Eyebrow shape management method, wearable device and storage medium

Abstract

The invention relates to the technical field of virtual reality and augmented reality, and provides an eyebrow shape management method, wearable equipment and a storage medium, and the method comprises the steps: scanning the head of a user, and determining a first facial form type of the user; determining a recommended eyebrow shape based on a preset recommendation rule and the first facial form type; calculating an eyebrow shape matching coefficient of the recommended eyebrow shape and the current eyebrow shape; and generating eyebrow shape management suggestion information of the current eyebrow shape based on the eyebrow shape matching coefficient. According to the method, photo input or video input is not needed, and the efficiency and precision of eyebrow shape management can be effectively improved.

Description

Technical Field

[0001] This application relates to the fields of virtual reality and augmented reality technology, and in particular to an eyebrow management method, wearable device, and storage medium. Background Technology

[0002] Eyebrow shaping, as an important part of shaping facial features and overall makeup, has always received much attention from the fashion industry and the general public.

[0003] With the development of technology, users can use a range of professional beauty software to detect and evaluate their eyebrow shape and receive eyebrow shaping advice. However, this eyebrow management method usually requires photo or video input, which is inefficient and inaccurate. Summary of the Invention

[0004] In view of this, this application provides an eyebrow management method, a wearable device, and a storage medium to solve the problems of low efficiency and accuracy of existing eyebrow management methods.

[0005] The first aspect of this application provides an eyebrow management method applied to a wearable device. The eyebrow management method includes: scanning a user's head to determine the user's first face shape type; determining a recommended eyebrow shape based on a preset recommendation rule and the first face shape type; calculating an eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape; and generating eyebrow shape management suggestion information for the current eyebrow shape based on the eyebrow shape matching coefficient.

[0006] In some embodiments, scanning the user's head to determine the user's first face type includes: scanning the user's head to obtain the user's digital face; determining the second face type of the digital face based on the first face type parameters of the digital face; and determining the user's first face type based on the second face type.

[0007] In some embodiments, determining the second face type of the digital face based on the first face type parameters of the digital face includes: obtaining the second face type parameters of a reference face in a preset face type library; calculating a face type matching coefficient between the digital face and the reference face based on the first face type parameters and the second face type parameters; matching a target reference face corresponding to the digital face from the preset face type library according to the face type matching coefficient; and determining the second face type according to the third face type of the target reference face.

[0008] In some embodiments, determining the recommended eyebrow shape based on preset recommendation rules and the first face shape type includes: matching a target reference eyebrow shape corresponding to the first face shape type from a preset eyebrow shape library according to a preset correspondence, wherein the preset correspondence includes the correspondence between face shape type and reference eyebrow shape; and determining the recommended eyebrow shape based on the user's preferred eyebrow shape and the target reference eyebrow shape.

[0009] In some embodiments, determining the recommended eyebrow shape based on the user's preferred eyebrow shape and the target reference eyebrow shape includes: if the eyebrow shape type of the user's preferred eyebrow shape matches that of the target reference eyebrow shape, then using either the user's preferred eyebrow shape or the target reference eyebrow shape as the recommended eyebrow shape; if the eyebrow shape type of the user's preferred eyebrow shape does not match that of the target reference eyebrow shape, then prompting the user to determine the recommended eyebrow shape from the user's preferred eyebrow shape and the target reference eyebrow shape.

[0010] In some embodiments, calculating the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape includes: obtaining a first eyebrow shape parameter of the current eyebrow shape and a second eyebrow shape parameter of the recommended eyebrow shape; and calculating the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape based on the first eyebrow shape parameter and the second eyebrow parameter.

[0011] In some embodiments, the first eyebrow shape parameters include the first eyebrow length, the first eyebrow width, and the first radius of curvature of the current eyebrow shape; the second eyebrow shape parameters include the second eyebrow length, the second eyebrow width, and the second radius of curvature of the recommended eyebrow shape; the step of calculating the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape based on the first eyebrow shape parameters and the second eyebrow shape parameters includes: calculating a first difference parameter between the first eyebrow length and the second eyebrow length, a second difference parameter between the first eyebrow width and the second eyebrow width, and a third difference parameter between the first radius of curvature and the second radius of curvature according to preset rules; and calculating the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape based on the first difference parameter, the second difference parameter, and the third difference parameter.

[0012] In some embodiments, generating eyebrow management suggestion information for the current eyebrow shape based on the eyebrow shape matching coefficient includes: if the eyebrow shape matching coefficient is not within a preset coefficient range, generating eyebrow management suggestion information for the current eyebrow shape based on the recommended eyebrow shape.

[0013] A second aspect of this application provides a wearable device including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor implements the above-described eyebrow management method when executing the computer-readable instructions.

[0014] A third aspect of this application provides a computer-readable storage medium storing computer-readable instructions that, when executed by a processor, implement the above-described eyebrow management method.

[0015] This application provides an eyebrow management method that uses a wearable device to scan the user's head and determine the user's primary face shape type. Based on preset recommendation rules and the primary face shape type, a recommended eyebrow shape is determined. The recommended eyebrow shape represents the optimal eyebrow shape suitable for the user's face shape. By calculating the eyebrow shape matching coefficient between the current eyebrow shape and the recommended eyebrow shape, the difference between the two is determined, thereby generating eyebrow management suggestions for the current eyebrow shape. This method eliminates the need for photo or video input, and by determining the recommended eyebrow shape based on preset recommendation rules and the user's primary face shape type, and generating eyebrow management suggestions based on the eyebrow shape matching coefficient between the current eyebrow shape and the recommended eyebrow shape, it effectively improves the efficiency and accuracy of eyebrow management. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is an example diagram illustrating an application scenario of the eyebrow management method provided in this application embodiment.

[0018] Figure 2 This is an example diagram of another application scenario of the eyebrow management method provided in the embodiments of this application.

[0019] Figure 3 This is a flowchart illustrating the implementation of the eyebrow management method provided in this application embodiment.

[0020] Figure 4 This is an example image of a user's face provided in an embodiment of this application.

[0021] Figure 5 This is an example diagram of the preset face library provided in the embodiments of this application.

[0022] Figure 6 This is an example diagram of the current eyebrow shape provided in the embodiments of this application.

[0023] Figure 7 This is an example diagram of the preset eyebrow shape library provided in the embodiments of this application.

[0024] Figure 8 This is a schematic diagram of the eyebrow management device provided in the embodiments of this application.

[0025] Figure 9 This is a schematic diagram of the structure of the wearable device provided in the embodiments of this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this application's specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It should be understood that, unless otherwise stated, " / " in this application means "or". For example, A / B can mean A or B. "And / or" in this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. "At least one" refers to one or more. "More than one" refers to two or more. For example, at least one of a, b, or c can represent: a, b, c, a and b, a and c, b and c, and a, b, and c (seven cases).

[0029] Please see Figure 1 The diagram shown is an example of an application scenario for the eyebrow management method provided in this embodiment. Figure 1As shown, a wearable device 100 is worn on the user's head 200. The wearable device 100 includes a camera module, a display module, and an eye-tracking module. The camera module consists of an infrared (IR) emitter 111 and an IR receiver 112. The display module includes a first display screen 121 and a second display screen 122. The eye-tracking module includes a light-emitting diode (LED) emitter 131 and an IR camera 132.

[0030] In the process of eyebrow management, the wearable device 100, through an IR transmitter 111 and an IR receiver 112, uses structured light technology to acquire a three-dimensional structural image of the user's head 200. Based on this image, a digital face shape is generated, determining the user's primary face shape type. After determining the primary face shape, the wearable device 100 determines a recommended eyebrow shape based on preset recommendation rules and the primary face shape type. The recommended eyebrow shape can be represented as the optimal eyebrow shape suitable for the user's face shape. To determine whether the user's current eyebrow shape needs adjustment or how to adjust it, the wearable device 100 calculates the eyebrow shape matching coefficient between the recommended and current eyebrow shapes, and generates eyebrow management suggestions based on this coefficient. The wearable device 100 uses these suggestions to prompt the user to trim their eyebrows and provides trimming advice. This method effectively improves the efficiency and accuracy of eyebrow management.

[0031] In some embodiments, the wearable device may display eyebrow management suggestions through the first display screen 121 and / or the second display screen 122, or may provide eyebrow management suggestions through voice prompts. This application embodiment does not limit the prompting method for eyebrow management suggestions.

[0032] In some embodiments, such as Figure 2 As shown, the wearable device 100 can capture the user's current eyebrow shape through the LED emitter 131 of the eye-tracking module and the IR camera 132.

[0033] In some embodiments, the wearable device 100 can be smart glasses, such as virtual reality (VR) glasses, augmented reality (AR) glasses, mixed reality (MR) glasses, etc. The camera in the camera module of the wearable device 100 can be a depth infrared camera.

[0034] Figure 1The scenarios shown are merely illustrative examples, and the device control method provided in this application can also be applied to other scenarios. For example, in some scenarios, the wearable device 100 may also include a microphone; in some scenarios, the wearable device 100 may also include an information processing module; in some scenarios, it may also include other types of components or modules. This application does not limit the specific application scenarios of the eyebrow management method.

[0035] Please see Figure 3 The diagram shown is a flowchart illustrating the implementation of the eyebrow management method provided in this application embodiment. This method is applied to wearable devices, and this application embodiment uses this method in… Figure 1 The following explanation will be given using wearable device 100 as an example. The method includes the following steps.

[0036] S11: Scan the user's head to determine the user's primary face shape type.

[0037] In some embodiments, the first face type represents the face type of the user's face. The face type includes, but is not limited to, long face, square face, heart-shaped face, round face, diamond-shaped face, etc. This application embodiment does not limit the face type.

[0038] In some embodiments, different face shapes are typically suited to different eyebrow shapes. Choosing an eyebrow shape that suits one's face shape can better balance facial features and enhance overall aesthetics. For example, long faces are suited to thick, flat eyebrows; square faces to tapering eyebrows; heart-shaped faces to willow-leaf eyebrows; round faces to European-style eyebrows; and diamond-shaped faces to small European-style eyebrows. Therefore, in managing a user's eyebrow shape, wearable devices can determine the user's primary face shape type. Based on this type, the optimal eyebrow shape can be determined, and the user's eyebrows can be managed accordingly, such as by providing eyebrow shaping tips and suggestions.

[0039] In this embodiment, the wearable device can scan the user's head using a camera or video camera to determine the user's primary face type. For example, a depth infrared camera can scan the user's head to obtain a 3D structural image of the user, and a digital human model can be generated based on the 3D structural image. By obtaining the face type of the digital human face in the digital human model, the wearable device can determine the user's primary face type.

[0040] In other embodiments, the wearable device may also acquire the user's facial features based on facial recognition technology and identify the user's first face type based on the facial features.

[0041] In some embodiments of this application, scanning the user's head to determine the user's first face type includes: scanning the user's head to obtain the user's digital face; determining the second face type of the digital face based on the first face type parameters of the digital face; and determining the user's first face type based on the second face type.

[0042] In some embodiments, the first face shape parameters include, but are not limited to, geometric feature parameters of the digital face (e.g., length, width, etc.), color parameters, and face shape proportion parameters (e.g., proportions from eyes to forehead, chin to lips, chin to eyes, etc.).

[0043] In some embodiments, the wearable device can obtain a digital human model corresponding to the user's head by scanning the user's head. The digital face shape corresponding to the digital human model is the same as the user's face shape corresponding to the user's head, so the wearable device can determine the first face shape type of the user's face by determining the second face shape type of the digital face shape.

[0044] In some embodiments, the wearable device can acquire first face shape parameters of the digital face and determine a second face shape type of the digital face based on the first face shape parameters.

[0045] In some embodiments of this application, determining a second face type of a digital face based on a first face type parameter of the digital face includes: obtaining a second face type parameter of a reference face in a preset face type library; calculating a face type matching coefficient between the digital face and the reference face based on the first face type parameter and the second face type parameter; matching a target reference face corresponding to the digital face from the preset face type library according to the face type matching coefficient; and determining the second face type according to a third face type of the target reference face.

[0046] In some embodiments, a preset face shape library stores at least one reference face shape and the face shape type corresponding to each reference face shape. A third face shape type represents the face shape type corresponding to the target reference face shape. Second face shape parameters include, but are not limited to, the geometric feature parameters (e.g., length, width), color parameters, and face shape proportion parameters (e.g., the proportions from eyes to forehead, chin to lips, chin to eyes, etc.) of the reference face shape. The face shape matching coefficient can be used to characterize the degree of similarity between different face shapes. Generally, a higher face shape matching coefficient indicates a higher degree of similarity between different face shapes.

[0047] In this embodiment, the wearable device can compare the digital face with the reference face in the preset face library based on the first face parameters of the digital face and the second face parameters of the reference face in the preset face library. The wearable device can then determine the target reference face that is similar to the digital face from the preset face library. The wearable device can then use the third face type corresponding to the target reference face as the second face type of the digital face, and then determine the first face type of the user or the user's face based on the second face type of the digital face.

[0048] In the process of determining a target reference face that is similar to the digital face from a preset face shape library, the wearable device can obtain the second face shape parameters of the reference faces in the preset face shape library. Based on the first face shape parameters and the second face shape parameters, it calculates the face shape matching coefficient between the digital face and the reference face. After obtaining the face shape matching coefficient between the digital face and each reference face in the preset face shape library, the wearable device can select the reference face corresponding to the largest face shape matching coefficient as the target reference face, and determine the second face shape type of the digital face based on the third face shape type of the target reference face.

[0049] In other embodiments, the wearable device can also set a face shape matching coefficient threshold. When the face shape matching coefficient between the digital face shape and the reference face shape is determined to be greater than the face shape matching coefficient threshold, the wearable device can use the reference face shape as the target reference face shape. The face shape matching coefficient threshold can be customized based on the first face shape parameter and the second face shape parameter.

[0050] In other embodiments, the wearable device may also determine the target reference face shape through other means. This application does not limit the method of determining the target reference face shape.

[0051] In this embodiment, during the process of calculating the face matching coefficient between the digital face and the reference face based on the first face shape parameters and the second face shape parameters, the wearable device can calculate the face matching coefficient according to the geometric feature parameters of the digital face and the geometric feature parameters of the reference face. For example, the face matching coefficient between the digital face and the reference face can be calculated based on the length and width of the digital face and the length and width of the reference face.

[0052] In the process of calculating the face matching coefficient between the digital face and the reference face based on the length and width of the digital face and the reference face, the wearable device can take multiple length values ​​and multiple width values ​​of the digital face, as well as multiple length values ​​and multiple width values ​​of the corresponding positions of the reference face. By calculating the length ratio and width ratio of the digital face and the reference model at each corresponding position, the face matching coefficient between the digital face and the reference face is calculated.

[0053] For example, wearable devices can divide a digital face into multiple parts along both the length and width directions (e.g., perform multiple equal divisions), and also divide a reference face into multiple parts along both the length and width directions (e.g., perform multiple equal divisions). The number of divisions along the length and width directions of the digital face is the same as the number of divisions along the length and width directions of the reference face. For example, if the digital face is divided into four equal parts along both the length and width directions, then the reference face is also divided into four equal parts along both the length and width directions. The wearable device can then match the division lines of the digital face and the reference face one-to-one based on their positions, and calculate the ratio of the corresponding division lines by dividing the smaller value by the larger value. The wearable device can then use the accumulated ratios of these division lines as the face matching coefficient between the digital face and the reference face.

[0054] As an example, such as Figure 4 The digital face shown is divided into four equal parts along its width by lines a, b, and c, and into four equal parts along its length by lines d, e, and f. Figure 5 As shown, a preset face library stores reference faces A, B, C, D, and E. Taking the calculation of the face matching coefficient between the digital face and the reference face A as an example, according to the equal division method of the digital face, the wearable device divides the reference face A into four equal parts in the width direction through the dividing lines a1, b1, and c1, and divides the reference face A into four equal parts in the length direction through the dividing lines d1, e1, and f1. Then, the wearable device can determine the size of a and a1, b and b1, c and c1, d and d1, e and e1, f and f1 respectively. Taking a less than a1, b less than b1, c less than c1, d less than d1, e equal to e1, and f less than f1 as an example, the wearable device can calculate the ratios of a / a1, b / b1, c / c1, d / d1, e / e1 or e1 / e, and f / f1 respectively. The sum of the ratios of a / a1, b / b1, c / c1, d / d1, e / e1 or e1 / e, and f / f1 is used as the face matching coefficient between the digital face and the reference face A.

[0055] In other embodiments, the wearable device can also calculate the face matching coefficient between the digital face and the reference face by calculating the difference between the length difference and the width difference or the weighted difference sum, etc.

[0056] In other embodiments, the wearable device may also calculate a face shape matching coefficient between the digital face shape and the reference face shape based on other parameters in the first face shape parameters and the second face shape parameters. This application does not limit the method used to calculate the face shape matching coefficient between the digital face shape and the reference face shape.

[0057] In some embodiments, before calculating the face matching coefficient between the digital face and the reference face based on the first face parameter and the second face parameter, the wearable device can preprocess the digital face or the reference face, such as aligning the digital face and the reference face in terms of resolution, pixels, etc., to make the digital face and the reference face comparable.

[0058] In this embodiment, the wearable device obtains multiple length values ​​and multiple width values ​​by finely dividing the digital face shape and the reference face shape, and calculates the face shape matching coefficient between the digital face shape and the reference face shape based on the multiple length values ​​and multiple width values, which can effectively improve the face shape matching accuracy.

[0059] It is understood that in other embodiments, the position and number of dividing lines can be set independently. For example, wearable devices can divide the digital face into multiple parts along both the length and width directions, such as dividing the face according to the three courts and five eyes, and corresponding the positions of the dividing lines of the digital face with those of the reference face. This application does not limit the position and number of dividing lines on the face.

[0060] S12: Determine the recommended eyebrow shape based on the preset recommendation rules and the first face shape type.

[0061] In some embodiments, the recommended eyebrow shape refers to the preferred eyebrow shape that suits the user's face shape.

[0062] In some embodiments of this application, determining a recommended eyebrow shape based on preset recommendation rules and a first face shape type includes: matching a target reference eyebrow shape corresponding to the first face shape type from a preset eyebrow shape library according to a preset correspondence; and determining a recommended eyebrow shape based on the user's preferred eyebrow shape and the target reference eyebrow shape.

[0063] In some embodiments, a preset eyebrow shape library stores at least one reference eyebrow shape and the eyebrow shape type of each reference eyebrow shape. The eyebrow shape types include, but are not limited to, thick flat eyebrows, drooping tail eyebrows, willow leaf eyebrows, European-style eyebrows, and small European-style eyebrows. This application does not limit the eyebrow shape types. Typically, the preset eyebrow shape library stores a rich variety of reference eyebrow shapes to ensure the efficiency and accuracy of eyebrow shape management.

[0064] In some embodiments, the preset correspondence includes a correspondence between face shape type and reference eyebrow shape. Face shape types include, but are not limited to, long face, square face, heart-shaped face, round face, diamond-shaped face, etc. This application embodiment does not limit the face shape type.

[0065] Different face shapes are generally suited to different eyebrow shapes. Choosing an eyebrow shape that suits your face shape can better balance facial features and enhance overall aesthetics. For example, long faces suit thick, flat eyebrows; square faces suit droopy eyebrows; heart-shaped faces suit willow-leaf eyebrows; round faces suit European-style eyebrows; and diamond-shaped faces suit small European-style eyebrows. Therefore, wearable devices can pre-establish a preset correspondence between reference eyebrow shapes and face shapes. After determining the user's primary face shape, the wearable device can match a target reference eyebrow shape from a preset eyebrow shape library based on this pre-defined correspondence. The target reference eyebrow shape is one that best suits the user's face shape.

[0066] Furthermore, in order to ensure that the final recommended eyebrow shape meets the user's personalized needs and improve the personalization of the recommended eyebrow shape, wearable devices can obtain the user's preferred eyebrow shape and determine the recommended eyebrow shape based on the user's preferred eyebrow shape and the target reference eyebrow shape.

[0067] In some embodiments, the wearable device may obtain the user's preferred eyebrow shape by: obtaining a user eyebrow shape set, which includes multiple eyebrow shape types; identifying the user's frequently used eyebrow shape from the user eyebrow shape set; and using the user's frequently used eyebrow shape as the user's preferred eyebrow shape.

[0068] Furthermore, after determining the user's preferred eyebrow shape, the wearable device can detect whether the user's preferred eyebrow shape exists in the preset eyebrow shape library. If the user's preferred eyebrow shape does not exist in the preset eyebrow shape library, the wearable device can update the preset eyebrow shape library with the user's preferred eyebrow shape and its corresponding eyebrow type.

[0069] In other embodiments, the wearable device may also obtain the user's preferred eyebrow shape by: acquiring multiple user eyebrow shapes, which are eyebrow shapes that the user has used; determining the user eyebrow shape with high frequency of use from the multiple user eyebrow shapes; comparing the user eyebrow shape with high frequency of use with reference eyebrow shapes in a preset eyebrow shape library, and determining a reference eyebrow shape that is more similar to the user eyebrow shape with high frequency of use; the wearable device may use the user eyebrow shape with high frequency of use or the reference eyebrow shape that is more similar to the user eyebrow shape with high frequency of use as the user's preferred eyebrow shape, and determine the eyebrow shape type of the user's preferred eyebrow shape based on the eyebrow shape type of the reference eyebrow shape that is more similar to the user eyebrow shape with high frequency of use.

[0070] In other embodiments, the wearable device may also determine the user's preferred eyebrow shape through other means, such as predicting the user's preferred eyebrow shape using a deep learning network model. This application does not limit the method used to determine the user's preferred eyebrow shape.

[0071] In some embodiments of this application, determining a recommended eyebrow shape based on the user's preferred eyebrow shape and the target reference eyebrow shape includes: if the eyebrow shape type of the user's preferred eyebrow shape and the target reference eyebrow shape are consistent, the user's preferred eyebrow shape or the target reference eyebrow shape is used as the recommended eyebrow shape; if the eyebrow shape type of the user's preferred eyebrow shape and the target reference eyebrow shape are inconsistent, the user is prompted to determine the recommended eyebrow shape from the user's preferred eyebrow shape and the target reference eyebrow shape.

[0072] In some embodiments, to ensure the stability, efficiency, and accuracy of eyebrow management, wearable devices typically determine a recommended eyebrow shape as the best eyebrow shape suitable for the user's face shape. Therefore, in the process of determining the recommended eyebrow shape based on the user's preferred eyebrow shape and the target reference eyebrow shape, if the eyebrow shape type of the user's preferred eyebrow shape and the target reference eyebrow shape match, the wearable device can use either the user's preferred eyebrow shape or the target reference eyebrow shape as the recommended eyebrow shape; if the eyebrow shape type of the user's preferred eyebrow shape and the target reference eyebrow shape do not match, the wearable device can prompt the user to determine the recommended eyebrow shape from the user's preferred eyebrow shape and the target reference eyebrow shape.

[0073] In some embodiments, the notification methods include, but are not limited to, any one or more of the following: voice, text display, vibration notification, LED flashing notification, SMS notification, and email notification.

[0074] S13: Calculate the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape.

[0075] In some embodiments, the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape is used to characterize the similarity between the two. By calculating the eyebrow shape matching coefficient, it can be determined whether the current eyebrow shape is a recommended eyebrow shape suitable for the user's face shape. Wearable devices manage the current eyebrow shape based on the eyebrow shape matching coefficient, which can effectively improve the efficiency and accuracy of eyebrow shape management. For example, when the eyebrow shape matching coefficient indicates a large deviation between the current eyebrow shape and the recommended eyebrow shape, the wearable device can prompt the user to adjust the current eyebrow shape, and can generate adjustment suggestions based on the differences between the recommended and current eyebrow shapes, providing the adjustment suggestions to the user to improve the similarity between the adjusted eyebrow shape and the recommended eyebrow shape.

[0076] In some embodiments of this application, calculating the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape includes: obtaining the first eyebrow shape parameter of the current eyebrow shape and the second eyebrow shape parameter of the recommended eyebrow shape respectively; and calculating the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape based on the first eyebrow shape parameter and the second eyebrow shape parameter.

[0077] In some embodiments, the first eyebrow shape parameters include, but are not limited to, the geometric feature parameters of the current eyebrow shape (e.g., eyebrow length, eyebrow width, radius of curvature, etc.) and color parameters. The second eyebrow shape parameters include, but are not limited to, the geometric feature parameters of the recommended eyebrow shape (e.g., eyebrow length, eyebrow width, radius of curvature, etc.) and color parameters.

[0078] In this embodiment, during the process of calculating the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape based on the first eyebrow shape parameter and the second eyebrow shape parameter, the wearable device can calculate the eyebrow shape matching coefficient based on the combination feature parameter of the current eyebrow shape and the geometric feature parameter of the recommended eyebrow shape. For example, wearable devices can calculate an eyebrow shape matching coefficient based on the current eyebrow length, width, and radius of curvature compared to the recommended eyebrow shape; or based on the current eyebrow length and width compared to the recommended eyebrow shape; or based on the current eyebrow width and radius of curvature compared to the recommended eyebrow shape; or based on the current eyebrow length and width compared to the recommended eyebrow shape; or based on the current eyebrow width and radius of curvature compared to the recommended eyebrow shape.

[0079] In other embodiments, the wearable device may also calculate the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape based on the recommended eyebrow shape, other parameters in the corresponding geometric feature parameters of the current eyebrow shape, or other eyebrow shape parameters. The embodiments of this application do not limit the method of determining the eyebrow shape matching coefficient.

[0080] In some embodiments, before calculating the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape based on the first eyebrow shape parameter and the second eyebrow shape parameter, the wearable device may preprocess the recommended eyebrow shape or the current eyebrow shape, for example, by aligning the resolution, pixels, etc., to ensure that the recommended eyebrow shape and the current eyebrow shape are comparable.

[0081] In some embodiments of this application, the first eyebrow shape parameter includes the first eyebrow length, first eyebrow width, and first radius of curvature of the current eyebrow shape; the second eyebrow shape parameter includes the second eyebrow length, second eyebrow width, and second radius of curvature of the recommended eyebrow shape; the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape is calculated based on the first eyebrow shape parameter and the second eyebrow shape parameter, including: calculating the first difference parameter between the first eyebrow length and the second eyebrow length, the second difference parameter between the first eyebrow width and the second eyebrow width, and the third difference parameter between the first radius of curvature and the second radius of curvature according to preset rules; and calculating the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape based on the first difference parameter, the second difference parameter, and the third difference parameter.

[0082] In some embodiments, the first difference parameter can be the ratio or difference between the first eyebrow length and the second eyebrow length. The second difference parameter can be the ratio or difference between the first eyebrow width and the second eyebrow width. The third difference parameter can be the ratio or difference between the first radius of curvature and the second radius of curvature. This application does not limit the specific forms of the first, second, and third difference parameters, but their specific forms must remain consistent. For example, when the first difference parameter is the ratio between the first and second eyebrow lengths, the second difference parameter is the ratio between the first and second eyebrow widths, and the third difference parameter is the ratio between the first and second radii of curvature. When the first difference parameter is the difference between the first and second eyebrow lengths, the second difference parameter is the difference between the first and second eyebrow widths, and the third difference parameter is the difference between the first and second radii of curvature.

[0083] In some embodiments, when the first, second, and third difference parameters are expressed as ratios, the wearable device calculates the ratio by dividing the smaller eyebrow parameter by the correspondingly larger eyebrow parameter according to a preset rule. For example, if the first eyebrow length is less than the second eyebrow length, the first eyebrow width is less than the second eyebrow width, and the first radius of curvature is less than the second radius of curvature, then the first difference parameter is the ratio of the first eyebrow length to the second eyebrow length, the second difference parameter is the ratio of the first eyebrow width to the second eyebrow width, and the third difference parameter is the ratio of the first radius of curvature to the second radius of curvature. In this case, the wearable device can use the sum of the first, second, and third difference parameters as the eyebrow shape matching coefficient between the current eyebrow shape and the recommended eyebrow shape. In this situation, a larger eyebrow shape matching coefficient indicates a greater similarity between the current eyebrow shape and the recommended eyebrow shape. A smaller eyebrow shape matching coefficient indicates a smaller similarity between the current eyebrow shape and the recommended eyebrow shape.

[0084] In some embodiments, to improve the matching accuracy between the current eyebrow shape and the recommended eyebrow shape, and thus improve the accuracy of eyebrow shape management, the wearable device can select multiple first eyebrow widths at multiple positions of the current eyebrow shape. For example, correspondingly, the same number of second eyebrow widths can be selected at the corresponding positions of the recommended eyebrow shape. Taking the second difference parameter as a ratio as an example, when using the second difference parameter, the wearable device can calculate the ratio between the first eyebrow width and the corresponding second eyebrow width to obtain multiple second difference parameters.

[0085] As an example, such as Figure 6 The current eyebrow shape shown is represented by its first eyebrow length, denoted as g. The wearable device divides the current eyebrow shape into four equal parts using bisectors h, i, and j. h, i, and j each represent the first eyebrow width. k represents the first radius of curvature of the current eyebrow shape. For example... Figure 7As shown, taking reference eyebrow shape I as the recommended eyebrow shape as an example, the wearable device divides the recommended eyebrow shape into four equal parts using bisectors h1, i1, and j1. h1, i1, and j1 can all represent the second eyebrow width of the recommended eyebrow shape. k1 represents the second radius of curvature of the recommended eyebrow shape. g1 represents the second eyebrow length of the recommended eyebrow shape. The first, second, and third difference parameters are expressed as ratios, with the first eyebrow length g less than the second eyebrow length g1, the first eyebrow width h less than the second eyebrow width h1, the first eyebrow width i less than the second eyebrow width i1, the first eyebrow width j less than the second eyebrow width j1, and the first radius of curvature k greater than the second radius of curvature k1. Therefore, the first difference parameter is g / g1, the second difference parameters include h / h1, i / i1, and j / j1, and the third difference parameter is k1 / k1. The eyebrow shape matching coefficient between the current eyebrow shape and the recommended eyebrow shape is the sum of g / g1, h / h1, i / i1, j / j1, and k1 / k1.

[0086] In some embodiments, the first difference parameter, the second difference parameter, and the third difference parameter can also be expressed as difference values. In this case, the wearable device, according to preset rules, can use the absolute difference between the first eyebrow length and the second eyebrow length as the first difference parameter, the absolute difference between the first eyebrow width and the second eyebrow width as the second difference parameter, and the absolute difference between the first arc radius and the second arc radius as the third difference parameter. Then, the wearable device can use the sum of the first difference parameter, the second difference parameter, and the third difference parameter as the eyebrow shape matching coefficient between the current eyebrow shape and the recommended eyebrow shape. The smaller the eyebrow shape matching coefficient, the greater the similarity between the current eyebrow shape and the recommended eyebrow shape. When the eyebrow shape matching coefficient is large, it indicates that the similarity between the current eyebrow shape and the recommended eyebrow shape is small.

[0087] In other embodiments, the wearable device can calculate the eyebrow shape matching coefficient between the current eyebrow shape and the recommended eyebrow shape based on any one or more of the first difference parameter, the second difference parameter, and the third difference parameter. For example, the wearable device can use the sum of the first difference parameter and the second difference parameter as the eyebrow shape matching coefficient between the current eyebrow shape and the recommended eyebrow shape, or it can determine the eyebrow shape matching coefficient between the current eyebrow shape and the recommended eyebrow shape based on the sum of the first difference parameter and the third difference parameter, etc. The embodiments of this application do not limit the method of calculating the eyebrow shape matching coefficient between the current eyebrow shape and the recommended eyebrow shape.

[0088] S14: Based on the eyebrow shape matching coefficient, generate eyebrow shape management suggestions for the current eyebrow shape.

[0089] In some embodiments, eyebrow management suggestion information may include prompts indicating that the current eyebrow shape needs to be adjusted, and adjustment suggestions for adjusting the current eyebrow shape. Eyebrow management suggestion information can be customized; however, this application embodiment does not limit the specific settings of the eyebrow management suggestion information.

[0090] In some embodiments of this application, eyebrow management suggestion information for the current eyebrow shape is generated based on the eyebrow shape matching coefficient, including: if the eyebrow shape matching coefficient is not within the preset coefficient range, generating eyebrow management suggestion information for the current eyebrow shape based on the recommended eyebrow shape.

[0091] In some embodiments, the preset coefficient range can be customized according to the specific method of calculating the eyebrow matching coefficient. For example, when the first difference parameter, the second difference parameter, and the third difference parameter are expressed as ratios, the wearable device can use the sum of the first difference parameter, the second difference parameter, and the third difference parameter as the eyebrow matching coefficient between the current eyebrow shape and the recommended eyebrow shape. In this case, the larger the eyebrow matching coefficient, the greater the similarity between the current eyebrow shape and the recommended eyebrow shape. When the eyebrow matching coefficient is small, it indicates that the similarity between the current eyebrow shape and the recommended eyebrow shape is small. Therefore, the wearable device can set a first eyebrow matching coefficient threshold, and the preset coefficient range is the range where the eyebrow matching coefficient is greater than or equal to the first eyebrow matching coefficient threshold. When it is determined that the eyebrow matching coefficient between the current eyebrow shape and the recommended eyebrow shape is less than the first eyebrow matching coefficient threshold, the wearable device can determine that the eyebrow matching coefficient is not within the preset coefficient range. In this case, the wearable device can determine that the difference between the current eyebrow shape and the recommended eyebrow shape is large and the similarity is small, and can generate eyebrow shape management suggestion information for the current eyebrow shape based on the recommended eyebrow shape to prompt the user to adjust the current eyebrow shape in a timely manner.

[0092] For example, the first, second, and third difference parameters can be represented as difference values. In this case, the wearable device can use the sum of these three parameters as the eyebrow shape matching coefficient between the current and recommended eyebrow shapes. A smaller matching coefficient indicates a higher similarity between the current and recommended eyebrow shapes. Therefore, the wearable device can set a second eyebrow shape matching coefficient threshold, with a preset range where the matching coefficient is less than or equal to this threshold. When the matching coefficient between the current and recommended eyebrow shapes is determined to be greater than this threshold, the wearable device can determine that the matching coefficient is not within the preset range. In this situation, the wearable device can determine that the difference between the current and recommended eyebrow shapes is significant, and the similarity is low. It can then generate eyebrow shape management suggestions based on the recommended eyebrow shape to prompt the user to adjust their current eyebrow shape.

[0093] In other embodiments, the wearable device may also trigger the generation of eyebrow management suggestion information for the current eyebrow shape in other ways, such as comparing the first eyebrow shape parameters with the second eyebrow shape parameters to obtain the parameter comparison result; if the parameter comparison result does not meet the preset requirements, the eyebrow management suggestion information for the current eyebrow shape is generated according to the recommended eyebrow shape.

[0094] In some embodiments, after generating eyebrow management suggestions based on an eyebrow shape matching coefficient, the wearable device can prompt the user to perform operations such as trimming the current eyebrow shape according to the eyebrow management suggestions. The prompting methods include, but are not limited to, one or more of the following: voice, text display, vibration prompts, LED flashing prompts, SMS prompts, and email prompts.

[0095] In other embodiments, if the eyebrow shape matching coefficient is within a preset coefficient range, the eyebrow shape management suggestion information generated by the wearable device may include information such as the current eyebrow shape adaptation and the eyebrow shape matching coefficient.

[0096] This application provides an eyebrow management method that uses a wearable device to scan the user's head and determine the user's primary face shape type. Based on preset recommendation rules and the primary face shape type, a recommended eyebrow shape is determined. The recommended eyebrow shape represents the optimal eyebrow shape suitable for the user's face shape. By calculating the eyebrow shape matching coefficient between the current eyebrow shape and the recommended eyebrow shape, the difference between the two is determined, thereby generating eyebrow management suggestions for the current eyebrow shape. This method eliminates the need for photo or video input, and by determining the recommended eyebrow shape based on preset recommendation rules and the user's primary face shape type, and generating eyebrow management suggestions based on the eyebrow shape matching coefficient between the current eyebrow shape and the recommended eyebrow shape, it effectively improves the efficiency and accuracy of eyebrow management.

[0097] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0098] Please see Figure 8 The diagram shown is a structural diagram of the eyebrow management device provided in an embodiment of this application. It can implement the details of the eyebrow management method described in the above embodiments and achieve the same effect. Figure 8 As shown, the eyebrow management device 10 can be applied to wearable devices with data processing capabilities. The eyebrow management device 10 includes: a scanning module 11 for scanning the user's head and determining the user's first face shape type; a determination module 12 for determining a recommended eyebrow shape based on preset recommendation rules and the first face shape type; a calculation module 13 for calculating the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape; and a management module 14 for generating eyebrow shape management suggestion information for the current eyebrow shape based on the eyebrow shape matching coefficient.

[0099] For specific limitations regarding the eyebrow management device 10, please refer to the limitations of the eyebrow management method above, which will not be repeated here. Each module in the aforementioned eyebrow management device 10 can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in the wearable device, or stored in software in the memory of the wearable device, so that the processor can call and execute the operations corresponding to each module.

[0100] Please see Figure 9 The diagram shown is a structural schematic of a wearable device 100 provided in an embodiment of this application. The wearable device 100 includes, but is not limited to, smart glasses (e.g., VR glasses, AR glasses, MR glasses). The network in which the wearable device 100 is located includes, but is not limited to, the Internet, wide area network, metropolitan area network, local area network, and virtual private network (VPN).

[0101] like Figure 9 As shown, the wearable device 100 includes a communication module 101, a memory 102, a processor 103, an input / output interface 104, and a bus 105. The processor 103 is coupled to the communication module 101, the memory 102, and the input / output interface 104 via the bus 105.

[0102] The communication module 101 can be a wireless communication module or a mobile communication module. The wireless communication module can provide solutions for wireless communication applications on the wearable device 100, including Wireless Local Area Networks (WLAN) (e.g., Wireless Fidelity, Wi-Fi), Bluetooth (BT), Global Navigation Satellite System (GNSS), Frequency Modulation (FM), Near Field Communication (NFC), and Infrared (IR) technologies. The mobile communication module can provide solutions for wireless communication applications on the wearable device 100, including 2G / 3G / 4G / 5G technologies.

[0103] Memory 102 may include one or more random access memory (RAM) and one or more non-volatile memory (NVM). The RAM can be directly read and written by the processor 103 and can be used to store executable programs (e.g., machine instructions) of the operating system or other running programs, as well as user and application data. The RAM may include static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM, such as fifth-generation DDR SDRAM, generally referred to as DDR5 SDRAM), etc.

[0104] Non-volatile memory can also store executable programs and user and application data, and can be pre-loaded into random access memory for direct reading and writing by the processor 103. Non-volatile memory can include disk storage devices and flash memory.

[0105] The memory 102 is used to store one or more computer programs. The one or more computer programs are configured to be executed by the processor 103. The one or more computer programs include a plurality of instructions that, when executed by the processor 103, enable an eyebrow management method to be executed on the wearable device 100.

[0106] In other embodiments, the wearable device 100 also includes an external memory interface for connecting to an external memory to expand the storage capacity of the wearable device 100.

[0107] Processor 103 may include one or more processing units, such as application processor (AP), modem processor, graphics processing unit (GPU), image signal processor (ISP), controller, video codec, digital signal processor (DSP), baseband processor, and / or neural network processing unit (NPU). These different processing units may be independent devices or integrated into one or more processors.

[0108] The processor 103 provides computing and control capabilities; for example, the processor 103 is used to execute computer programs stored in the memory 102 to implement the eyebrow management method described above.

[0109] The input / output interface 104 is used to provide a channel for user input or output. For example, the input / output interface 104 can be used to connect various input / output devices, such as a mouse, keyboard, touch device, display screen, etc., so that users can enter information or visualize information.

[0110] Bus 105 is used at least to provide a channel for communication between the communication module 101, memory 102, processor 103, and input / output interface 104 in the wearable device 100.

[0111] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the wearable device 100. In other embodiments of this application, the wearable device 100 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0112] This application also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, and the method implemented when the program instructions are executed can refer to the eyebrow management method in the above embodiments of this application.

[0113] The computer-readable storage medium can be the internal memory of the wearable device described in the above embodiments, such as the hard drive or memory of the wearable device. The computer-readable storage medium can also be an external storage device of the wearable device, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the wearable device.

[0114] Furthermore, the computer-readable storage medium may primarily include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, etc.; and the data storage area may store data created based on the use of the wearable device, etc.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. An eyebrow shaping method applied to wearable devices, characterized in that, The eyebrow management method includes: Scan the user's head to determine the user's primary face shape type; Based on the preset recommendation rules and the first face shape type, a recommended eyebrow shape is determined; Calculate the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape; Based on the eyebrow shape matching coefficient, eyebrow shape management suggestions are generated for the current eyebrow shape.

2. The eyebrow management method as described in claim 1, characterized in that, The process of scanning the user's head to determine the user's primary face shape includes: Scan the user's head to obtain the user's digital facial features; Based on the first face shape parameters of the digital face, determine the second face shape type of the digital face; The user's first face type is determined based on the second face type.

3. The eyebrow management method as described in claim 2, characterized in that, Determining the second face type of the digital face based on the first face type parameters of the digital face includes: Retrieve the second face shape parameters from the reference face shape library; Based on the first face shape parameter and the second face shape parameter, calculate the face shape matching coefficient between the digital face shape and the reference face shape; Based on the face shape matching coefficient, the target reference face shape corresponding to the digital face shape is matched from the preset face shape library; The second face type is determined based on the third face type of the target reference face type.

4. The eyebrow management method as described in claim 1 or 2, characterized in that, The step of determining the recommended eyebrow shape based on preset recommendation rules and the first face shape type includes: According to the preset correspondence, the target reference eyebrow shape corresponding to the first face type is matched from the preset eyebrow shape library. The preset correspondence includes the correspondence between face type and reference eyebrow shape. The recommended eyebrow shape is determined based on the user's preferred eyebrow shape and the target reference eyebrow shape.

5. The eyebrow management method as described in claim 4, characterized in that, The step of determining the recommended eyebrow shape based on the user's preferred eyebrow shape and the target reference eyebrow shape includes: If the eyebrow shape preferred by the user matches the eyebrow shape type of the target reference eyebrow shape, the eyebrow shape preferred by the user or the target reference eyebrow shape will be used as the recommended eyebrow shape. If the eyebrow shape type of the user's preferred eyebrow shape does not match that of the target reference eyebrow shape, the user is prompted to determine the recommended eyebrow shape from the user's preferred eyebrow shape and the target reference eyebrow shape.

6. The eyebrow management method as described in claim 1, characterized in that, The calculation of the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape includes: Obtain the first eyebrow shape parameter of the current eyebrow shape and the second eyebrow shape parameter of the recommended eyebrow shape respectively; Based on the first eyebrow shape parameter and the second eyebrow shape parameter, calculate the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape.

7. The eyebrow management method as described in claim 6, characterized in that, The first eyebrow shape parameters include the first eyebrow length, first eyebrow width, and first radius of curvature of the current eyebrow shape; the second eyebrow shape parameters include the second eyebrow length, second eyebrow width, and second radius of curvature of the recommended eyebrow shape; the step of calculating the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape based on the first eyebrow shape parameters and the second eyebrow shape parameters includes: According to the preset rules, calculate the first difference parameter between the first eyebrow length and the second eyebrow length, the second difference parameter between the first eyebrow width and the second eyebrow width, and the third difference parameter between the first arc radius and the second arc radius respectively. Based on the first difference parameter, the second difference parameter, and the third difference parameter, calculate the eyebrow shape matching coefficient between the recommended eyebrow shape and the current eyebrow shape.

8. The eyebrow management method as described in claim 1 or 7, characterized in that, The step of generating eyebrow management suggestions based on the eyebrow shape matching coefficient includes: If the eyebrow shape matching coefficient is not within the preset coefficient range, eyebrow shape management suggestion information for the current eyebrow shape is generated based on the recommended eyebrow shape.

9. A wearable device, characterized in that, It includes a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the computer-readable instructions, when executed by the processor, implement the eyebrow management method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions that, when executed by a processor, implement the eyebrow management method as described in any one of claims 1 to 8.

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