KR102543427B1 - Apparatus for analyzing face facial features - Google Patents

Abstract

According to an embodiment of the present invention, a device for analyzing a face using features of the face comprises: an input unit receiving an input of a facial image of a user; and a processor generating a feature coordinate of each of a plurality of features showing features on the shape of the face of the user and an eyebrow outer line function showing an outer line of eyebrows based on the facial image. The processor is able to calculate numerical information used for determining the type information of the eyes, nose, and lips of the user showing the type of each of the eyes, nose, and lips of the user by using one or more of the plurality of feature coordinates and the eyebrow outer line function and reflecting the shape of the eyes, nose, and lips of the user, and determine the type information of the eyes, nose, and lips based on the numerical information. Therefore, numerical and objective information on the user's type of eyebrows can be provided.

Description

Apparatus for analyzing face facial features}

The present invention relates to a facial analysis device using facial feature points, and more particularly, extracts a plurality of feature points from a user's face image, and calculates various information representing facial features using the plurality of feature points to provide eyebrow type information. , It relates to a facial analysis device using facial feature points capable of providing Jeontaegung type information, glabellar type information, total philtrum type information, chin philtrum type information, and jawline type information.

In general, the four pillars in Eastern philosophy refer to the year pillar, the month pillar, the day pillar, and the hour pillar, which are the pillars for the year of birth, the month of birth, the birthday and the time of birth, respectively. .

In addition, since one week (柱) is made up of two letters of the kanji (干支), the total number of four weeks is eight, so it is called four weeks.

Therefore, the solution to the four main characters is to find out how my five elements (wood, fire, earth, metal, water) and my four main eight characters are related based on the Yin-Yang five elements, and how my luck will progress from this.

In addition, seeing a person's face, hands, feet, etc., and judging the person's luck or fate is called contemplation.

This type of fortune-telling or contemplation can only be known through in-depth study for a long period of time or through experts such as fortune tellers. They have solved their curiosity by predicting the year or four weeks of their lifetime and their own contemplation, or through divination provided as a service in each daily newspaper or magazine.

In this way, as the interest in coronary art and beauty of appearance increases, interest in and demand for plastic surgery to change one's own coronary art and appearance are also increasing.

However, conventionally, subjective and empirical analysis opinions of a service provider (a physiotherapist) on a user's facial image are provided as physiognomy information through a popular Internet service.

Accordingly, there arises a problem in that the physiognomy information is different for each service provider even for the same user's face image.

Therefore, a technique for providing numerical data by extracting feature points and outlines of a user's facial image is required for objective physioscopic analysis of the user's facial image.

Korean Patent Publication No. 10-2016-0062429

An object of the present invention is to use face feature points capable of determining user's eyebrow type information based on at least one of a plurality of eyebrow peak relative positions, eyebrow inclination, eyebrow curvature, and eyebrow thickness ratio calculated from a user's face image. It is to provide an analysis device.

It is also an object of the present invention to determine the user's front arch type information and eyebrow type information based on at least one of a plurality of eyebrow peak relative positions, eyebrow inclination, eyebrow curvature, and eyebrow thickness ratio calculated from a user's facial image. An object of the present invention is to provide a facial analysis device using facial feature points.

Another object of the present invention is to provide a face analysis device using facial feature points capable of determining total weight type information based on a total weight ratio calculated from a user's face image.

Another object of the present invention is to provide a facial analysis device using facial feature points capable of determining chin-thrust type information based on a chin-thrust ratio calculated from a user's face image.

Another object of the present invention is to provide a facial analysis device using facial feature points capable of determining jawline type information based on a jawline ratio calculated from a facial image of a user.

The objects of the present invention are not limited to the above-mentioned objects, and other objects and advantages of the present invention not mentioned above can be understood by the following description and will be more clearly understood by the examples of the present invention. It will also be readily apparent that the objects and advantages of the present invention may be realized by means of the instrumentalities and combinations indicated in the claims.

A facial analysis apparatus using facial feature points according to an embodiment of the present invention includes an input unit for receiving a user's facial image; and a processor for generating an eyebrow contour function representing coordinates of each of a plurality of feature points representing features of the shape of the user's face and an eyebrow outline function representing an outline of the eyebrow based on the face image, wherein the processor includes a plurality of feature point coordinates and Using one or more of the eyebrow outline functions, it is used to determine feature type information representing each type of the user's features, calculates numerical information in which the shape of the features is reflected, and determines the feature type information based on the numerical information. can

Preferably, the processor uses a plurality of feature point coordinates and one or more of the eyebrow outline functions to represent the relative position of the user's eyebrow peak relative to the position of at least one of the eyes and eyebrows, the relative position of the eyebrows, the inclination of the eyebrows, and the eyebrows. Eyebrow type information that calculates a curvature and eyebrow thickness ratio as the numerical information, and indicates the type of the user's eyebrow based on at least one of the plurality of eyebrow peak relative positions, the eyebrow inclination, the eyebrow curvature, and the eyebrow thickness ratio. may be determined as one of the features type information.

Preferably, the processor may set, as a vertical axis, a straight line connecting an upper and lower feature points representing upper and lower faces of the user's face, respectively, among the plurality of feature points, and set a horizontal axis perpendicular to the vertical axis.

Preferably, the processor extracts a first vertical line passing through the eye head feature point representing the head of the eye from among the plurality of feature points and a first intersection point at which an upper outline intersects among the outline lines of the eyebrow, and extracts the eyebrow peak from among the plurality of feature points. An inclination of a first straight line connecting the eyebrow peak feature point and the first intersection point may be calculated as the eyebrow inclination.

Preferably, the processor calculates a first distance between the eyebrow peak feature point and the first intersection point on the horizontal axis, calculates a second distance between the upper outline and the first straight line on the vertical axis, and calculates the first distance on the vertical axis. A first distance ratio of the second distance to the distance may be calculated as the eyebrow curvature.

Preferably, the processor extracts a second intersection point where the lower outline intersects among the first vertical line and the outline of the eyebrow, calculates a third distance between the first intersection point and the second intersection point, and determines the feature point of the eyebrow peak. A third intersection point at which a passing second vertical line and the lower outline intersect is extracted, a fourth distance is calculated between the eyebrow peak feature point and the third intersection point, and a second distance ratio of the fourth distance to the third distance is calculated. It can be calculated by the eyebrow thickness ratio.

Preferably, the processor extracts a first midpoint on the horizontal axis between the eye head feature point representing the head of the eye and the eyebrow tail feature point representing the eyebrow tail from among the plurality of feature points, and the eyebrow peak feature point representing the eyebrow peak among the plurality of feature points. and calculating a fifth distance on the horizontal axis between the vertical axes, calculating a sixth distance between the first midpoint on the horizontal axis and the vertical axis, and calculating a first distance difference between the fifth distance and the sixth distance. It can be calculated as the relative position of the first eyebrow ridge among the plurality of eyebrow ridge relative positions.

Preferably, the processor extracts a second midpoint on the horizontal axis between the eyebrow head feature point representing the eyebrow head and the eyebrow tail feature point among the plurality of feature points, and a seventh distance between the eyebrow head feature point and the vertical axis on the horizontal axis. , calculates an eighth distance between the second midpoint on the horizontal axis and the vertical axis, calculates a sum of distances between the seventh distance and the eighth distance, and calculates a second distance between the fifth distance and the sum of distances. The distance difference may be calculated as the relative position of the second eyebrow ridge among the plurality of eyebrow ridge relative positions.

Preferably, the processor determines the eyebrow type information when the eyebrow slope is equal to or less than the first reference slope, the eyebrow thickness ratio is greater than or equal to the reference thickness ratio, and the first eyebrow peak relative position among the plurality of eyebrow peak relative positions is greater than or equal to the reference relative position. is determined as a straight eyebrow type, and the eyebrow type information may be determined as a semi-arch eyebrow type when the eyebrow slope is less than or equal to the first reference slope and the eyebrow thickness ratio is less than or equal to the reference thickness ratio.

Preferably, the processor determines the eyebrow type information as an arcuate eyebrow type when the eyebrow slope is greater than or equal to a first reference slope and the eyebrow curvature is greater than or equal to the reference curvature, and the eyebrow slope is greater than or equal to the first reference slope and the eyebrow curvature is If the curvature is less than the reference curvature, the eyebrow type information may be determined as an angular eyebrow type.

Preferably, the processor determines the eyebrow type information as a standard eyebrow type when the eyebrow slope exceeds the first reference slope and is less than a second reference slope greater than the first reference slope, and the eyebrow slope is the second reference slope above, and if the relative position of the second eyebrow peak among the plurality of eyebrow peaks is equal to or greater than the reference relative position, the eyebrow type information may be determined as the raised eyebrow type.

The facial analysis apparatus using facial feature points according to the present invention determines the user's eyebrow type information based on at least one of a plurality of eyebrow ridge relative positions, eyebrow inclination, eyebrow curvature, and eyebrow thickness ratio calculated from a user's facial image. By doing so, it is possible to provide numerical and objective information about the user's eyebrow type.

In addition, the facial analysis apparatus using facial feature points according to the present invention is based on one or more of the relative position of a plurality of eyebrow peaks calculated from the user's facial image, the slope of the eyebrows, the curvature of the eyebrows, and the thickness ratio of the eyebrows. By determining the information and the eyebrow type information, it is possible to provide numerical and objective information about the user's Jeon Taekung type and the eyebrow type.

In addition, an object of the present invention is to provide numerical and objective information about a user's total weight type by determining total weight type information based on a total weight ratio calculated from a user's face image.

In addition, an object of the present invention is to provide numerical and objective information about a user's chin-thrust type by determining chin-thrust type information based on a chin-thrust ratio calculated from a user's face image.

Also, an object of the present invention is to provide numerical and objective information about a user's jawline type by determining jawline type information based on a jawline ratio calculated from a user's facial image.

1 is a diagram illustrating a state in which a facial analysis device using facial feature points according to an embodiment of the present invention receives a user's facial image.
2 is a configuration block diagram of a facial analysis device using facial feature points according to an embodiment of the present invention.
3 is a diagram for explaining a process of generating a plurality of feature point coordinates and an eyebrow contour function by a facial analysis apparatus using facial feature points according to an embodiment of the present invention.
4 is a diagram for explaining a process of calculating an eyebrow inclination by a facial analysis device using facial feature points according to an embodiment of the present invention.
5 is a diagram for explaining a process of calculating eyebrow curvature by a facial analysis device using facial feature points according to an embodiment of the present invention.
6 is a diagram for explaining a process of calculating an eyebrow thickness ratio by a facial analysis device using facial feature points according to an embodiment of the present invention.
7 is a diagram for explaining a process of calculating a first relative position of an eyebrow among a plurality of relative positions of an eyebrow by a facial analysis apparatus using facial feature points according to an embodiment of the present invention.
8 is a diagram for explaining a process of calculating a second relative position of an eyebrow among a plurality of relative positions of an eyebrow by a facial analysis apparatus using facial feature points according to an embodiment of the present invention.
9 is a diagram for explaining a process in which a facial analysis device using face feature points determines information on a Jeontaekung type according to another embodiment of the present invention.
10 is a diagram for explaining a process of determining forehead type information by a facial analysis apparatus using facial feature points according to another embodiment of the present invention.
FIG. 11 is a diagram for explaining a process of determining total weight type information and chin weight type information by a facial analysis apparatus using facial feature points according to another embodiment of the present invention.
12 is a diagram for explaining a process of determining jawline type information by a facial analysis apparatus using facial feature points according to another embodiment of the present invention.

Hereinafter, various embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that this is not intended to limit the present invention to specific embodiments, and includes various modifications, equivalents, and/or alternatives of the embodiments of the present invention. In connection with the description of the drawings, like reference numerals may be used for like elements.

In this document, expressions such as "has", "may have", "includes", or "may include" refer to the presence of a corresponding feature (eg, numerical value, function, operation, or component such as a part). , which does not preclude the existence of additional features.

In this document, expressions such as "A or B", "at least one of A and/and B", or "one or more of A or/and B" may include all possible combinations of the items listed together. . For example, "A or B", "at least one of A and B", or "at least one of A or B" (1) includes at least one A, (2) includes at least one B, or (3) It may refer to all cases including at least one A and at least one B.

Expressions such as "first", "second", "first", or "second" used in this document may modify various components, regardless of order and/or importance, and refer to a component as It is used only to distinguish it from other components and does not limit the corresponding components. For example, a first user device and a second user device may represent different user devices regardless of order or importance. For example, without departing from the scope of rights described in this document, a first element may be called a second element, and similarly, the second element may also be renamed to the first element.

A component (e.g., a first component) is "(operatively or communicatively) coupled with/to" another component (e.g., a second component); When referred to as "connected to", it should be understood that the certain component may be directly connected to the other component or connected through another component (eg, a third component). On the other hand, when an element (eg, a first element) is referred to as being “directly connected” or “directly connected” to another element (eg, a second element), the element and the other element are referred to as being “directly connected”. It may be understood that there are no other components (eg, a third component) between the components.

As used in this document, the expression "configured to" means, depending on the situation, e.g., "suitable for", "having the capacity to" )", "designed to", "adapted to", "made to", or "capable of" . The term "configured (or set) to" may not necessarily mean only "specifically designed to" hardware. Instead, in some contexts, the phrase "a device configured to" may mean that the device is "capable of" in conjunction with other devices or components. For example, the phrase “a control unit configured (or set) to perform A, B, and C” may be expressed by a dedicated processor (e.g., embedded processor) to perform the operation, or by executing one or more software programs stored in memory. , may mean a general-purpose processor (eg, CPU or application processor) capable of performing corresponding operations.

In particular, in the present specification, “~ device” may include one or more of a central processing unit (CPU), an application processor (AP), and a communication processor (CP). .

In this specification, “~device” refers to all types of hardware devices including at least one processor, and may be understood as encompassing software configurations operating in the corresponding hardware devices according to embodiments. For example, “~device” may be understood as including wearable devices, smart phones, tablet PCs, and user clients and applications running on each device, but is not limited thereto.

Terms used in this document are only used to describe a specific embodiment, and may not be intended to limit the scope of other embodiments. Singular expressions may include plural expressions unless the context clearly dictates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as commonly understood by a person of ordinary skill in the technical field described in this document. Among the terms used in this document, terms defined in general dictionaries may be interpreted as having the same or similar meaning as the meaning in the context of the related art, and unless explicitly defined in this document, in an ideal or excessively formal meaning. not interpreted In some cases, even terms defined in this document cannot be interpreted to exclude the embodiments of this document.

1 is a diagram showing a state in which a facial analysis device using facial feature points according to an embodiment of the present invention receives a user's facial image, and FIG. 3 is a block diagram of a facial analysis apparatus, and FIG. 3 is a diagram for explaining a process of generating a plurality of feature point coordinates and an eyebrow outline function by the facial analysis apparatus using facial feature points according to an embodiment of the present invention.

1 to 3, the facial analysis apparatus 100 using facial feature points according to an embodiment of the present invention represents characteristics of the shape of the user's face from a facial image generated by photographing the user's face. A plurality of feature points may be extracted and an outline of the eyebrow may be extracted.

Thereafter, the facial analysis apparatus 100 using the feature points of the face generates an eyebrow outline function of the feature point coordinates of each of the plurality of feature points and the eyebrow outline, and uses one or more of the plurality of feature point coordinates and the eyebrow outline function to obtain numerical information. , and the feature type information may be determined based on numerical information.

Here, the numerical information is used to determine feature type information indicating each type of the user's features and may be information in which the shape of the features is reflected.

For example, the numerical information may be relative positions of a plurality of eyebrow peaks, an eyebrow inclination, an eyebrow curvature, an eyebrow thickness ratio, a total philtrum ratio, a chin philtrum ratio, and a chin line ratio, which will be described later.

In addition, the feature type information includes eyebrow type information representing the user's eyebrow type, front arch type information representing the user's front arch type, glabellar type information representing the user's eyebrow type, and total philtrum type information representing the user's total brow type. , may be chin-in type information indicating the user's chin-in type and jaw-line type information indicating the user's jaw-line type.

To this end, the face analysis device 100 using facial feature points according to an embodiment of the present invention includes an input unit 110, a processor 120, an output unit 130, a communication unit 140, and a storage unit 150. can include

The input unit 110 may receive a user's face image.

At this time, the input unit 110 may be connected to the photographing device and receive a facial image output from the photographing device. In addition, the input unit 110 may include a camera module to generate a facial image by photographing the user's face instead of receiving the facial image from the photographing device.

The processor 120 may extract a plurality of feature points indicating characteristics of the user's face shape and an outline of the eyebrows based on the face image.

In addition, the processor 120 may further extract feature points representing features of the shape of the user's face based on the face image.

To this end, the processor 120 may extract a plurality of feature points and the outline of the eyebrows from the facial image using at least one of a ventilation-based model, an artificial neural network model, a connection structure model, and a Markov model.

At this time, the processor 120 may extract facial contours, feature points of either one of the eyebrows and both eyes, and the outline of either of the eyebrows described above.

Specifically, as shown in FIG. 3 , the processor 120 selects, as a plurality of feature points, an upper feature point (A) representing the upper part of the user's face, a lower feature point (Q) representing the lower part of the user's face, and the tip of the user's cheekbones. The cheekbone feature point I can be extracted.

In addition, as a plurality of feature points, the processor 120 includes an eyebrow head feature point (C) representing the user's eyebrow head, a eyebrow peak feature point (D) representing the user's eyebrow peak, and an eyebrow tail feature point (E) representing the user's eyebrow tail. can be extracted.

In addition, the processor 120 may extract, as a plurality of feature points, an eye feature point (F) representing the head of the user's eyes, a feature point (R) representing the eyes of the user, and a feature point (H) representing the tail of the user's eyes. there is.

In addition, the processor 120 extracts a nose tip feature point (M) representing the tip of the user's nose, an upper lip feature point (N) representing the top of the user's upper lip, and a lower lip feature point (P) representing the bottom of the user's lower lip, as a plurality of feature points. can do.

Here, the eyebrow head may refer to the innermost point of the outer edge of the eyebrow.

Here, the eyebrow tail may refer to the outermost point of the outer edge of the eyebrow.

Here, the eyebrow mountain may refer to the highest point among the outlines of the eyebrows.

Here, the head of the eye may mean the innermost point of the outer periphery of the eye.

Here, the outer corner of the eye may mean the outermost point of the outer periphery of the eye.

Here, the snow mountain may mean the highest point among the outer rims of snow.

Meanwhile, as shown in FIG. 3 , the processor 120 may extract the outline of the eyebrows from the facial image by dividing the outline into an upper outline U and a lower outline L.

Here, the upper outline (U) may refer to an outline located at the top of two outlines obtained by separating the outline of the eyebrow forming a closed curve based on the eyebrow head feature point (C) and the eyebrow tail feature point (E). That is, the upper outline (U) may refer to an outline positioned at the upper part among the outlines of the eyebrow connecting the eyebrow head feature point (C) and the eyebrow tail feature point (E).

Here, the lower outline (L) may refer to an outline located at the lower part of two outlines obtained by separating the outline of the eyebrow forming a closed curve based on the eyebrow head feature point (C) and the eyebrow tail feature point (E). That is, the lower outline (L) may refer to an outline located at the lower part of the outlines of the eyebrow connecting the eyebrow head feature point (C) and the eyebrow tail feature point (E).

The processor 120 may generate feature point coordinates of each of a plurality of feature points and create an eyebrow outline function representing the outline of the eyebrow.

In this case, the processor 120 may set a coordinate plane on the face image and generate a plurality of feature point coordinates and an eyebrow contour function based on the set coordinate plane.

The processor 120 generates an eyebrow upper contour function (U(x)) representing the upper contour line (U) and an eyebrow upper contour function (L(x)) representing the lower contour line (L) as the eyebrow contour function. can do.

To this end, as shown in FIG. 3 , the processor 120 draws a straight line connecting the upper and lower feature points A and Q representing the upper and lower faces of the user among the plurality of feature points on a vertical axis (Y-axis). ) and set a horizontal axis (X axis) perpendicular to the vertical axis (Y axis).

Accordingly, the processor 120 may generate the feature point coordinates of the lower feature point Q as (0, 0) and the feature point coordinates of the upper feature point A as (0, 100).

Thereafter, the processor 120 uses a plurality of feature point coordinates and one or more of the eyebrow contour functions to determine the relative positions of the eyebrow ridges of the user relative to the position of one or more of the eyes and eyebrows, the slope of the eyebrows, Eyebrow curvature and eyebrow thickness ratio can be calculated.

4 is a diagram for explaining a process of calculating an eyebrow inclination by a facial analysis device using facial feature points according to an embodiment of the present invention.

Referring to FIG. 4 , the processor 120 further includes a first intersection point where a first vertical line passing through the eye head feature point F representing the eye head among the plurality of feature points and the upper outline U among the outlines of the eyebrows intersect ( Z1) can be extracted.

Then, the processor 120 may calculate the slope of a first straight line connecting the eyebrow peak feature point D representing the eyebrow peak among the plurality of feature points and the first intersection point Z1 as the eyebrow slope.

At this time, the processor 120 may calculate the eyebrow slope using Equation 1 below.

<Equation 1>

Here, C1 is the inclination of the eyebrow, G is the inclination of the first straight line, y _d is the coordinate on the vertical axis of the feature point D of the eyebrow ridge, U(x _f ) is the coordinate on the vertical axis of the first intersection point Z1, x _d is the coordinate of the eyebrow peak feature point D on the horizontal axis, and x _f is the coordinate of the first intersection point Z1 on the horizontal axis.

5 is a diagram for explaining a process of calculating eyebrow curvature by a facial analysis device using facial feature points according to an embodiment of the present invention.

Referring further to FIG. 5 , the processor 120 calculates a first distance between the eyebrow peak feature point D and the first intersection point Z1 on the horizontal axis, and calculates the upper outline U and the above description on the vertical axis. A second distance between the first straight lines may be calculated.

Thereafter, the processor 120 may calculate a first distance ratio of the second distance to the first distance as the eyebrow curvature.

At this time, the processor 120 may calculate the eyebrow curvature using Equation 2 below.

<Equation 2>

Here, C2 is the eyebrow curvature, R1 is the first distance ratio, L1 is the first distance, L2 is the second distance, and U((x _f +x _d )/2) is the front eye feature point (F on the horizontal axis) ) and the coordinates on the vertical axis of the intersection of the vertical line passing through the midpoint of the eyebrow peak feature point D and the upper outline U, y _d is the coordinate on the vertical axis of the eyebrow peak feature point D, and U(x _f ) is the first X d is the coordinate on the vertical axis of the intersection point Z1, x _d is the coordinate on the horizontal axis of the feature point D of the eyebrow peak, and x _f is the coordinate on the horizontal axis of the first intersection point Z1.

6 is a diagram for explaining a process of calculating an eyebrow thickness ratio by a facial analysis device using facial feature points according to an embodiment of the present invention.

Referring further to FIG. 6 , the processor 120 may extract a second crossing point Z2 where the lower outline L intersects among the aforementioned first vertical line and the outline of the eyebrow.

In addition, the processor 120 may extract a third intersection point Z3 where the second vertical line passing through the eyebrow peak feature point D and the lower outline L intersect.

Thereafter, the processor 120 calculates a third distance between the first intersection point Z1 and the second intersection point Z2, and calculates a fourth distance between the eyebrow peak feature point D and the third intersection point Z1. can do.

Finally, the processor 120 may calculate a second distance ratio of the third distance to the third distance as the eyebrow thickness ratio.

At this time, the processor 120 may calculate the eyebrow thickness ratio using Equation 3 below.

<Equation 3>

Here, C3 is the eyebrow thickness ratio, R2 is the second distance ratio, L3 is the third distance, L4 is the fourth distance, U(x _d ) is the coordinate on the vertical axis of the eyebrow peak feature point D, and L (x _d ) is the coordinate on the vertical axis of the third intersection point Z3, U(x _f ) is the coordinate on the vertical axis of the first intersection point Z1, and L(x _f ) is the coordinate on the vertical axis of the second intersection point Z2. is the coordinates

7 is a diagram for explaining a process of calculating a first relative position of an eyebrow among a plurality of relative positions of an eyebrow by a facial analysis apparatus using facial feature points according to an embodiment of the present invention.

Referring further to FIG. 7 , the processor 120 may extract a first midpoint on the horizontal axis between the eye head feature point F indicating the eye head and the eyebrow tail feature point E indicating the eyebrow tail from among the plurality of feature points. there is.

Thereafter, the processor 120 calculates a fifth distance on the horizontal axis between the eyebrow peak feature point D representing the eyebrow peak among the plurality of feature points and the vertical axis, and calculates a fifth distance between the vertical axis and the first midpoint on the horizontal axis. 6 distances can be calculated.

Finally, the processor 120 may calculate a first distance difference between the fifth distance and the sixth distance as a first relative position of the eyebrow ridge among the plurality of eyebrow ridge relative positions.

In this case, the processor 120 may calculate the relative position of the first eyebrow ridge using Equation 4 below.

<Equation 4>

Here, C4 is the first eyebrow peak relative position, D1 is the first distance difference, L5 is the fifth distance, L6 is the sixth distance, x _d is the coordinate on the horizontal axis of the eyebrow peak feature point D, and x _f is the coordinate on the horizontal axis of the eyebrow feature point (F), and x _e is the coordinate on the horizontal axis of the eyebrow tail feature point (E).

8 is a diagram for explaining a process of calculating a second relative position of an eyebrow among a plurality of relative positions of an eyebrow by a facial analysis apparatus using facial feature points according to an embodiment of the present invention.

Further referring to FIG. 8 , the processor 120 may extract a second midpoint on the horizontal axis between the eyebrow head feature point C representing the eyebrow head and the eyebrow tail feature point E among the plurality of feature points.

Thereafter, the processor 120 calculates a seventh distance between the eyebrow head feature point C on the horizontal axis and the vertical axis, calculates an eighth distance between the second midpoint on the horizontal axis and the vertical axis, and calculates the A sum of distances between the 7th distance and the 8th distance may be calculated.

Finally, the processor 120 may calculate a second distance difference between the seventh distance and the distance sum as a second relative position of the eyebrow ridge among the plurality of eyebrow ridge relative positions.

At this time, the processor 120 may calculate the relative position of the second eyebrow ridge using Equation 5 below.

<Equation 5>

Here, C5 is the second eyebrow peak relative position, D2 is the second distance difference, L5 is the fifth distance, L7 is the seventh distance, L8 is the eighth distance, and x _d is the eyebrow peak feature point (D). It is a coordinate on the horizontal axis, x _c is a coordinate on the horizontal axis of the eyebrow head feature point (C), and x _e is a coordinate on the horizontal axis of the eyebrow tail feature point (E).

Thereafter, the processor 120 may determine eyebrow type information indicating the type of the user's eyebrows based on at least one of the plurality of eyebrow peak relative positions, the eyebrow inclination, the eyebrow curvature, and the eyebrow thickness ratio.

At this time, the processor 120 may determine the user's eyebrow type information as one or more of a straight eyebrow type, a semi-arched eyebrow type, an arched eyebrow type, an angled eyebrow type, a standard eyebrow type, and a raised eyebrow type.

Specifically, the processor 120 determines the eyebrow ridge when the eyebrow slope is equal to or less than the first reference slope, the eyebrow thickness ratio is greater than or equal to the reference thickness ratio, and the first eyebrow peak relative position among the plurality of eyebrow peak relative positions is greater than or equal to the reference relative position. The type information may be determined as a straight eyebrow type.

In addition, the processor 120 may determine the eyebrow type information as a semi-arch eyebrow type when the eyebrow slope is less than or equal to the first reference slope and the eyebrow thickness ratio is less than or equal to the reference thickness ratio.

Also, the processor 120 may determine the eyebrow type information as an arched eyebrow type when the eyebrow slope is greater than or equal to the first reference slope and the eyebrow curvature is greater than or equal to the reference curvature.

In addition, the processor 120 may determine the eyebrow type information as the angular eyebrow type when the eyebrow slope is greater than or equal to the first reference slope and the eyebrow curvature is less than the reference curvature.

In addition, the processor 120 may determine the eyebrow type information as a standard eyebrow type when the eyebrow slope exceeds the first reference slope and is less than a second reference slope greater than the first reference slope.

In addition, the processor 120 may determine the eyebrow type information as the raised eyebrow type when the eyebrow slope is equal to or greater than the second reference slope and the second eyebrow peak relative position among the plurality of eyebrow peak relative positions is equal to or greater than the reference relative position.

Here, the first reference slope may be 0.1 and the second reference slope may be 0.3.

Here, the reference thickness ratio may be 2/3.

Here, the reference curvature may be 0.05.

Here, the reference relative position may be 0.

Through this, the facial analysis device 100 using facial feature points provides information numerically representing the shape of the eyebrows using a plurality of feature points and the outline of the eyebrows (relative positions of a plurality of eyebrow peaks, eyebrow inclination, eyebrow curvature, and eyebrow thickness). ratio) can be calculated, and the type of eyebrows can be determined using the calculated information.

The processor 120 may perform the operation of each of the above-described components, and one or more cores (not shown) and a graphic processing unit (not shown) and/or a connection path for transmitting and receiving signals to and from other components ( For example, a bus, etc.) may be included.

The processor 120 may be configured to perform the operation of each component described above by executing one or more instructions stored in the storage unit 150 .

The storage unit 150 may store programs (one or more instructions) for processing and control of the processor 120 . Programs stored in the storage unit 150 may be divided into a plurality of modules according to functions.

The output unit 130 may display the user's facial image, information numerically indicating the shape of the eyebrows (relative positions of a plurality of eyebrow peaks, eyebrow slope, eyebrow curvature, and eyebrow thickness ratio), and determined eyebrow type information on the screen. .

To this end, the output unit 130 may include a display module.

The facial analysis device 100 using facial feature points may be any one of a smart phone, a tablet PC, a desktop computer, and a laptop computer, and preferably may be a smart phone.

Hereinafter, a process of determining the Jeontaegung type information and the glabellar type information by the facial analysis apparatus 100 using facial feature points according to another embodiment of the present invention will be described.

Since the facial analysis device 100 using facial feature points according to another embodiment of the present invention is different from the facial analysis device 100 using facial feature points according to an embodiment of the present invention, only the role of the processor 120 is different. , repeated explanations are omitted.

FIG. 9 is a diagram for explaining a process of determining the type information of Jeontaekung by a facial analysis apparatus using facial feature points according to another embodiment of the present invention, and FIG. 10 is a diagram illustrating facial feature points according to another embodiment of the present invention. It is a diagram for explaining the process of determining the type information between the eyebrows by the used facial analysis device.

9, the processor 120 of the facial analysis apparatus 100 using facial feature points according to another embodiment of the present invention uses at least one of a plurality of feature point coordinates and the eyebrow contour function to determine the ratio , and based on the ratio of palaces for palaces, it is possible to determine palace type information indicating the type of palaces for palaces of the user.

Specifically, the processor 120 may extract a fourth crossing point Z4 where a third vertical line passing through a feature point R of a snow mountain representing a snow mountain and the lower outline L intersect from among a plurality of feature points.

Thereafter, the processor 120 calculates a ninth distance on a horizontal line between the eye tail feature point H and the eye feature point F among the plurality of feature points, and between the eye mountain feature point R and the fourth intersection point Z4. A tenth distance can be calculated.

Finally, the processor 120 may calculate a third distance ratio of the tenth distance to the ninth distance as the Jeon Taekung ratio.

At this time, the processor 120 may calculate the Jeon Taek-gung ratio using Equation 6 below.

<Equation 6>

Here, C6 is the Jeon Taekung ratio, R3 is the third distance ratio, L9 is the ninth distance, L10 is the tenth distance, L(x _r ) is the coordinate on the vertical axis of the fourth intersection point Z4, and y _r is the coordinate on the vertical axis of the eye feature point R, x _h is the coordinate on the horizontal axis of the feature point H of the outer corner of the eye, and x _r is the coordinate on the horizontal axis of the feature point R of the eye.

Thereafter, the processor 120 may determine the type of palace palace information as one of a small palace type and a major palace type based on the ratio of palace palaces.

Here, the small Jeontaekung type may be a Jeontaekung type indicating that the Jeontaekung palace is narrow, and the Daejeontaekung type may be a Jeontaekung type indicating that the Jeontaekung palace is wide.

Specifically, the processor 120 may determine the total palace type information as the minor palace type when the total palace ratio is less than the first reference palace palace ratio.

Also, the processor 120 may determine the Taetaekung type information as the Taetaekung type when the Taetaekung ratio exceeds the second reference Jeontaekung ratio.

Here, the first standard Ponytail ratio may be 0.1, and the second standard Ponytail ratio may be 0.5.

Further referring to FIG. 10 , the processor 120 calculates a plurality of relative lengths between the eyebrows using one or more of the plurality of feature point coordinates and the eyebrow outline function, and determines the type of the user's forehead based on the plurality of relative lengths between the eyebrows. Glabellar type information to be displayed may be determined.

Specifically, the processor 120 may calculate the first relative length between the eyebrows and the second relative length between the eyebrows as the plurality of relative lengths between the eyebrows.

First, a process of calculating the first eyebrow relative length by the processor 120 will be described.

The processor 120 calculates an eleventh distance on the horizontal axis between the vertical axis and the eyebrow head feature point (C) representing the eyebrow head among the plurality of feature points, and calculates the eyebrow tail feature point (E) representing the eyebrow tail among the plurality of feature points. A twelfth distance on a horizontal axis may be calculated between the eye feature points F representing the eyes.

Thereafter, the processor 120 may reduce the twelfth distance by a preset magnification to calculate the corrected distance.

Finally, the processor 120 may calculate the third distance difference between the eleventh distance and the corrected distance as the first eyebrow relative length among the plurality of eyebrow relative lengths.

In this case, the processor 120 may calculate the relative length between the first eyebrows using Equation 7 below.

<Equation 7>

Here, C7 is the first glabellar relative length, D3 is the third distance difference, L11 is the 11th distance, L12' is the correction distance, L12 is the 12th distance, and j is a preset magnification (e.g., 3 ), x _c is the coordinate on the horizontal axis of the eyebrow head feature point (C), x _e is the coordinate on the horizontal axis of the eyebrow tail feature point (E), and x _f is the coordinate on the horizontal axis of the eyebrow head feature point (F).

Next, a process of calculating the second eyebrow relative length by the processor 120 will be described.

The processor 120 may calculate the aforementioned 11th distance and calculate a 13th distance on the vertical axis between the first crossing point Z1 and the horizontal axis.

Finally, the processor 120 may calculate the fourth distance difference between the 11th distance and the 13th distance as the second eyebrow relative length among the plurality of eyebrow relative lengths.

In this case, the processor 120 may calculate the relative length between the second eyebrows using Equation 8 below.

<Equation 8>

Here, C8 is the relative length between the second eyebrows, D4 is the fourth distance difference, L11 is the 11th distance, L13 is the 13th distance, x _c is the coordinate on the horizontal axis of the feature point C of the eyebrow head, and U(x _f ) is the coordinate on the vertical axis of the first intersection point Z1.

The processor 120 determines the eyebrow type information as the eyebrow type based on the first eyebrow relative length among the plurality of eyebrow relative lengths, and converts the eyebrow type information based on the second eyebrow relative length among the plurality of eyebrow relative lengths to the major eyebrow. type can be determined.

Here, the small glabella type may be a glabellar type indicating narrow glabellars, and the large glabellar type may be a glabellar type indicating wide glabellars.

Specifically, the processor 120 may determine the eyebrow type information as the eyebrow type when the first relative length between the eyebrows exceeds the reference relative length.

Also, the processor 120 may determine the eyebrow type information as the eyebrow type when the second relative length between the eyebrows exceeds the reference relative length.

Here, the reference relative length may be zero.

Hereinafter, a process of determining total philtrum type information and chin philtrum type information by the facial analyzing apparatus 100 using facial feature points according to another embodiment of the present invention will be described.

The facial analysis apparatus 100 using facial feature points according to another embodiment of the present invention is different from the facial analysis apparatus 100 using facial feature points according to an embodiment of the present invention only in the role of the processor 120 Therefore, repeated descriptions are omitted.

FIG. 11 is a diagram for explaining a process of determining total weight type information and chin weight type information by a facial analysis apparatus using facial feature points according to another embodiment of the present invention.

Referring further to FIG. 11 , the processor 120 of the facial analysis apparatus 100 using facial feature points according to another embodiment of the present invention uses at least one of a plurality of feature point coordinates and the eyebrow contour function to determine the entire face. A weight ratio may be calculated, and based on the weight ratio, weight type information representing a user's weight type may be determined.

Specifically, the processor 120 calculates a 14th distance on a vertical line between the upper feature point A and the lower feature point Q, and calculates a 15th distance on a vertical line between the nose tip feature point M and the upper lip feature point N. can do.

Finally, the processor 120 may calculate a fourth distance ratio of the fifteenth distance to the fourteenth distance as a total weight ratio.

At this time, the processor 120 may calculate the total weight ratio using Equation 9 below.

<Equation 9>

Here, C9 is the total weight ratio, R4 is the fourth distance ratio, L14 is the 14th distance, L15 is the 15th distance, y _a is the coordinate on the vertical axis of the upper feature point (A), and y _q is the lower It is a coordinate on the vertical axis of the feature point Q, y _m is a coordinate on the vertical axis of the nose tip feature point M, and y _n is a coordinate on the vertical axis of the upper lip feature point N.

Then, the processor 120 may determine the total popularity type information as the chapter total popularity type based on the total popularity ratio.

Here, the total philtrum type may be a total philtrum type representing length of the philtrum relative to the vertical length of the face.

Specifically, the processor 120 may determine the total crowd type information as the chapter total crowd type when the total crowd ratio is equal to or greater than the reference crowd crowd ratio.

Here, the standard total population ratio may be 0.1.

Meanwhile, the processor 120 of the facial analysis apparatus 100 using facial feature points according to another embodiment of the present invention calculates a chin chin ratio using one or more of the coordinates of a plurality of feature points and the eyebrow contour function, Chin-in type information representing the user's chin-in type may be determined based on the chin-in ratio.

Specifically, the processor 120 may calculate a sixteenth distance between the lower lip feature point P and the lower lip feature point Q on a vertical line.

Finally, the processor 120 may calculate a fifth distance ratio of the fifteenth distance to the sixteenth distance as a chin-in ratio.

At this time, the processor 120 may calculate the chin-in ratio using Equation 10 below.

<Equation 10>

Here, C10 is the chin-in ratio, R5 is the 5th distance ratio, L15 is the 15th distance, L16 is the 16th distance, y _a is the coordinate on the vertical axis of the upper feature point (A), and y _p is the lower lip It is a coordinate on the vertical axis of the feature point P, y _m is a coordinate on the vertical axis of the nose tip feature point M, and y _n is a coordinate on the vertical axis of the upper lip feature point N.

Then, the processor 120 may determine the chin-thrust type information as the long chin-thrust type based on the chin-thrust ratio.

Here, the long chin philtrum type may be a chin philtrum type in which the philtrum represents seam compared to the vertical length from the lower lip to the lowermost end of the face.

Specifically, the processor 120 may determine the chin-thrust type information as the long chin-throw type when the chin-thrust ratio is greater than or equal to the reference chin-thrust ratio.

Here, the reference chin-in ratio may be 1.

Hereinafter, a process of determining jawline type information by the facial analyzing apparatus 100 using facial feature points according to another embodiment of the present invention will be described.

The facial analysis apparatus 100 using facial feature points according to another embodiment of the present invention is different from the facial analysis apparatus 100 using facial feature points according to an embodiment of the present invention only in the role of the processor 120 Therefore, repeated descriptions are omitted.

12 is a diagram for explaining a process of determining jawline type information by a facial analysis apparatus using facial feature points according to another embodiment of the present invention.

12, the processor 120 of the facial analysis apparatus 100 using facial feature points according to another embodiment of the present invention uses one or more of the coordinates of a plurality of feature points and the eyebrow outline function to determine the ratio of the jaw line. , and jawline type information representing the user's jawline type may be determined based on the jawline ratio.

Specifically, the processor 120 may detect a fifth intersection point O at which a vertical line passing through midpoints of the cheekbone feature point I and the lower feature point Q on the horizontal axis and the jaw line intersect.

Thereafter, the processor 120 may detect a vertical line passing through the fifth intersection point O from among vertical lines perpendicular to the straight line connecting the cheekbone feature point I and the lower feature point Q as an orthogonal line.

Next, the processor 120 may detect a sixth intersection point V at which straight lines and orthogonal lines connecting the cheekbone feature point I and the lower feature point Q intersect.

The processor 120 may calculate a seventeenth distance between the wide feature point I and the lower feature point Q, and calculate an eighteenth distance between the fifth intersection point O and the sixth intersection point V.

Finally, the processor 120 may calculate a sixth distance ratio of the eighteenth distance to the seventeenth distance as a jaw line ratio.

In this case, the processor 120 may calculate the jaw line ratio using Equation 11 below.

<Equation 11>

Here, C11 is the jawline ratio, R6 is the sixth distance ratio, L17 is the seventeenth distance, L18 is the eighteenth distance, y _o is the coordinate on the vertical axis of the sixth intersection point O, and y _v is the seventh is the coordinate on the vertical axis of the intersection point (V), y _q is the coordinate on the vertical axis of the lower feature point (Q), y _i is the coordinate on the vertical axis of the cheekbone feature point (I), and x _o is the coordinate on the horizontal axis of the sixth intersection point (O) coordinates, x _v is the coordinate on the horizontal axis of the seventh intersection point V, x _q is the coordinate on the horizontal axis of the lower feature point Q, and x _i is the coordinate on the horizontal axis of the cheekbone feature point I.

Then, the processor 120 may determine jaw line type information as a pointed jaw line type based on the jaw line ratio.

Here, the pointed jawline type may be a jawline type indicating that the jawline is sharp.

Specifically, the processor 120 may determine the jaw line type information as the long jaw line type when the jaw line ratio is less than or equal to the reference jaw line ratio.

Here, the reference jaw line ratio may be 0.1.

Meanwhile, the processor 120 of the facial analysis apparatus 100 using facial feature points according to another embodiment may correct the user's facial image based on the deformation type information.

To this end, the facial analysis apparatus 100 using facial feature points according to another embodiment includes transformation type information indicating a type to be changed among eyebrow type information, Jeontaekung type information, and glabellar type information according to a current facial image. can be obtained

For example, when the eyebrow type information determined according to the current face image is a straight eyebrow type, the deformation type information may be a standard eyebrow type different from the eyebrow type information.

In this case, the processor 120 may correct the facial image by changing one or more of the position of the eyes, the shape of the eyes, the position of the eyebrows, and the shape of the eyebrows so that the facial image becomes the type indicated by the deformation type information.

Continuing the above example, when the eyebrow inclination of the facial image in which the eyebrow type information is determined as the straight eyebrow type is equal to or less than the first reference inclination, but the standard eyebrow type is obtained as the deformation type information, the processor 120 determines that the eyebrow inclination is The facial image may be corrected by changing at least one of the position of the eyes, the shape of the eyes, the position of the eyebrows, and the shape of the eyebrows so that the slope exceeds the first reference slope and is less than the second reference slope.

Then, the processor 120 may control the output unit 130 to output the corrected facial image to correspond to the deformation type information on the screen.

Through this, the user can preview a facial image after performing plastic surgery and plastic surgery according to the type of type to be changed among the eyebrow type, the front arch type, and the glabella type before performing plastic surgery and plastic surgery.

So far, the present invention has been mainly looked at with respect to preferred embodiments. Those skilled in the art to which the present invention belongs will understand that the present invention can be implemented in a modified form without departing from the essential characteristics of the present invention. Therefore, the disclosed embodiments should be considered from a descriptive point of view rather than a limiting point of view. The scope of the present invention is shown in the claims rather than the foregoing description, and all differences within the equivalent scope should be construed as being included in the present invention.

As described above, although the present invention has been described by the limited embodiments and drawings, the present invention is not limited thereto, and the technical spirit of the present invention and the following by those skilled in the art to which the present invention belongs Of course, various modifications and variations are possible within the scope of equivalents of the claims to be described.

100: Facial analysis device using facial feature points
110: input unit
120: processor
130: output unit
140: Ministry of Communication
150: storage unit

Claims (11)

an input unit that receives a user's facial image; and
A processor for generating an eyebrow outline function representing coordinates of each of a plurality of feature points representing features of the shape of the user's face and an eyebrow outline function representing the outline of the eyebrows based on the facial image;
The processor
Using at least one of a plurality of feature point coordinates and the eyebrow outline function, numerical information is calculated that is used to determine feature type information representing each type of the user's features and reflects the shape of the features, and based on the numerical information, the feature type information is calculated. determine the feature type information;
The processor
A plurality of relative positions of eyebrow peaks, eyebrow inclination, eyebrow curvature, and eyebrow thickness ratio representing the relative position of the user's eyebrow peak relative to the position of one or more of the eyes and eyebrows using at least one of the plurality of feature point coordinates and the eyebrow outline function. as the numerical information, and eyebrow type information indicating the type of the user's eyebrows based on at least one of the plurality of eyebrow peak relative positions, the eyebrow inclination, the eyebrow curvature, and the eyebrow thickness ratio is converted into the feature type information. decide on one of the
The processor
Among the plurality of feature points, a straight line connecting an upper feature point and a lower feature point representing the upper face and lower face of the user, respectively, is set as a vertical axis, and a horizontal axis perpendicular to the vertical axis is set,
The processor
calculating a vertebrae ratio using at least one of the plurality of feature point coordinates and the eyebrow outline function, and determining a vertebrae type information indicating a type of a vernacular arch of the user based on the vernacular arch ratio;
The processor
Among the plurality of feature points, a third vertical line passing through the snow mountain feature point representing the snow mountain and a fourth intersection point at which the lower outline intersects among the outlines of the eyebrow are extracted, and among the plurality of feature points, an eye tail feature point representing the tail of the eye and the eye head representing the eye head are extracted. Calculate a ninth distance on the horizontal axis between feature points, calculate a tenth distance between the Nunshan feature point and a fourth intersection, and calculate a third distance ratio of the tenth distance to the ninth distance as the vertex arch ratio; , Calculate the Jeon Taekung ratio using Equation 6 below,
The processor
If the total palace ratio is less than the first reference palace ratio, the palace type information is determined as a small palace type indicating that the palace is narrow, and if the total palace ratio exceeds the second reference palace ratio, the Decide the type information of the palace as the type of palace that represents the wideness of the palace,
The processor
A plurality of relative lengths between the eyebrows are calculated using at least one of the plurality of feature point coordinates and the eyebrow outline function, and a first relative length between the eyebrows and a second relative length between the eyebrows are calculated as the plurality of relative lengths between the eyebrows, Based on the relative length between the eyebrows of the user, determining the eyebrow type information indicating the type of the user's eyebrows;
The processor
An eleventh distance on the horizontal axis is calculated between the eyebrow head feature point representing the eyebrow head and the vertical axis among the plurality of feature points, and an 11th distance on the horizontal axis is calculated between the eyebrow tail feature point representing the eyebrow tail and the eyebrow head feature point among the plurality of feature points. A 12th distance is calculated, the 12th distance is reduced by a preset magnification to be calculated as a corrected distance, and a third distance difference between the 11th distance and the corrected distance is calculated as the relative length between the first eyebrows. Calculate the relative length between the first eyebrows using Equation 7;
The processor
Among the plurality of feature points, a first vertical line passing through the eyebrow feature point and a first intersection point at which an upper outline intersects among the outlines of the eyebrow are extracted, and a 13th distance on the vertical axis is calculated between the first intersection point and the horizontal axis, A fourth distance difference between the 11th distance and the 13th distance is calculated as the second glabella relative length, and the second glabellar relative length is calculated using Equation 8 below,
The processor
When the first relative length between the eyebrows exceeds the reference relative length, the between-glabellar type information is determined as a between-glabellar type indicating narrow glabellar length, and when the second relative length between the eyebrows exceeds the reference relative length, the between-glabellar type information is determined as Decide on the type of Daemyeon that represents this wideness,
The processor
A total philtrum ratio is calculated using at least one of the plurality of feature point coordinates and the eyebrow outline function, and total philtrum type information indicating a user's philtrum type is determined based on the total philtrum ratio,
The processor
A 14th distance on the vertical axis is calculated between the upper feature point and the lower feature point, and a 15th distance on the vertical axis is calculated between a nose tip feature point representing the tip of the user's nose and an upper lip feature point representing the top of the user's upper lip among the plurality of feature points. and calculating a fourth distance ratio of the 15th distance to the 14th distance as the total weight ratio, using Equation 9 below to calculate the total weight ratio,
The processor
If the total weight ratio is greater than or equal to the reference total weight ratio, determining the total weight type information as a chapter total weight type indicating the length of the user's face compared to the vertical length of the user's face;
The processor
A chin-thrust ratio is calculated using at least one of the plurality of feature point coordinates and the eyebrow outline function, and chin-thrust type information indicating a user's chin-throw type is determined based on the chin-throw ratio,
The processor
Among the plurality of feature points, a 16th distance on the vertical axis is calculated between a lower lip feature point representing the bottom of the lower lip of the user and the lower feature point, and a fifth distance ratio of the 15th distance to the 16th distance is the chin-in-the-chin ratio. calculated as,
The processor
If the chin throttle ratio is greater than or equal to the reference chin throttle ratio, the chin throttle type information is determined as a long chin throttle type representing the length of the chin compared to the vertical length from the bottom of the user's lower lip to the bottom of the face,
The processor
Obtaining transformation type information representing a type to be changed among the determined eyebrow type information, the Jeontaekung type information, and the eyebrow type information;
The processor
The user's face image is corrected based on the transformation type information, but at least one of the eye position, eye shape, eyebrow position, and eyebrow shape is changed so that the face image becomes the type indicated by the transformation type information. Correcting the facial image;
The processor
Characterized in that the output unit is controlled so that the corrected facial image is output on the screen so that the user can check the facial image after plastic surgery and plastic surgery in advance.
A facial analysis device using facial feature points.

<Equation 6>

Here, C6 is the Jeon Taekung ratio, R3 is the third distance ratio, L9 is the ninth distance, L10 is the tenth distance, L(x _r ) is the coordinate on the vertical axis of the fourth intersection point Z4, and y _r is the coordinate on the vertical axis of the eye feature point R, x _h is the coordinate on the horizontal axis of the feature point H of the outer corner of the eye, and x _r is the coordinate on the horizontal axis of the feature point R of the eye.
<Equation 7>

Here, C7 is the first glabellar relative length, D3 is the third distance difference, L11 is the 11th distance, L12' is the correction distance, L12 is the 12th distance, and j is a preset magnification (e.g., 3 ), x _c is the coordinate on the horizontal axis of the eyebrow head feature point (C), x _e is the coordinate on the horizontal axis of the eyebrow tail feature point (E), and x _f is the coordinate on the horizontal axis of the eyebrow head feature point (F).
<Equation 8>

Here, C8 is the relative length between the second eyebrows, D4 is the fourth distance difference, L11 is the 11th distance, L13 is the 13th distance, x _c is the coordinate on the horizontal axis of the feature point C of the eyebrow head, and U(x _f ) is the coordinate on the vertical axis of the first intersection point Z1.
<Equation 9>

Here, C9 is the total weight ratio, R4 is the fourth distance ratio, L14 is the 14th distance, L15 is the 15th distance, y _a is the coordinate on the vertical axis of the upper feature point (A), and y _q is the lower y m is the coordinate on the vertical axis of the feature point Q, y _m is the coordinate on the vertical axis of the feature point M of the tip of the nose, and y _n is the coordinate on the vertical axis of the feature point N of the upper lip.
delete delete According to claim 1,
The processor
Among the plurality of feature points, a first vertical line passing through the eye head feature point representing the eye head and a first intersection point where the upper outline intersects among the outlines of the eyebrows are extracted, and the eyebrow peak feature point representing the eyebrow peak and the eyebrow peak feature point among the plurality of feature points are extracted. Characterized in that the slope of the first straight line connecting the first intersection is calculated as the slope of the eyebrow
A facial analysis device using facial feature points.
According to claim 4,
The processor
A first distance is calculated between the eyebrow peak feature point and the first intersection point on the horizontal axis, a second distance is calculated between the upper outline and the first straight line on the vertical axis, and the second distance is compared to the first distance. Characterized in that for calculating the first distance ratio of the eyebrow curvature
A facial analysis device using facial feature points.
According to claim 4,
The processor
A second intersection point at which the lower outline intersects is extracted from among the first vertical line and the outline of the eyebrow, a third distance is calculated between the first intersection point and the second intersection point, and the second vertical line passing through the feature point of the eyebrow ridge and the A third intersection point where the lower outline intersects is extracted, a fourth distance is calculated between the eyebrow peak feature point and the third intersection point, and a second distance ratio of the fourth distance to the third distance is calculated as the eyebrow thickness ratio. characterized by
A facial analysis device using facial feature points.
According to claim 1,
The processor
Among the plurality of feature points, a first midpoint on the horizontal axis is extracted between the eye head feature point representing the head of the eye and the eyebrow tail feature point representing the eyebrow tail, and the horizontal axis between the eyebrow peak feature point representing the eyebrow ridge and the vertical axis among the plurality of feature points. A fifth distance on the image is calculated, a sixth distance between the first midpoint on the horizontal axis and the vertical axis is calculated, and a first distance difference between the fifth distance and the sixth distance is calculated as the relative positions of the plurality of eyebrow peaks. Characterized in that it is calculated as the relative position of the first eyebrow ridge among
A facial analysis device using facial feature points.
According to claim 7,
The processor
Among the plurality of feature points, a second midpoint on the horizontal axis is extracted between the eyebrow head feature point representing the eyebrow head and the eyebrow tail feature point, a seventh distance is calculated between the eyebrow head feature point on the horizontal axis and the vertical axis, and the horizontal axis An eighth distance between the second midpoint and the vertical axis is calculated, a distance sum between the seventh distance and the eighth distance is calculated, and a second distance difference between the fifth distance and the distance sum is calculated in the plurality of distances. Characterized in that it is calculated as the relative position of the second eyebrow ridge among the relative positions of the eyebrow ridge
A facial analysis device using facial feature points.
According to claim 1,
The processor
If the eyebrow slope is less than or equal to the first reference slope, the eyebrow thickness ratio is greater than or equal to the reference thickness ratio, and the first eyebrow peak relative position among the plurality of eyebrow peak relative positions is greater than or equal to the reference relative position, the eyebrow type information is determined as a straight eyebrow type. And, if the eyebrow slope is less than or equal to the first reference slope and the eyebrow thickness ratio is less than or equal to the reference thickness ratio, the eyebrow type information is determined as a semi-arch type eyebrow type.
A facial analysis device using facial feature points.
According to claim 1,
The processor
If the eyebrow slope is greater than or equal to a first reference slope and the eyebrow curvature is greater than or equal to the reference curvature, the eyebrow type information is determined as an arcuate eyebrow type, and if the eyebrow slope is greater than or equal to the first reference slope and the eyebrow curvature is less than the reference curvature, the eyebrow type information is determined. Characterized in determining the type information as an angular eyebrow type
A facial analysis device using facial feature points.
◈Claim 11 was abandoned when the registration fee was paid.◈ According to claim 1,
The processor
If the eyebrow slope exceeds the first reference slope and is less than the second reference slope greater than the first reference slope, the eyebrow type information is determined as a standard type eyebrow type, and the eyebrow slope is greater than or equal to the second reference slope and the plurality of eyebrows Characterized in that if the relative position of the second eyebrow ridge among the relative positions of the mountains is equal to or greater than the reference relative position, the eyebrow type information is determined as a rising eyebrow type.
A facial analysis device using facial feature points.

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