TW202016881A - Program, information processing device, quantification method, and information processing system - Google Patents

Abstract

Provided is a program for causing a computer to function to output a value acquired by analyzing moving image data and quantifying a makeup action of a person shown in the moving image data. The program causes the computer to function as: a first detection means for detecting a face area in which a face of the person is shown from the moving image data; a second detection means for detecting a hand area in which hands of the person are shown from the moving image data; and an output means for outputting a value acquired by quantifying a makeup action of the person shown in the moving image data from movements in the face area and the hand area on the basis of the detected face area and hand area.

Description

Program, information processing device, quantitative method and information processing system

The invention relates to a program, an information processing device, a quantitative method and an information processing system.

As we all know, makeup effects vary depending on the cosmetics and makeup techniques used. Historically, most users of makeup rarely have the opportunity to see the makeup techniques of others. In recent years, many animations (make-up animations) of shooting makeup have been shared on animation websites and other websites. Through this kind of makeup animation, the chances of the user confirming the makeup technique are increasing (for example, refer to Non-Patent Document 1). <Prior Technical Documents> <Non-patent literature>

Non-Patent Document 1: "Animation Course", [online], Shiseido Co., Ltd. [retrieved on April 20, 2018], Internet (URL: https://www.shiseido.co.jp/beauty/dictionary/lesson /index.html)

<Problems to be solved by the invention>

By viewing the above-mentioned makeup animation, the user can see the cosmetics and makeup actions used by the characters reflected in the makeup animation. However, it is difficult for the user to correctly imitate the makeup movement of the character by watching only the makeup movement of the character reflected in the makeup animation. In this regard, if the makeup movement can be quantified based on the makeup animation, it is easy to compare the makeup movement of the user who imitated with the makeup movement of the character reflected in the makeup animation, and it is convenient to use.

An object of an embodiment of the present invention is to provide a program capable of quantifying the makeup actions of characters reflected in animation data. <Technical means to solve problems>

In order to achieve the above object, a program according to an embodiment of the present invention is a program for causing a computer to perform a function of analyzing animation data and outputting a value that quantifies the makeup movement of a person reflected in the animation data. The program causes the computer to function as the following elements: the first detection element detects the facial area reflecting the character's face from the animation data; the second detection element detects the hand reflecting the character's hand from the animation data Area; the output element, based on the detected face area and the hand area, outputs a value quantifying the makeup movement of the person reflected in the animation data based on changes in the face area and the hand area. <Effect of invention>

According to an embodiment of the present invention, it is possible to quantify the makeup actions of the characters reflected in the animation material.

Hereinafter, embodiments of the present invention will be described in detail.

[First embodiment] <System structure> 1A and 1B are configuration diagrams of an example of the information processing system of this embodiment. The information processing system of FIG. 1A includes a single information processing device 1. The information processing device 1 is a PC operated by a user, a smartphone, a tablet computer, a dedicated machine for quantifying makeup movements for home or business use, and the like.

In addition, in the information processing system of FIG. 1B, one or more client terminals 2 and the server device 3 are connected via a network 4 such as the Internet. The client terminal 2 is a terminal device such as a PC, smart phone, or tablet computer operated by a user, or a dedicated device for quantifying makeup actions for home or business use. The server device 3 performs processing related to quantification of makeup actions performed in the client terminal 2 and the like.

As described above, the present invention can be applied to the single information processing device 1 shown in FIG. 1A in addition to the client/server type information processing system shown in FIG. 1B. Here, the information processing system of FIG. 1A and FIG. 1B is just an example, and of course various system configuration examples are possible depending on the purpose and purpose. For example, the server device 3 of FIG. 1B may be composed of a plurality of distributed computers.

<Hardware structure> The information processing device 1, the client terminal 2 and the server device 3 of FIGS. 1A and 1B are realized by, for example, a computer with a hardware structure shown in FIG. 2. FIG. 2 is a hardware configuration diagram of an example of the computer of this embodiment.

The computer of FIG. 2 includes an input device 501, an output device 502, an external I/F 503, a RAM 504, a ROM 505, a CPU 506, a communication I/F 507, an HDD 508, etc., and is connected via a bus B, respectively.

The input device 501 is a keyboard, a mouse, etc. for input operation. The output device 502 is composed of a display such as a liquid crystal or organic EL capable of displaying a screen, a microphone for outputting audio materials such as sound or music, and the like. The communication I/F507 is an interface for connecting a computer to the network 4. HDD508 is an example of a non-volatile memory device used to store programs or data.

The external I/F503 is an interface to connect with external devices. The computer can read and/or write to the recording medium 503a through the external I/F 503. The recording medium 503a may be a DVD, SD memory card, USB memory, or the like.

The CPU 506 is a computing device that reads programs and data from a memory device such as a ROM 505 or a HDD 508 to the RAM 504 and performs processing to realize overall control and functions of the computer. The information processing device 1, the client terminal 2 and the server device 3 of the present embodiment can realize various functions by executing a program in a computer having the above-mentioned hardware structure.

Here, FIG. 2 is an example of a hardware structure, and of course various structure examples can be formed according to the use and purpose. For example, in the computer of FIG. 2, the input device 501 may also have a camera function capable of shooting movies.

<Discussion on quantification of makeup movements> As a method of quantifying makeup actions, for example, there is a method using a sensor. In the method of quantifying the makeup movement using the sensor, the person who does makeup wears the sensor to perform the makeup movement. In the method of quantifying the makeup movement using the sensor, the makeup movement of the person can be quantified based on the data output from the sensor, but the makeup movement of the person reflected in the makeup animation after shooting cannot be quantified .

If you can analyze the makeup animation after shooting and quantify the makeup movements of the characters reflected in the makeup animation, you can use the makeup animation of websites such as animation websites without the need to wear a sensor, etc. Expect to quantify natural makeup movements. Here, in this embodiment, in order to quantify the makeup movements of the people reflected in the makeup animation after shooting, it is discussed about grasping the object acquired from the makeup animation and using the object to quantify the makeup movement.

＜＜Mastering objects acquired from makeup animation＞＞ In order to grasp the objects obtained from the makeup animation, the movement capture (device) was used to measure the changes in the makeup movement of the subject. Here, a total of 6 measurement sites are the tip of the right middle finger, the root of the middle finger, the center of the back of the hand, the center of the wrist, the elbow, and the forehead. The analysis objects are the coordinates (displacement), velocity, acceleration, and angular velocity of the measurement site. The main component of the subject's variation after the above analysis is estimated to be the main variation element in the makeup movement.

3A and 3B are distribution diagrams of an example of measurement results. FIG. 3A is a distribution diagram showing an example of the fineness of the hand movement in each makeup movement and the interlocking property of the hand and the elbow. 3B is a distribution diagram showing an example of the fineness of hand movements and facial changes for each makeup movement. As shown in FIGS. 3A and 3B, the main components of the subject’s changes are the fineness of hand movements, the interlocking of the hands and elbows, and facial and facial movements. Here, in this embodiment, the object acquired from the makeup animation is set to the coordinates and speed of the hand and the coordinates and speed of the face.

<<Quantization of makeup actions using acquired objects>> As a method of obtaining the coordinates and speed of the hand and the coordinates and speed of the face in the makeup movement of the character reflected in the makeup animation, a diagram using a Convolutional Neural Network (hereinafter referred to as CNN) can be mentioned. Like recognition. In image recognition using CNN, the face area and hand area can be detected from the two-dimensional image. Therefore, by tracking the face area and hand area detected from the frame image of the makeup animation (tracking ), it is possible to obtain the coordinates and speed of the hand and the coordinates and speed of the face in the makeup movement of the character reflected in the makeup animation. The image recognition using CNN in this embodiment will be described in detail below.

＜Software structure＞ ＜＜Function Block＞＞ The following describes the software structure of the information processing system of this embodiment. Here, the information processing device 1 shown in FIG. 1A will be described as an example. FIG. 4 is a functional block diagram of an example of the information processing system of this embodiment. The information processing device 1 implements the functions of the operation acceptance unit 10, the area detection unit 12, the quantification unit 14, the post-processing unit 16, and the animation data storage unit 18 by executing a program.

The operation acceptance unit 10 accepts various operations from the user. The animation data memory section 18 memorizes makeup animation. Here, the animation data storage unit 18 may be provided outside the information processing device 1. The makeup animation stored in the animation data storage section 18 and the makeup animation shot by the camera function are input to the area detection section 12. The area detection unit 12 performs the following detection of the face area and hand area of the person reflected in the frame image for each frame image constituting the input makeup animation.

The quantification unit 14 acquires the coordinates and speed of the hand and the coordinates and speed of the face in the makeup movement of the character reflected in the makeup animation from the face area and the hand area detected by the area detection unit 12 The makeup actions of the characters reflected in are quantified. The post-processing unit 16 performs post-processing on the processing results of the area detection unit 12 and the quantization unit 14 and outputs them to the output device 502 or the like.

For example, the post-processing unit 16 performs post-processing on a face area and a hand area in a makeup operation in which a person reflected in a makeup animation is surrounded by a rectangle. In addition, the post-processing unit 16 visually expresses the fineness of the hand movement, the linkage between the hand and the elbow, and the coordinate and speed of the hand based on the makeup movement of the character reflected in the makeup animation. Post-processing of facial changes, etc.

In addition, the post-processing unit 16 can quantify and compare the makeup actions of the characters shown in the two makeup animations, and output the comparison result. For example, a user who uses the information processing system of the present embodiment can easily understand the difference from his own makeup technology by quantifying and comparing his own makeup movements and makeup movements of people with high makeup skills such as makeup artists. Thus, the information processing system of this embodiment can provide services for improving the user's makeup technology.

The area detection unit 12 of FIG. 4 for detecting the face area and the hand area of the person reflected in the frame image of the makeup animation has, for example, the configuration shown in FIG. 5. 5 is a configuration diagram of an example of an area detection unit. The area detection unit 12 of FIG. 5 includes a framing unit 20, a face area detection unit 22, a hand area detection unit 24, and a face feature point detection unit 26.

The framing unit 20 supplies the input makeup animation to the face area detection unit 22 and the hand area detection unit 24 in units of frame images. The face region detection unit 22 includes a face region learning model including a face organ region learning model.

Here, through machine learning using a two-dimensional image in which a hand region overlaps a face region as teaching data, a face region learning model included in the face region detection unit 22 is created. Based on the two-dimensional image in which the hand is reflected as the foreground of the face, the teaching material in which the hand area overlaps the face area is made. Relative to the annotated (created as teaching material) hand region learning data set (data set), with the hand as the foreground, the image of the annotated face region learning data set is attached, and the hand region can be made to overlap the face region Teaching materials.

The face area detection unit 22 uses a face area learning model that performs CNN learning to implement a face area detection that has a strong effect on the overlap of the face area and the hand area. The CNN learning data set uses a part of the face area to be blocked by the hand area The teaching materials of the state (shading the environment).

The hand area detection unit 24 has a hand area learning model including a fingertip position area learning model. By using the two-dimensional image of the hand at the time of makeup as a teaching material, a hand region learning model included in the hand region detection unit 24 is created.

Here, the teaching material of the hand during makeup is made of an annotation hand region learning material set specialized in the shape of the hand during makeup, and an annotation fingertip position region learning data set specialized in the position of the fingertip during makeup. The hand area detection unit 24 uses a hand area learning model that performs CNN learning to realize hand area detection with high detection accuracy of the hand and fingertip positions during shape changes. The CNN learning materials focus on the above teaching materials .

In addition, the facial feature point detection unit 26 has a facial feature point learning model that includes a facial organ feature point learning model. The facial feature point detection unit 26 detects facial feature points of the entire face by using the facial feature point learning model. Then, the facial feature point detection unit 26 detects the facial feature points of the facial organs for each part by learning the model using the facial organ feature points. The facial feature point detection unit 26 detects the facial feature points of the eyes included in the facial organs, and corrects the positions of the facial feature points (including the outline) of the parts other than the eyes based on the positions of the facial feature points of the eyes, so that High-precision facial feature point detection can also be achieved in low-resolution or shaded environments.

<Processing> ＜＜Face area detection and facial feature point detection＞＞ The area detection unit 12 detects the face area of the person reflected in the frame image, for example, as shown in FIG. 6. 6 is a conceptual diagram of an example of detection processing of a face area of a person reflected in a frame image.

The face area detection section 22 of the area detection section 12 detects the face area of the person's face reflecting the frame image 1000 in the rectangle 1002 by using the aforementioned face area learning model. The face area detection unit 22 detects the face organ area from the area of the rectangle 1002 using the above-mentioned face organ area learning model, and corrects the rectangle ratio of the detected rectangle 1002 centered on the nose to form the rectangle 1004.

The facial feature point detection unit 26 uses the aforementioned facial feature point learning model to detect the facial feature points shown in the rectangular area image 1006 from the area of the rectangle 1004. In addition, the facial feature point detection unit 26 uses the facial organ feature point learning model to detect the feature points of the facial organs shown in the rectangular area image 1008 for different parts from the rectangular area image 1006.

Here, by performing the processing shown in the flowchart of FIG. 7, the area detection unit 12 of FIG. 5 can realize high-precision facial feature point detection even in a low-resolution and shaded environment. 7 is a flowchart of an example of face feature point detection processing.

In step S11, the facial feature point detection unit 26 of the region detection unit 12 detects the facial feature points using the above facial feature point learning model from the facial region (whole face) detected by the facial region detection unit 22, and estimates the head posture.

Proceeding to step S12, the facial feature point detection unit 26 considers the posture of the head estimated in step S11, and uses the facial organ feature point learning model to detect the eyes detected by the face region detection unit 22, thereby detecting the position of the eyes The speculation is corrected to improve the accuracy of the speculative position of the eye.

Proceeding to step S13, the facial feature point detection unit 26 considers the estimated eye position corrected in step S12, and uses the above facial organ feature point learning model to detect feature points (including contours) of facial organs other than the eye Correct the estimated position of other facial organs. The processing of the flowchart of FIG. 7 is effective, for example, in the case where the facial contour is blocked by the hand.

＜＜Hand area detection＞＞ The area detection unit 12 detects the hand area of the person reflected in the frame image, for example, as shown in FIG. 8. 8 is a conceptual diagram of an example of detection processing of a person's hand region reflected in a frame image.

The hand region detection unit 24 of the region detection unit 12 detects the hand regions of the left and right hands of the person reflecting the frame image 1100 by using the above-mentioned hand region learning model. The hand area detection unit 24 uses the above-mentioned fingertip position area learning model to detect the left and right hand areas 1112 and 1114 of the person in the frame image 1110 and the fingertip positions 1116 and 1118 of the left and right hands from the area of the rectangle 1102.

＜＜output＞＞ The information processing device 1 of the present embodiment can calculate and output the fineness of hand movements and the linkage between the hands and elbows based on, for example, the coordinates and speed of the hand and the coordinates and speed of the face in the makeup movement of the person reflected in the makeup animation Sexual and facial changes. The fineness of the above-mentioned hand movements, the linkage between the hands and elbows, and the output of facial changes can be used to study makeup movements.

In addition, the information processing device 1 of the present embodiment can quantify and compare the makeup movements of the characters reflected in the two makeup animations. Therefore, the makeup movements of users who want to learn makeup techniques and makeup techniques such as makeup artists can be improved. The makeup actions of the characters are quantified and compared. The comparison result may be scored and provided to the user, or the user may be visually reminded of the difference in makeup action between the user and a person with high makeup skills such as a makeup artist. In addition, the information processing device 1 of the present embodiment may also present the makeup technique to the user based on the comparison result, so that the makeup action of the user is close to the makeup action of a person with high makeup technique such as a makeup artist.

For example, when applying cosmetic products for wide area application such as blush/foundation, the information processing device 1 of the present embodiment can provide the user with guidance on application area determination and application range. In addition, the information processing device 1 of the present embodiment can provide the user with correctness and error judgment and guidance when using cosmetic products that are more difficult in techniques such as eyeliner/eye shadow/concealer. In addition, the information processing device 1 of the present embodiment can also provide the user with a method of applying a hair wax (hairdressing product), a method of applying a skin care product, or a method of massaging.

In addition, makeup methods such as blush (rouge) application methods vary according to users with sharp bones and round users. Therefore, the information processing device 1 of the present embodiment can also provide recommended makeup suitable for the user’s face shape. Technical guidance for the makeup.

[Second Embodiment] In the information processing system of the second embodiment, the configuration is the same as the information processing system of the first embodiment except for a part of the configuration, so it is appropriate to omit the description. In the configuration of the information processing system of the second embodiment, the area detection unit 12 of FIG. 5 is replaced with the area detection unit 12 of FIG. 9. 9 is a configuration diagram of an example of an area detection unit. The structure of the area detection unit 12 in FIG. 9 includes a framing unit 50, a skin color area extraction unit 52, an area division unit 54, a face area detection unit 56, a hand area detection unit 58, a force field calculation unit 60, and a channel calculation unit 62 .

The framing unit 50 supplies the input makeup animation to the skin-color area extraction unit 52 in units of frame images. The skin color area extraction unit 52 extracts the skin color area from the frame image. The region dividing unit 54 divides the skin color region extracted by the skin color region extracting unit 52 into candidate blocks, and marks the candidate blocks. The area division unit 54 supplies the marked candidate blocks to the face area detection unit 56 and the hand area detection unit 58.

The face area detection unit 56 classifies the candidate blocks of the face area (detects the face area) based on the markers of the candidate blocks (the characteristics of the divided skin-color area) provided above. In addition, the hand area detection unit 58 selects the candidate block provided by the area division unit 54 (the characteristics of the divided skin color area), the candidate block of the face region classified by the face area detection unit 56, and the candidate region of the hand region Blocks are classified (detect hand area).

As shown in FIG. 10, the face region detection unit 56 and the hand region detection unit 58 of FIG. 9 first classify candidate regions of the face region, remove the candidate regions of the face region, and detect by the hand region Part 58 classifies candidate blocks in the opponent area. Therefore, in this embodiment, erroneous detection of the hand area can be prevented.

When the face area detection unit 56 cannot classify the candidate blocks of the face area, the force field calculation section 60 determines that the face area and the hand area have interfered, and performs processing as described below. The force field calculation unit 60 is provided with candidate blocks of the face area and hand area of the previous frame (t-1) and marked candidate blocks of the current frame (t).

As shown in FIG. 11, the force field calculation unit 60 sets a plurality of channels in the image of the candidate block based on the force field (Force Field). The channel calculation unit 62 calculates the moving distance with respect to the previous frame for each channel, and sets the candidate block of the channel with a large moving distance as the candidate block of the moving hand, thereby enabling the candidate area of the opponent area and the face area Block to classify.

Here, in addition to the magnitude of the moving distance, the force field calculation unit 60 and the channel calculation unit 62 of the area detection unit 12 can further prevent the hand area and the face area by clustering changes of similar hand areas in advance Error detection of candidate blocks.

(to sum up) As described above, according to the present embodiment, it is possible to quantify the makeup movement of the person reflected in the makeup animation after shooting without providing a sensor or the like, and provide or teach makeup techniques. The present invention is not limited to the above specifically disclosed embodiments, and various modifications or changes can be made as long as they do not deviate from the scope of the patent application. For example, in the present embodiment, two-dimensional animation data has been described as an example, and three-dimensional animation data may also be used. According to this embodiment, through the same data analysis as the two-dimensional animation data, or combining the analysis of the two-dimensional animation data and the analysis of the three-dimensional information, it is possible to quantify and provide or instruct the makeup actions of the characters reflected in the three-dimensional animation data. Makeup technique.

The present invention has been described above based on the embodiments. However, the present invention is not limited to the above embodiments, and various modifications can be made within the scope of the description of the patent application. This application claims priority based on the basic application No. 2018-199739 filed with the Japanese Patent Office on October 24, 2018, and refers to the entire contents of the basic application.

1: Information processing device 2: client terminal 3: server device 4: Internet 10: Operation acceptance department 12: Area detection department 14: Quantification Department 16: Post-processing department 18: Animation Data Memory Department 20: Framing Department 22: Face area detection section 24: Hand area detection department 26: Facial feature point detection unit 50: Framing Department 52: Skin color area extraction section 54: Area division 56: Face area detection section 58: Hand area detection department 60: Force Field Calculation Department 62: Channel calculation department

FIG. 1A is a configuration diagram of an example of an information processing system of this embodiment. FIG. 1B is a configuration diagram of an example of the information processing system of this embodiment. FIG. 2 is a hardware configuration diagram of an example of the computer of this embodiment. FIG. 3A is a distribution diagram of an example of measurement results. Fig. 3B is a distribution diagram of an example of measurement results FIG. 4 is a functional block diagram of an example of the information processing system of this embodiment. 5 is a configuration diagram of an example of an area detection unit. 6 is a conceptual diagram of an example of detection processing of a face area of a person reflected in a frame image. 7 is a flowchart of an example of face feature point detection processing. FIG. 8 is a conceptual diagram of an example of detection processing of a hand region of a person reflected in a frame image. 9 is a configuration diagram of an example of an area detection unit. 10 is a conceptual diagram of an example of processing by a face area detection unit and a hand area detection unit. 11 is a conceptual diagram of an example of processing by a force field calculation unit and a channel calculation unit.

1: Information processing device

10: Operation acceptance department

12: Area detection department

14: Quantification Department

16: Post-processing department

18: Animation Data Memory Department

Claims (11)

A program that enables a computer to analyze animation data and output a value that quantifies the makeup actions of the characters reflected in the animation data. The program is used to make the computer function as the following components: The first detection element detects the facial area reflecting the character's face from the animation data; the second detection element detects the hand area reflecting the character's hand from the animation data; and the output element is based on the detected The face area and the hand area output values that quantify the makeup movement of the person reflected in the animation data based on the changes in the face area and the hand area. The program of claim 1 is characterized in that the first detection element is provided with: a facial area detection element that uses a facial area learning model to detect the person's facial area from the animation data; and a facial organ area detection element that uses a face An organ feature point learning model to detect facial organ regions from the facial region, and the second detection element is provided with: a hand region detection element, a hand region learning model that uses the makeup action, and detects the character's hand region from the animation data; And a fingertip position area detection element, which uses the fingertip position area learning model of the makeup movement to detect the fingertip position area from the hand area. The program of claim 2 is characterized in that the facial region learning model is a convolutional neural network that uses the teaching data of the state where a part of the facial region is blocked by the hand region for learning in the facial region learning data set. The program of claim 2 is characterized in that the hand region learning model is a convolutional neural network that uses the teaching data of the hand shape when applying makeup to the hand region learning data set for learning, and the fingertip location region learning model is The teaching data of the fingertip position during makeup is used in the convolutional neural network for learning in the fingertip position area learning data set. The program of claim 1 is characterized in that it also makes the computer function as the following components: extracting components, extracting skin color regions from the animation data; and segmenting components, segmenting the skin color regions into segmented regions, the first detection The element detects the facial area reflecting the face of the person from the skin color area based on the feature amount of the segmented area, and the second detection element removes the facial area reflecting the face of the person based on the feature amount of the segmented area In the skin-color area other than the detected skin-color area, a hand area reflecting the person's hand is detected. The program of claim 5 is characterized in that the computer also functions as a third detection element in the case where the first detection element cannot detect the facial area reflecting the face of the person from the skin color area , Detecting the moving distance of the divided area between the frame images constituting the animation data, and assuming that the divided area with the larger moving distance is the hand area reflecting the character's hand. The program of claim 1 is characterized in that the output element uses the coordinates and speed of the hand and the coordinates and speed of the face in the makeup movement as the values quantifying the makeup movement of the character reflected in the animation data Output. The program of claim 1 is characterized in that the output element outputs a visual representation of the two of the animation data based on the comparison result of the quantified values of the makeup actions of the characters reflected in the two of the animation data. Image showing the difference in the makeup actions of the characters. An information processing device that analyzes animation data and outputs a value that quantifies the makeup actions of the characters reflected in the animation data. The information processing device includes: a first detection element that detects and reflects from the animation data The facial area of the character's face; the second detection element, which detects the hand area reflecting the character's hand from the animation data; and the output element, based on the detected facial area and the hand area, the output is based on the facial area And the change in the hand area is a value that quantifies the makeup movement of the character reflected in the animation data. A quantitative method that performs analysis of animation data in an information processing device and outputs a value that quantifies the makeup movement of the person reflected in the animation data. The quantitative method includes: the first detection step, Detecting the facial area reflecting the character's face from the animation data; the second detection step, detecting the hand area reflecting the character's hand from the animation data; and the output step, based on the detected facial area and the The hand area outputs a value that quantifies the makeup movement of the person reflected in the animation data based on the changes in the face area and the hand area. An information processing system includes a client terminal and a server device, the client terminal accepts operations from a user, the server device analyzes animation data according to the operations accepted by the client terminal from the user, and outputs the animation data The quantified value of the makeup action of the person reflected in the image is provided to the client terminal. The server device includes: a receiving element that receives information about operations received by the client terminal from the user; the first detection element is based on The operation accepted by the user detects the facial area reflecting the face of the character from the animation data; the second detection element detects the hand area reflecting the hand of the character from the animation data based on the operation accepted from the user; An output element, based on the detected face area and the hand area, outputs a value that quantifies the makeup movement of the character reflected in the animation data based on changes in the face area and the hand area; and the sending element will output The value of is sent to the client terminal.

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