KR20220155967A - Server apparatus and method for recommending cloth thereby - Google Patents

Abstract

According to an embodiment of the present invention, disclosed is a clothing recommendation method by a server device, which includes the steps of: receiving scan data of a user's body from a user terminal or a scanning device; determining a user's body type based on the received scan data; selecting clothing corresponding to the determined body type; and transmitting information on the selected clothing to a user terminal. Therefore, it is possible for a user to more easily select the clothing which suit him/herself.

Description

Server device and clothing recommendation method using the same {SERVER APPARATUS AND METHOD FOR RECOMMENDING CLOTH THEREBY}

The present invention relates to a server device and a method for recommending clothes using the same. More specifically, the present invention relates to a server device for recommending customized clothes that can suit a user and a method for recommending clothes using the same.

Recently, the use of online shopping is increasing due to the development of Internet technology. Users can easily purchase desired goods or services through computers, smartphones, and the like. In particular, in the case of clothes, since accessibility to offline stores, which are mainly located in department stores, is not good compared to other products, users purchase a lot of clothes through online shopping.

However, when clothes are purchased online, very low conversion rates and high return rates are shown due to users' dissatisfaction with fittings. In order to improve this, virtual fitting technology has appeared, but the process of purchasing clothes in online shopping itself is not efficient, and users still have a problem in that it is difficult to find clothes that suit them.

A server device and a method for recommending clothes according to an embodiment of the present invention are aimed at allowing a user to more easily select clothes that suit him/herself.

Another object of the server device and the method for recommending clothes according to an embodiment of the present invention is to reduce a user's return rate in online shopping and improve the rate of return of an online server that sells clothes.

A clothing recommendation method according to an embodiment of the present invention includes receiving 3D scan data obtained by photographing a user's body from a user terminal or a scanning device; obtaining second 2D scan data representing the front of the user from the received 3D scan data, and determining a body type of the user based on feature points extracted from the second 2D scan data; selecting clothing corresponding to the determined body type; and transmitting information on the selected clothing to the user terminal, wherein the feature point includes a shoulder point (SP) of the user extracted from a contour of a second body image of the second 2D scan data. In the step of determining the body type of the user, the second body image is divided into a plurality of closed curves, an armpit point (AP) is determined in consideration of the planar shape of the divided closed curves, and an armpit point is located in the vertical direction of the determined AP. A point located on the contour of the second body image among the points to be used may be determined as the SP.

Here, the determining of the body type of the user may include extracting an elbow point (EP) and a waist point (WP) of the user from the contour of a second body image of the second 2D scan data; and comparing the positions of the user's WP and EP to determine the user's body type.

Here, the method of recommending clothes further includes determining a body type corresponding to each of a plurality of clothes, and the selecting step includes selecting clothes corresponding to the determined body type from among the plurality of clothes. Further steps may be included.

Here, the selecting of the clothes may include selecting a predetermined number of clothes from among the plurality of clothes in order of preference of the users, corresponding to the determined body type.

Here, the clothing recommendation method may further include transmitting a web page in which clothing categories are classified according to body types to the user terminal.

Here, the clothing recommendation method further includes transmitting a message requesting rescanning to the user terminal or the scanning device when the shape of the user's body image included in the user's scan data is different from a preset shape. can do.

Here, the clothing recommendation method may include obtaining a BMI index of the user calculated based on the user's height and weight; and determining a degree of obesity of the user according to the calculated BMI, wherein the selecting may include selecting the clothes in consideration of the degree of obesity.

Some of the effects that can be achieved by the server device and the clothing recommendation method according to the embodiment of the present invention are as follows.

i) Users can more easily select clothing that suits them.

ii) A user's return rate in online shopping can be reduced.

iii) The profit rate of online servers that sell clothes can be improved.

However, the effects that can be achieved by the server device and the clothing recommendation method according to an embodiment of the present invention are not limited to those mentioned above, and other effects not mentioned are to which the present invention belongs from the description below. It will be clearly understood by those skilled in the art.

1 is an exemplary diagram illustrating a user terminal, a server device, and a scanning device according to an embodiment of the present invention.
2 is a flowchart illustrating a method for recommending clothes according to an embodiment of the present invention.
3 is an exemplary diagram illustrating types of user's body types.
4 is a flowchart illustrating a method of determining a first type of body type of a user.
5(a) and 5(b) are diagrams for explaining a method of extracting a user's BP, WP, and HP.
6 is a flowchart illustrating a method of determining a first type of body type of a user through comparison of WHR and WCR.
7 is a diagram for explaining another method of determining a first type of body type of a user.
8(a) to 8(d) are views for explaining a method of determining an AP of a second body image.
9 is a diagram for explaining a method of determining a second type of body type of a user.
10 is a diagram for explaining another method of determining a second type of body type of a user.
11 is a block diagram showing the configuration of a server device according to another embodiment of the present invention.

Since the present invention can make various changes and have various embodiments, specific embodiments are illustrated in the drawings and will be described in detail through detailed description. However, this is not intended to limit the present invention to specific embodiments, and it should be understood that the present invention includes all modifications, equivalents, and substitutes included in the spirit and scope of the present invention.

In describing the present invention, if it is determined that a detailed description of related known technologies may unnecessarily obscure the subject matter of the present invention, the detailed description will be omitted. In addition, numbers (eg, 1st, 2nd, etc.) used in the description process of this specification are only identifiers for distinguishing one component from another component.

In addition, in this specification, when one component is referred to as “connected” or “connected” to another component, the one component may be directly connected or directly connected to the other component, but in particular Unless otherwise described, it should be understood that they may be connected or connected via another component in the middle.

In addition, in this specification, a component expressed as '~ unit (unit)', 'module', etc. means a hardware component such as software, FPGA, or ASIC, and this component performs certain roles. However, a component is not meant to be limited to software or hardware. A component may be configured to reside in an addressable storage medium. Also, two or more components may be combined into one component, or one component may be divided into two or more components for each more subdivided function. In addition, each of the components to be described below may additionally perform some or all of the functions of other components in addition to its own main function, and some of the main functions of each component may be different from other components. Of course, it may be performed exclusively by a component.

On the other hand, in this specification, 'clothing' as worn by humans, may include outerwear, underwear, socks, hats, gloves, scarves, etc., but is not limited thereto.

Hereinafter, exemplary embodiments according to the technical idea of the present invention will be described with reference to the drawings.

1 is an exemplary diagram illustrating a user terminal 100, a server device 200, and a scanning device 300 according to an embodiment of the present invention.

The user terminal 100 is a device for purchasing clothes through the server device 200, and the user terminal 100 is a smartphone, a desktop computer, a notebook computer, a PDA, a web pad, a tablet PC, and a wearable device. Any device capable of communicating with the server device 200 through a predetermined network may include various types of terminal devices.

The server device 200 may include a web server, and for example, the server device 200 is a device used to sell clothes online or to mediate sales. The server device 200 may transmit clothing information to the user terminal 100 in the form of a web page.

The user terminal 100 and the server device 200 shown in FIG. 1 are connected to each other through a network. The network may include a wired network and a wireless network, and specifically, various networks such as a local area network (LAN), a metropolitan area network (MAN), and a wide area network (WAN). can include Also, the network may include the well-known World Wide Web (WWW). However, the network according to the present invention is not limited to the networks listed above, and may include at least a part of a known wireless data network, a known telephone network, or a known wired/wireless television network.

The scanning device 300 may scan the body of the user 10 . Scan data generated by the scan device 300 may be transmitted to the server device 200 through a network. If the scan device 300 does not have a network communication function, the scan data generated by the scan device 300 is transmitted to the user terminal 100 through a separate storage medium, and the user terminal 100 stores the scan data. It can be transmitted to the server device 200 through a network. The scanning device 300 may have a voice output function or an image output function for guiding the scanning posture of the user 10 .

The server device 200 recommends clothing tailored to the user 10 to the user 10 through scan data transmitted from the user terminal 100, which will be described with reference to FIGS. 2 and 3 .

FIG. 2 is a flowchart illustrating a method for recommending clothes according to an embodiment of the present invention, and FIG. 3 is an exemplary diagram showing types of body types of the user 10 .

In step S210, the server device 200 receives scan data of the user 10's body from the user terminal 100 or the scan device 300. The scan data may include, for example, 3D scan data. When the user terminal 100 accesses the server device 200 and registers as a member through a web page, the server device 200 may request transmission of scan data to the user terminal 100 .

When the shape of the body image of the user 10 included in the scan data of the user 10 is different from the preset shape, the server device 200 sends a message requesting re-photographing to the user terminal 100 or the scan device ( 300). For example, when scanning is performed with the user 10 standing upright and with both arms and torso separated to some extent, but with the user 10 holding both arms above the head, the server The device 200 requests rescanning to the user terminal 100 or the scanning device 300 .

In step S220, the server device 200 determines the body type of the user 10 based on the scan data received from the user terminal 100.

3 shows the types of body types of the user 10. For example, the body types of the user 10 are classified into a first type body type, a second type body type, and a third type body type. It can be. All body types determined based on the same data (eg, feature points) belong to the same type.

The first type of body type may include a rectangular type, a triangle type, an inverted triangle type, an oval type, and an hourglass type. The first type of body type may be determined based on a breast point (BP), a hip point (HP), and a waist point (WP).

The second type of body type may include a short leg type, a balanced type, and a long leg type, and the second type of body type is based on WP and the height of the user 10, or EP (elbow point) and WP. can be determined by

The third type of body type may include an underweight type, an average weight type, an overweight type, and an obese type, and the third type of body type may be determined based on the height and weight of the user 10 . The third type of body type may indicate the degree of obesity.

The body type of the first type, the body type of the second type, and the body type of the third type are just examples, and the server device 200 is the body type of the user 10, At least one of a body type of the type and a body type of the third type may be determined.

In step S230, the server device 200 selects clothing corresponding to the body type of the user 10.

The server device 200 may previously store body information suitable for each garment as information on a plurality of clothes. For example, information on the silhouette of each garment may be designated by a clothing maker or the like, and information on which silhouette of clothing suits each body type may be designated by a fashion expert or the like. Specifically, when a clothing maker designates the silhouette of a dress he or she has produced as 'sheath' and a fashion expert designates that a 'sheath' silhouette dress suits the user 10 with an inverted triangle body shape, the server device 200 When the body type of the user 10 is determined to be an inverted triangle, a 'sheath' silhouette dress can be selected as clothing suitable for the user 10 .

The server device 200 may select clothes corresponding to the body type determined in step S220 from among a plurality of clothes, or may select clothes corresponding to the body type determined in step S220 from among the plurality of clothes, in order of preference of the users 10. You can also select a certain number of clothes as desired. Several users 10 may input preferences for clothes recommended by the server device 200 through their user terminals 100 .

In step S240 , the server device 200 may transmit information about the clothing selected in step S230 , for example, an image of clothing and price information of the clothing to the user terminal 100 .

The server device 200 may generate a 3D avatar of the user 10 using scan data of the user 10. In step S240, the server device 200 virtually fits the 3D image of the selected clothing to the 3D avatar. and may be transmitted to the user terminal 100. Since a method of virtual fitting by generating a 3D image of a 3D avatar of the user 10 and clothes is obvious to those skilled in the art, a detailed description thereof will be omitted herein.

The user terminal 100 may determine whether or not to purchase the corresponding clothing by referring to a clothing image virtually fitted to the user's 3D avatar and price information of the clothing. When the user terminal 100 requests to purchase clothing, the server device 200 proceeds with a payment process for the user terminal 100 or forwards the connection of the user terminal 100 to a separate sales server. , the server can proceed with the payment process.

Meanwhile, when the server device 200 transmits a web page including a plurality of clothes information to the user terminal 100, the server device 200 may classify the plurality of clothes information according to body type and include them in the web page. For example, the server device 200 includes clothing suitable for body type A in the category of body type A, clothing suitable for body type B in the category of body type B, and clothing suitable for body type C in category C. It can be included in the category of body type.

According to the conventional clothing web page, clothing is classified based on gender (male, female, child, etc.), type of clothing (jeans, skirt, etc.), etc. There is a problem that makes it difficult.

Since the server device 200 according to an embodiment of the present invention classifies clothing categories for each body type, the user 10 can easily check clothing information that suits him/her well if he/she knows only his or her body type. .

Next, a method of determining the first type of body type of the user 10 will be described with reference to FIGS. 4 to 6 .

4 is a flowchart illustrating a method of determining the first type of body type of the user 10, and FIGS. 5 (a) and 5 (b) are for explaining a method of extracting the user's BP, WP, and HP. it is a drawing 6 is a flowchart illustrating a method of determining the first type of body type of the user 10 through comparison of WHR and WCR.

In step S410, the server device 200 obtains first 2D scan data representing the side of the user 10 from the 3D scan data of the user 10. The server device 200 may obtain first 2D scan data by projecting the 3D scan data in the direction of the side of the user 10 . 5(a) shows a first body image 510 of first 2D scan data obtained from 3D scan data.

In step S420, the server device 200 extracts feature points of the user 10 from the contour of the first body image of the first 2D scan data. At this time, the feature points of the user 10 are BP (breast point) ), a hip point (HP) and a waist point (WP).

Referring to FIG. 5( b ), the server device 200 determines a method of determining BP, HP, and WP. First, the server device 200 determines a reference line R passing through the inside of the first body image 510. and three regions 520a, 520b, and 520c for extracting BP 532, HP 534, and WP 536 are set in the first body image.

The server device 200 creates three regions 520a, 520b, and 520c, that is, a chest region 520a and a waist region 520b, in the first body image 510 using various methods used in the field of 3D body modeling. and the buttocks area 520c may be set. For example, the server device 200 may obtain first 2D scan data representing the side of the user 10 or second 2D scan data representing the front of the user 10, obtained from the 3D scan data of the user 10. Three regions 520a, 520b, and 520c may be set in the first body image 510 of the user 10 by applying the superquadric technique to the scan data. When three areas are set in the second body image of the second 2D scan data, the first body image 510 is obtained by mapping the three areas to the first body image 510 of the first 2D scan data. Also, three areas 520a, 520b, and 520c can be set. Since the superquadric technique is described in 'Building Superquadric Men from 3D Whole-Body Scan Data', Yijun Xiao and J. Paul Siebert, etc., a detailed description thereof will be omitted.

Next, the server device 200 considers the distance between the contours 512 and 514 of the first body image 510 and the reference line R, in one direction of the reference line R, for example, in the forward direction. The BP 532 is extracted from the contour 512 of the first body image 510 located there. Specifically, in the chest area 520a set for the first body image 510, the distance from the reference line R among the contours 512 of the first body image 510 located in one direction of the reference line R. (d1) may extract the farthest point to the BP 532.

The server device 200 considers the distance between the contours 512 and 514 of the first body image 510 and the reference line R, and the first body image 510 is positioned in the other direction of the reference line R, for example, in the rearward direction. 1 HP 534 is extracted from the outline 514 of the body image 510. Specifically, in the hip area 520c set for the first body image 510, the distance from the reference line R among the contours 514 of the first body image 510 located in the other direction of the reference line R. (d2) can extract the farthest point as HP 534.

Finally, the server device 200 considers the contours 512 and 514 of the first body image 510, and the first body image 510 positioned in the other direction of the reference line R, for example, in the rearward direction. The WP 536 is extracted from the contour 514 of ). Specifically, in the waist region 536 set for the first body image 510, a point having the greatest curvature among the contours 514 of the first body image 510 located in the other direction of the reference line R is WP (536) can be extracted.

In step S430, the server device 200 measures a chest circumference including BP, a waist circumference including WP, and a hip circumference including HP from the 3D scan data.

In step S440, the server device 200 determines the size of the waist to hip ratio (WHR) and the waist to chest ratio (WCR) of the user ( 10) determines the first type of body type.

6 illustrates a method of determining the first type of body type of the user 10 through comparison of WHR and WCR. First, in step S605, it is determined whether the WHR is greater than the value obtained by adding δ1 to the WCR. In step S610, when the WHR is greater than the value obtained by adding δ1 to the WCR, the server device 200 determines the body type of the user 10 as a triangle type.

In step S615, if the WHR is not greater than the value obtained by adding δ1 to the WCR, the server device 200 determines whether the WCR is greater than the value obtained by adding δ1 to the WHR. In step S620, when the WCR is greater than the value obtained by adding δ1 to the WHR, the server device 200 determines the body type of the user 10 as an inverted triangle type.

In step S625, if the WCR is not greater than the value obtained by adding δ1 to the WHR, the server device 200 has a chest circumference (C) greater than the value obtained by adding δ2 to the waist circumference (W), and the waist circumference (W) It is determined whether the hip circumference (H) is greater than the value obtained by adding δ2.

If the chest circumference (C) is greater than the value obtained by adding δ2 to the waist circumference (W) and the hip circumference (H) is greater than the value obtained by adding δ2 to the waist circumference (W), in step S630, the server device 200 determines the body type of the user 10 as an hourglass shape.

If the chest circumference (C) is not greater than the value obtained by adding δ2 to the waist circumference (W), or the hip circumference (H) is not greater than the value obtained by adding δ2 to the waist circumference (W), in step S635, the server device ( 200) determines whether the chest circumference is larger than the waist circumference and the hip circumference is larger than the waist circumference.

In step S640, if the chest circumference is greater than the waist circumference and the hip circumference is greater than the waist circumference, the body type of the user 10 is determined as a rectangle, and in step S645, the chest circumference is greater than the waist circumference. If it is not large or the hip circumference is not larger than the waist circumference, the body type of the user 10 is determined as an elliptical shape.

Meanwhile, δ1 and δ2 of FIG. 6 may vary according to nationality information, age information, and gender information of the user 10 . The server device 200 may more accurately determine the body type of the user 10 by obtaining nationality information, age information, and gender information of the user 10 and determining δ1 and δ2 corresponding to the acquired information. For example, δ1 and δ2 of the user 10 of Western nationality may be determined to be greater than δ1 and δ2 of the user 10 of Asian nationality.

Meanwhile, in order to determine the first type of body type of the user 10, the waist width and shoulder width of the user 10 may be used. This will be described with reference to FIGS. 7 and 8 .

7 is a diagram showing a second body image 710 of the second 2D scan data representing the front of the user 10, and FIGS. 8(a) to 8(d) show the second body image 710 It is a diagram for explaining a method of determining an armpit point (AP) of

As shown in FIG. 7 , the server device 200 generates two WPs 720a from the second body image 710 of the second 2D scan data of the user 10 obtained from the 3D scan data of the user 10. , 720b) and two SPs (730a, 730b) are extracted, and then the user ( 10) can determine the first type of body type. For example, if the shoulder width w2 is greater than the value obtained by adding a predetermined value to the waist width w1, the body type of the user 10 may be determined to be an inverted triangle type.

The server device 200 may further consider the hip width together with the waist width w1 and the shoulder width w2 to determine the first type of body type of the user. For example, by substituting the ratio between the waist width (w1) and the shoulder width (w2) into the WCR of FIG. 6 and the ratio between the waist width (w1) and the hip width (w2) into the WHR of FIG. 6, the user The first type of body type can be determined.

After determining the waist area in the second body image 710 of the second 2D scan data, the server device 200 may determine two WPs 720a and 720b based on the curvature of the contour. Depending on the implementation, the server device 200 determines the WP in the first body image of the first 2D scan data as in the method described above with reference to FIG. 5 (b), and then draws a line 722 passing through the determined WP. After mapping to the second body image 710 of the second 2D scan data, a point where the mapped line 722 and the contour of the second body image 710 meet is determined as two WPs 720a and 720b. may be

In order to acquire two SPs 730a and 730b from the second body image 710 , first, armpit points (APs) 740a and 740b of the second body image 710 must be extracted. The second body image 710 shown in FIG. 8(a) is divided into a plurality of closed curves as shown in FIG. have. As shown in FIG. 8(c), the fact that the plane shape is made up of three closed curves means that the corresponding plane shape represents the both arms and torso of the user 10, so the topmost of the planar shapes made of three closed curves The APs 740a and 740b may be determined in portions corresponding to the flat shape where they are located.

Referring to FIG. 7 again, the server device 200 assigns points located on the outline of the second body image 710 among points located in the vertical direction of the determined two APs 740a and 740b to two SPs 730a and 730b. ) can be determined.

FIG. 9 is a diagram for explaining a method of determining the second type of body type of the user 10. Referring to FIG.

The server device 200 extracts the HP 920 from the first body image 910 as first 2D scan data through the method described with reference to FIG. 5(b). Next, the server device 200 determines the distance H1 between the top end 930 and the bottom end 940 of the first body image 910, the HP 920 and the bottom end 940 of the first body image 910. ) to determine the second type of body type of the user 10 by comparing the distance H2 between them.

For example, when (H1/2) is greater than H2, the server device 200 determines the body type of the user 10 as the short leg type, and when (H1/2) is equal to H2, the user 10 ) is determined as a balanced type, and when (H1/2) is smaller than H2, the body type of the user 10 may be determined as a long leg type.

FIG. 10 is a diagram for explaining another method of determining the second type of body type of the user 10 .

For another method for determining the second type of body type of the user 10, as shown in FIG. 10, the user 10 should be scanned in a straight posture with both arms bent.

The server device 200 extracts the WP 1030 of the user 10 from the second body image 1010 of the second 2D scan data representing the front of the user 10 . Since the method of extracting the WP 1030 from the 2D scan data representing the front of the user has been described with reference to FIG. 7 , a detailed description thereof will be omitted.

In addition, the server device 200 extracts an elbow point (EP) 1020 of the user 10 from the second body image 1010 of the second 2D scan data showing the front of the user 10 . As described with reference to FIG. 8 , the server device 200 divides the second body image 1010 shown in FIG. 10 into a plurality of closed curves and extracts the EP 1020 by considering the planar shape of the divided closed curves. can do. Since the number of closed curves is reduced from three to one below the point where the EP (1020) exists, EP (1020) can be extracted according to the positions of the closed curves on both sides of the three closed curves when the closed curves are reduced from three to one. can

The server device 200 may determine the body type of the user 10 by comparing the positions of the WP 1030 and the EP 1020. For example, the server device 200 may determine the second body image 1010 By comparing the distance (H3) between the lowest end (1040) and the EP (1020) and the distance (H4) between the lowest end (1040) and the WP (1030), if H3 is greater than H4, short leg type, H3 and H4 are the same If the balance type and H3 are smaller than H4, it can be determined as a long-grass type.

Meanwhile, the server device 200 may obtain the height and weight of the user 10 and determine the third type of body type of the user 10 using the BMI index calculated from the height and weight of the user 10. have. For example, the server device 200 compares the BMI index of the user 10 with preset values to determine whether the user 10 is an underweight type, an average weight type, an overweight type, or an obese type. Since the method of determining the degree of obesity of the user 10 using the BMI index is self-evident, a detailed description thereof will be omitted.

11 is a block diagram showing the configuration of a server device 1100 according to another embodiment of the present invention.

Referring to FIG. 11 , a server device 1100 according to another embodiment of the present invention may include a communication unit 1110, a storage unit 1130, and a control unit 1150.

The communication unit 1110 communicates with the user terminal 100 through a network. The communication unit 1110 may transmit, to the user terminal 100, a web page in which a plurality of pieces of clothing information are classified according to body types. The communication unit 1110 receives scan data obtained by photographing the body of the user 10 from the user terminal 100 or the scanning device 300 . Also, the communication unit 1110 may transmit information about clothes selected as suitable for the body type of the user 10 by the control unit 1150 to the user terminal 100 .

The storage unit 1130 may store information on a plurality of clothes, for example, at least one of a price, a silhouette of each of the plurality of clothes, information on which body type suits them, and a 3D image. In addition, the storage unit 1130 may further store preference information of users 10 for each body type mapped as suitable for each of a plurality of clothes.

The controller 1150 determines the body type of the user 10 based on the received scan data, and selects clothing corresponding to the determined body type. Since the method for determining the body type of the user 10 and selecting clothes corresponding to the body type has been described above, a detailed description thereof will be omitted.

Meanwhile, it has been described so far that the server device 200 or 1000 according to an embodiment of the present invention determines the user's body type in order to recommend clothing to the user. However, depending on the implementation, the server device 200 or 1000 determines the user's body type by using the feature points extracted from the scan data and body measurements related thereto (eg, waist circumference, waist width, etc.) , It can be used for other purposes, for example, for medical diagnosis purposes or for transmission to a medical server.

A server device and a method for recommending clothes according to an embodiment of the present invention enable the user 10 to more easily select clothes that suit him/herself. In addition, the server device and the method for recommending clothes according to an embodiment of the present invention can reduce the return rate of the user 10 in online shopping and improve the rate of return of the online server that sells clothes.

Meanwhile, the above-described embodiments of the present invention can be written as a program that can be executed on a computer, and the written program can be stored in a medium.

The medium may include a storage medium such as a magnetic storage medium (eg, ROM, floppy disk, hard disk, etc.) and an optical reading medium (eg, CD-ROM, DVD, etc.), but is not limited thereto.

Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains know that the present invention can be implemented in other specific forms without changing the technical spirit or essential features. You will understand. Therefore, the embodiments described above should be understood as illustrative in all respects and not limiting.

100: user terminal
200, 1100: server device
300: scan device
1110: communication department
1130: storage unit
1150: control unit

Claims (8)

Receiving 3D scan data of a user's body from a user terminal or scanning device;
obtaining second 2D scan data representing the front of the user from the received 3D scan data, and determining a body type of the user based on feature points extracted from the second 2D scan data;
selecting clothing corresponding to the determined body type; and
Transmitting information on the selected clothing to the user terminal,
The feature point includes a shoulder point (SP) of the user extracted from the contour of the second body image of the second 2D scan data,
The step of determining the user's body type is
The second body image is divided into a plurality of closed curves, an armpit point (AP) is determined in consideration of the planar shape of the divided closed curve, and points located in the vertical direction of the determined AP are located on the contour of the second body image. A clothing recommendation method characterized by determining a point as the SP.
According to claim 1,
Determining the body type of the user,
extracting an elbow point (EP) and a waist point (WP) of the user from the contour of the second body image of the second 2D scan data; and
and determining the user's body type by comparing locations of the user's WP and EP.
According to claim 1,
The clothing recommendation method,
For each of the plurality of garments, determining a corresponding body type;
The selection step is
and selecting clothes corresponding to the determined body type from among the plurality of clothes.
According to claim 3,
The step of selecting the clothing,
and selecting a predetermined number of clothes from among the plurality of clothes corresponding to the determined body type and in order of preference of users.
According to claim 1,
The clothing recommendation method,
The clothing recommendation method further comprising transmitting a web page in which clothing categories are classified according to body types to the user terminal.
According to claim 1,
The clothing recommendation method,
and transmitting a message requesting rescanning to the user terminal or the scanning device when the shape of the user's body image included in the user's scan data is different from the predetermined shape. Way.
According to claim 1,
The clothing recommendation method,
obtaining a BMI index of the user calculated based on the user's height and weight; and
Further comprising the step of determining the degree of obesity of the user according to the calculated BMI,
The selection step is
and selecting the clothing by further considering the degree of obesity.
A program stored in a medium to execute the clothing recommendation method according to any one of claims 1 to 7 in combination with hardware.

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