 102 extracts the nose region based on the feature points extracted in S1.
  • the image including the nose is an image in which only the nose is captured (for example, an image in which the nose region of the user 20 is captured within a predetermined region displayed on the display device of the skin condition estimation device 10). , the image in which only the nose is captured is used as it is (that is, S1 can be omitted).
  • step 3 the nose feature identification unit 102 reduces the number of gradations of the image of the nose region extracted in S2 (eg, binarizes).
  • the nose feature identifying unit 102 uses at least one of brightness, luminance, RGB Blue, and RGB Green to reduce the number of gradations of the image of the nose region. Note that S3 may be omitted.
  • the nasal feature identifying unit 102 identifies the nasal features (nasal skeleton). Specifically, the nose feature identification unit 102 calculates the feature amount of the nose based on the image information (for example, pixel values of the image) of the image of the nose region. For example, the nose feature identification unit 102 calculates the average value of pixel values in the nose region, the number of pixels equal to or greater than a predetermined value, the cumulative pixel value, the amount of change in pixel values, etc., as the feature amount of the nose.
  • the image information for example, pixel values of the image
  • the nose feature identification unit 102 calculates the average value of pixel values in the nose region, the number of pixels equal to or greater than a predetermined value, the cumulative pixel value, the amount of change in pixel values, etc., as the feature amount of the nose.
  • the skeleton estimation unit 104 estimates the shape of the facial skeleton. Note that S5 may be omitted.
  • step 6 (S6) the skin condition estimating unit 103 determines the skin condition (for example, future skin condition) based on the features of the nose identified in S4 (or the shape related to the facial skeleton estimated in S5). trouble).
  • the skin condition for example, future skin condition
  • the nasal feature is at least one of a nasal root, a nasal bridge, a nasal tip, and an alar.
  • FIG. 4 is a diagram for explaining features of the nose according to one embodiment of the present invention.
  • FIG. 4 shows the positions of the root of the nose, the bridge of the nose, the tip of the nose, and the wings of the nose.
  • the nasal root is the part at the base of the nose.
  • the features of the nose are at least one of the height of the nasal root, the low nasal root, the width of the nasal root, and the changing position of the nasal root where the nasal root changes to be higher.
  • the bridge of the nose is the part between the eyebrows and the tip of the nose.
  • the nasal feature is at least one of a nasal bridge height, a nasal bridge low, and a nasal bridge width.
  • the nasal tip is the tip of the nose (nose tip).
  • the nasal characteristic is at least one of the roundness or kurtosis of the nasal tip and the orientation of the nasal tip.
  • the wings of the nose are the swollen areas on either side of the head of the nose.
  • the nasal characteristic is at least one of the roundness or kurtosis of the alar and the size of the alar.
  • FIG. 5 is a diagram for explaining extraction of a nose region according to one embodiment of the present invention.
  • a nose feature identification unit 102 extracts a nose region in an image including the nose.
  • the nose region may be the entire nose as shown in FIG. 5(a), or a portion of the nose (for example, the right half or the left half) as shown in FIG. 5(b).
  • FIG. 6 is a diagram for explaining calculation of a nose feature amount according to one embodiment of the present invention.
  • step 11 (S11) the nose region in the image including the nose is extracted.
  • step 12 the number of gradations of the image of the nose region extracted in S11 is reduced (binarized, for example). Note that S12 may be omitted.
  • the feature amount of the nose is calculated.
  • the pixel cumulative value is expressed with 0 on the high brightness side of the image and 255 on the low brightness side.
  • the nose feature identification unit 102 performs normalization for each of multiple regions (for example, the divided regions in S12).
  • the nose feature identifying unit 102 determines the average pixel value, the number of pixels equal to or greater than a predetermined value, the X direction
  • At least one pixel cumulative value in the and Y directions, the amount of change in the pixel value in at least one of the X and Y directions, and the like are calculated as feature amounts of the nose.
  • the pixel cumulative value in the X direction at each position in the Y direction is calculated.
  • the feature amount of the nasal root is the feature amount of the upper (closer to the eye) area among the divided areas of S12
  • the feature amount of the nose bridge is the upper or central part of the divided area of S12.
  • the feature amounts of the nasal tip and alar are the feature amounts of the lower (closer to the mouth) area among the divided areas of S12.
  • Height of root of nose The height and height are determined from the amount of change in pixel values in the Y direction in the upper region of the nose. The height or lowness may be calculated as a numerical value, or may be classified as high or low. As for the position of change in the height of the nose root, it can be seen that the value of nose 2 immediately changes in the Y direction in S13, and the position of change in the height of the nose root is in the upper part.
  • Width of nose root The area above the nose is divided into a plurality of areas (2 to 4, etc.) in the X direction, and the width is determined from the pattern of the average pixel values of each area.
  • ⁇ Height of nose bridge The height and height are determined from the average value of the accumulated pixel values in the central region of the nose. The height or lowness may be calculated as a numerical value, or may be classified as high or low.
  • Width of nose bridge The area in the center of the nose is divided into a plurality of areas (2 to 4, etc.) in the X direction, and the width is determined from the pattern of the average pixel values of each area.
  • Nose tip roundness or kurtosis Determined from other nasal features (height of the nose bridge, roundness or kurtosis of the alar), the lower the bridge and the rounder the alar, the more rounded.
  • ⁇ Direction of nose tip In the region of the central part of the nose, it is obtained from the width from the lowest point of the nose at a predetermined ratio to the maximum value of the pixel cumulative value in the X direction. .
  • Alar roundness or kurtosis Roundness or kurtosis is determined from the amount of change in Y-direction value in the lower nose region.
  • Alar size Determined from the ratio of the number of pixels below a predetermined value in the central portion of the lower region. The larger the number of pixels, the larger the nostrils.
  • shape related to the skeleton of the face refers to at least one of “the shape of the facial skeleton itself” and “the shape of the face resulting from the skeleton”.
  • shape related to facial skeleton can include face type.
  • the user's face is classified into a plurality of face types (specifically, "the shape of the facial skeleton itself” and “the shape of the face resulting from the skeleton") based on the features of the user's nose. It is possible to estimate which of the face types is classified based on at least one of them.
  • the face type will be described below with reference to FIGS. 7 and 8. FIG.
  • FIG. 7 is an example of nose features for each face type according to one embodiment of the present invention.
  • FIG. 7 shows the features of the nose of each face type (face types A to L).
  • face type may be estimated using all (four) of the nasal bridge, the alar, the nasal root, and the nasal tip, or partly (for example, two of the nasal bridge and the nasal alar, two of the nasal bridge and the nasal tip).
  • face type may be estimated using only the bridge of the nose, only the alar of the nose, etc.).
  • the face type is estimated from the features of the nose.
  • the roundness of the eyes round
  • the inclination of the eyes downward
  • the size of the eyes small
  • the shape of the eyebrows arched
  • the position of the eyebrows and eyes apart
  • the shape of the face Contour Estimated to be ROUND.
  • the roundness of the eyes sharp
  • the inclination of the eyes considerably raised
  • the size of the eyes large
  • the shape of the eyebrows sharp
  • the position of the eyebrows and the eyes fairly close
  • the face CONTOURS Estimated RECTANGLE.
  • FIG. 8 is an example of a face estimated from nose features according to one embodiment of the present invention.
  • based on the features of the user's nose it is possible to infer which face type the user's face is among the various face types shown in FIG. .
  • face types classified based on nose features can be used to guide makeup or present skin characteristics (e.g., what facial features a face type has, what facial features It can present makeup guides and skin characteristics based on what kind of impression you have or your face type).
  • the skin condition can be easily estimated from the characteristics of the nose.
  • the future skin condition is estimated from the characteristics of the nose, and cosmetics that can more effectively suppress future skin troubles are selected, and beauty treatments such as massage are determined. be able to.
  • FIG. 9 is a diagram showing the hardware configuration of the skin condition estimation device 10 according to one embodiment of the present invention.
  • the skin condition estimation device 10 has a CPU (Central Processing Unit) 1001 , a ROM (Read Only Memory) 1002 and a RAM (Random Access Memory) 1003 .
  • the CPU 1001, ROM 1002, and RAM 1003 form a so-called computer.
  • the skin condition estimation device 10 can have an auxiliary storage device 1004 , a display device 1005 , an operation device 1006 , an I/F (Interface) device 1007 and a drive device 1008 .
  • Each piece of hardware of the skin condition estimation device 10 is connected to each other via a bus B.
  • the CPU 1001 is an arithmetic device that executes various programs installed in the auxiliary storage device 1004 .
  • the ROM 1002 is a non-volatile memory.
  • the ROM 1002 functions as a main storage device that stores various programs, data, etc. necessary for the CPU 1001 to execute various programs installed in the auxiliary storage device 1004 .
  • the ROM 1002 functions as a main storage device that stores boot programs such as BIOS (Basic Input/Output System) and EFI (Extensible Firmware Interface).
  • BIOS Basic Input/Output System
  • EFI Extensible Firmware Interface
  • the RAM 1003 is a volatile memory such as DRAM (Dynamic Random Access Memory) or SRAM (Static Random Access Memory).
  • the RAM 1003 functions as a main storage device that provides a work area that is developed when various programs installed in the auxiliary storage device 1004 are executed by the CPU 1001 .
  • the auxiliary storage device 1004 is an auxiliary storage device that stores various programs and information used when various programs are executed.
  • the display device 1005 is a display device that displays the internal state of the skin condition estimation device 10 and the like.
  • the operating device 1006 is an input device through which a person who operates the skin condition estimation device 10 inputs various instructions to the skin condition estimation device 10 .
  • the I/F device 1007 is a communication device for connecting to a network and communicating with other devices.
  • a drive device 1008 is a device for setting a storage medium 1009 .
  • the storage medium 1009 here includes media such as CD-ROMs, flexible disks, magneto-optical disks, etc., which record information optically, electrically or magnetically.
  • the storage medium 1009 may also include a semiconductor memory that electrically records information such as an EPROM (Erasable Programmable Read Only Memory), a flash memory, or the like.
  • auxiliary storage device 1004 Various programs to be installed in the auxiliary storage device 1004 are installed by, for example, setting the distributed storage medium 1009 in the drive device 1008 and reading the various programs recorded in the storage medium 1009 by the drive device 1008. be done. Alternatively, various programs installed in the auxiliary storage device 1004 may be installed by being downloaded from the network via the I/F device 1007 .
  • the skin condition estimation device 10 has an imaging device 1010 .
  • a photographing device 1010 photographs the user 20 .
  • skin condition estimation device 20 user 101 image acquisition unit 102 nose feature identification unit 103 skin condition estimation unit 104 skeleton estimation unit 105 output unit 1001 CPU 1002 ROMs 1003 RAM 1004 auxiliary storage device 1005 display device 1006 operation device 1007 I/F device 1008 drive device 1009 storage medium 1010 imaging device

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CN202280010218.8A CN116801800A (zh) 2021-02-15 2022-02-15 皮肤状态推定方法、装置、程序、系统、已学习模型生成方法及已学习模型

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