Makeup auxiliary method and device based on intelligent cosmetic mirror and electronic equipment

Abstract

The invention provides a makeup auxiliary method and device based on an intelligent cosmetic mirror and electronic equipment, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a user voice instruction and user appearance data; based on the user instruction analysis, obtaining makeup demand keywords; and based on the makeup demand keyword and the user appearance data, obtaining a makeup teaching video, and displaying the makeup teaching video through an intelligent cosmetic mirror. According to the invention, through non-contact interaction, teaching content self-adaptive pushing and real-time action guiding, the makeup experience of the user is comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and particularly to a makeup assistance method, device and electronic device based on an intelligent makeup mirror. Background Art

[0002] In recent years, with the rapid development of artificial intelligence, Internet of Things and computer vision technologies, intelligent mirrors, as emerging human-computer interaction carriers, have been gradually applied to the fields of home, beauty makeup and health management. In the beauty makeup scenario, by integrating a display screen and a camera, an intelligent mirror can display the user's facial image in real time and overlay makeup teaching videos to provide intuitive guidance for the user. However, there are still significant defects in the existing technology in practical applications: 1. During the makeup process, the user needs to frequently switch the playback progress of the teaching video, zoom in on a partial picture or adjust the display parameters by touching the mirror surface. When the user holds cosmetics or has makeup products on their hands, directly operating the mirror surface is likely to cause accidental touch of the interface, response delay, and the residual stains will reduce the imaging clarity of the mirror surface; 2. Most existing systems adopt a video playback mode with a fixed process and cannot automatically match teaching content according to the user's current makeup area (such as eye makeup, lip makeup). The user needs to manually retrieve the steps, resulting in distraction of attention and interruption of operation; 3. Traditional solutions lack the ability to perceive the user's actual operation state in real time and cannot provide targeted error correction prompts through motion capture or makeup analysis, which affects the learning efficiency.

[0003] Currently, some technologies have tried to solve the above problems through voice control or external devices (such as mobile phone APPs), but the recognition rate of voice commands is low in a noisy environment, and the external devices require the user to shift their line of sight to operate, and none of them have achieved "non-intrusive" interaction.

[0004] Therefore, a makeup assistance method, device and electronic device based on an intelligent makeup mirror are proposed. Summary of the Invention

[0005] This specification provides a makeup assistance method, device and electronic device based on an intelligent makeup mirror, which comprehensively improves the user's makeup experience through non-contact interaction, adaptive push of teaching content and real-time action guidance.

[0006] This specification provides a makeup assistance method based on an intelligent makeup mirror, including: Obtaining user voice commands and user appearance data; Based on the analysis of the user commands, obtaining makeup requirement keywords; Based on the makeup requirement keywords and the user appearance data, obtaining a makeup teaching video and displaying the makeup teaching video through the intelligent makeup mirror.

[0007] Optionally, after obtaining the user's facial appearance data, the following steps are further included: Analyze based on the user's facial appearance data to obtain the user's facial contour curvature, relative positions of facial features, skin texture features, and user skin color classification; Construct a 3D facial model of the user based on the user's facial contour curvature, relative positions of facial features, and skin texture features; Perform zoning processing on the 3D facial model to obtain a contour zoning that fits the user's facial appearance; Based on the user's skin color classification and skin texture features, determine the target cosmetics and their color numbers.

[0008] Optionally, after obtaining a makeup teaching video based on the makeup requirement keywords and the user's facial appearance data and displaying the makeup teaching video through the intelligent makeup mirror, the following steps are further included: Obtain the image of the holding posture of the makeup brush in real time; Analyze the image of the holding posture of the makeup brush to obtain the model of the makeup brush; Based on the model of the makeup brush, obtain the optimal dipping amount and the type of cosmetics.

[0009] Optionally, after obtaining the optimal dipping amount and the type of cosmetics based on the model of the makeup brush, the following steps are further included: Obtain the image of the makeup brush dipping cosmetics in real time; Analyze the image of the makeup brush dipping cosmetics to predict the dipping amount of the makeup brush; When the dipping amount of the makeup brush exceeds the threshold, activate the LED ring-shaped light strip on the edge of the mirror to emit a red light warning, and trigger the air pump device to blow off the excess powder.

[0010] Optionally, after obtaining a makeup teaching video based on the makeup requirement keywords and the user's facial appearance data and displaying the makeup teaching video through the intelligent makeup mirror, the following steps are further included: Obtain the facial image of the user during the makeup process in real time; Extract the facial image of the user during the makeup process through an image segmentation model to obtain the boundary between the made-up area and the non-made-up area; Compare the boundary with the expected boundary of the target makeup in the makeup teaching video to generate a boundary offset; Dynamically adjust the playback progress of the makeup teaching video based on the boundary offset and overlay a correction trajectory prompt layer.

[0011] Optionally, after obtaining a makeup teaching video based on the makeup requirement keywords and the user's facial appearance data and displaying the makeup teaching video through the intelligent makeup mirror, the following steps are further included: Obtain ambient light intensity and color temperature data in real time; Calculate the color rendering deviation of the target cosmetic color number under the ambient light intensity and color temperature data through a light simulation model; When the color rendering deviation exceeds a preset value, automatically screen color number alternative plans from the makeup teaching video, and display a comparison diagram of the virtual makeup application effects of the original plan and the alternative plan side by side on the mirror display interface.

[0012] This specification provides a makeup assistance device based on an intelligent makeup mirror, including: An acquisition module for acquiring user voice instructions and user appearance data; An analysis module for analyzing based on the user instructions to obtain makeup requirement keywords; An auxiliary makeup module for obtaining a makeup teaching video based on the makeup requirement keywords and the user appearance data, and displaying the makeup teaching video through the intelligent makeup mirror.

[0013] Optionally, after the acquisition module, it further includes: Analyze based on the user appearance data to obtain the user's facial contour curvature, relative positions of facial features, skin texture features, and user skin color classification; Construct a user's three-dimensional facial model based on the user's facial contour curvature, relative positions of facial features, and skin texture features; Perform partition processing on the three-dimensional facial model to obtain a contouring partition adapted to the user's appearance; Determine the target cosmetics and their color numbers based on the user skin color classification and skin texture features.

[0014] Optionally, after the auxiliary makeup module, it further includes: Obtain the holding posture image of the makeup brush in real time; Analyze the holding posture image of the makeup brush to obtain the model of the makeup brush; Based on the model of the makeup brush, obtain the optimal dipping amount and cosmetic type.

[0015] Optionally, after the auxiliary makeup module, it further includes: Obtain the image of the makeup brush dipping cosmetics in real time; Analyze the image of the makeup brush dipping cosmetics to predict the dipping amount of the makeup brush; When the dipping amount of the makeup brush exceeds the threshold, activate the LED ring-shaped light strip on the edge of the mirror to emit a red light warning, and trigger the air pump device to blow off the excess powder.

[0016] Optionally, after the auxiliary makeup module, it further includes: Obtain the facial image of the user during the makeup process in real time; The facial image of the user in the makeup process is extracted through the image segmentation model to obtain the boundary between the makeup area and the non-makeup area; Comparing the boundary with an expected boundary of a target makeup in the makeup teaching video to generate a boundary offset; The playback progress of the makeup teaching video is dynamically adjusted based on the boundary offset, and a correction track prompt layer is superimposed.

[0017] Optionally, after the auxiliary makeup module, it also includes: Obtain ambient light intensity and color temperature data in real time; Calculate the color rendering deviation of the target cosmetic color number under the ambient light intensity and color temperature data through a lighting simulation model; When the color rendering deviation exceeds a preset value, color number alternatives are automatically selected from the makeup teaching video, and a virtual makeup trial effect comparison chart of the original scheme and the alternative scheme is displayed side by side on the mirror display interface.

[0018] In the present invention, a full-link intelligence from user feature recognition to makeup generation is realized through a multimodal data fusion architecture, and high-precision 3D facial modeling, real-time posture perception and dynamic ambient light compensation technology are innovatively integrated, combined with a lightweight deep learning model and an augmented reality interaction system to form a "teaching-operation-correction" closed-loop control system; its technical difference is reflected in the cross-dimensional parameter coupling capability - dynamically associating biometric feature recognition, physical environment perception and beauty knowledge graph, achieving millimeter-level boundary alignment of personalized makeup through generative adversarial networks, and creating an original brush-cosmetics-skin quality ternary collaborative optimization algorithm, and finally constructing an intelligent beauty ecosystem with real-time feedback, adaptive correction and cross-scenario continuous service. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A schematic diagram of the principle of a makeup assistance method based on a smart makeup mirror provided in an embodiment of this specification; Figure 2 A schematic diagram of the structure of a makeup assisting device based on a smart makeup mirror provided in an embodiment of this specification; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification; Figure 4A schematic diagram of a computer-readable medium provided for an embodiment of this specification. DETAILED DESCRIPTION

[0021] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0022] The following is combined with Figures 1-4 The exemplary embodiments of the present invention are described more fully. However, the exemplary embodiments can be implemented in various forms, and it should not be understood that the present invention is limited to the embodiments set forth herein. On the contrary, providing these exemplary embodiments can make the present invention more comprehensive and complete, and it is more convenient to fully convey the inventive concept to those skilled in the art. The same reference numerals in the figures represent the same or similar elements, components or parts, and thus their repeated description will be omitted.

[0023] Under the premise of being consistent with the technical concept of the present invention, the features, structures, characteristics or other details described in a specific embodiment do not exclude that they can be combined in one or more other embodiments in a suitable manner.

[0024] In the description of specific embodiments, the features, structures, characteristics or other details described in the present invention are intended to enable those skilled in the art to fully understand the embodiments. However, it does not exclude that those skilled in the art can practice the technical solutions of the present invention without one or more of the specific features, structures, characteristics or other details.

[0025] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0026] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0027] The term "and / or" or "and / or" includes all combinations of any one or more of the associated listed items.

[0028] Figure 1Schematic diagram of the principle of a makeup assistance method based on an intelligent makeup mirror provided by an embodiment of this specification. The method may include: S110: Obtain user voice instructions and user appearance data; S120: Analyze based on the user instructions to obtain makeup requirement keywords; S130: Based on the makeup requirement keywords and the user appearance data, obtain a makeup teaching video and display the makeup teaching video through the intelligent makeup mirror.

[0029] In the specific implementation manner of this specification, the array microphone and ultra-clear camera integrated in the intelligent makeup mirror are used to collect user voice instructions and three-dimensional facial data in real time. Among them, the voice recognition module adopts an end-cloud collaboration architecture. After the basic instruction parsing is completed locally, the voice feature vector is uploaded to the cloud ASR system through an encrypted channel for in-depth semantic analysis. Combining with the corpus in the beauty field in the knowledge graph, the attention mechanism is used to extract multi-dimensional requirement tags such as "dinner / smoky makeup / durable anti-hair loss". At the same time, the 3D structured light module built into the intelligent mirror will construct a user facial topology grid, and combine deep learning algorithms to quantitatively analyze several biometric feature points such as eye distance and cheekbone height. Through multi-modal fusion technology, the semantic tags and facial feature data are input into the makeup generation engine. This engine includes a virtual makeup trial module based on a generative adversarial network and a makeup disassembly algorithm optimized by knowledge distillation. It can not only render the eyeshadow blending path adapted to the user's face shape in real time, but also decompose the complete makeup into several standard steps, and superimpose dynamic teaching guidelines on the mirror surface through AR projection technology, and cooperate with the voice interaction system to realize functions such as "gesture control of the playback rhythm" and "real-time makeup comparison and correction". The finally generated interactive teaching video will be presented through the built-in display screen of the mirror surface, and at the same time, it will be synchronized to the user's mobile terminal to form a personalized beauty makeup file.

[0030] Optionally, after S110, it further includes: Analyze based on the user appearance data to obtain the user's facial contour curvature, relative positions of facial features, skin texture features, and user skin color classification; Construct a user three-dimensional facial model based on the user's facial contour curvature, relative positions of facial features, and skin texture features; Perform zoning processing on the three-dimensional facial model to obtain a contour zoning adapted to the user's appearance; Based on the user skin color classification and skin texture features, determine the target cosmetics and their color numbers.

[0031] In the specific embodiments of this specification, user facial data is collected through the high-precision sensors of the smart mirror, and the contour curvature, facial feature proportions, and skin texture are analyzed using core algorithms to construct a dynamic three-dimensional facial model; based on the golden ratio and skin characteristics, several contouring modules such as the cheekbone brightening area and the nose bridge shadow area are intelligently divided, combined with the skin color LAB value to match the color card system, and the liquid foundation with a color difference meeting the conditions is compared through the cosmetics database, and the concealer product adapted to the user's skin pH value is recommended synchronously. Finally, a personalized contouring plan is generated and projected onto the mirror AR interface.

[0032] Optionally, after S130, the method further includes: Obtaining the image of the holding posture of the makeup brush in real time; Analyzing the image of the holding posture of the makeup brush to obtain the model of the makeup brush; Based on the model of the makeup brush, obtaining the optimal dipping amount and the type of cosmetics.

[0033] In the specific embodiments of this specification, the holding posture of the makeup brush is captured by a camera, and the brand logo of the brush handle and the morphological characteristics of the bristles are synchronously recognized through the improved YOLOv8 model combined with the ResNet18 pose estimation network. The three-dimensional parameters of professional makeup brushes are matched using the makeup brush database constructed by transfer learning. Combining the data of the brush head inclination sensor and the pressure feedback module, the dipping amount threshold suitable for the current brush type is calculated through a multi-modal fusion algorithm (for example, the recommended dipping amount of a tongue-shaped liquid foundation brush < 0.3 ml), and the cosmetics RFID tag recognition system is linked. When it is detected that the user holds a powder brush close to a cream blush, a red warning frame is immediately superimposed on the mirror AR interface and a real-time prompt of "It is detected that the brush type does not match the product texture. It is recommended to switch to a flat-headed brush with fiber bristles" is pushed.

[0034] Optionally, after obtaining the optimal dipping amount and the type of cosmetics based on the model of the makeup brush, the method further includes: Obtaining the image of the makeup brush dipping cosmetics in real time; Analyzing the image of the makeup brush dipping cosmetics to predict the dipping amount of the makeup brush; When the dipping amount of the makeup brush exceeds the threshold, activate the LED ring-shaped light strip on the edge of the mirror to emit a red warning, and trigger the air pump device to blow off the excess powder.

[0035] In a specific implementation of the present specification, a high-speed macro camera is integrated in the edge of the smart mirror to capture a close-up image of the makeup brush head in real time, and the powder-carrying area of the bristles is identified by improving the YOLOv5 model combined with HSV color space analysis. The powder amount regression prediction model constructed by transfer learning is used to dynamically estimate the amount of cosmetics with different textures such as eye shadow / loose powder. When it is detected that the powder coverage rate exceeds a preset threshold (such as matte eye shadow > preset coverage area, or pearlescent eye shadow > preset coverage area), the main control chip integrated in the frame synchronously triggers the annular RGB light strip to switch to a red light warning, and at the same time starts the hidden micro air pump array at the bottom of the mirror body. The PID algorithm is used to control the pulse airflow to accurately blow the edge of the brush head, and the dual-axis inclination sensor is used to correct the blowing angle in real time to ensure that excess powder is blown into the bottom recovery slot in a directionally controlled manner.

[0036] Optionally, after S130, the step further includes: Real-time acquisition of facial images of users during makeup process; The facial image of the user in the makeup process is extracted through the image segmentation model to obtain the boundary between the makeup area and the non-makeup area; Comparing the boundary with an expected boundary of a target makeup in the makeup teaching video to generate a boundary offset; The playback progress of the makeup teaching video is dynamically adjusted based on the boundary offset, and a correction track prompt layer is superimposed.

[0037] In a specific implementation of the present specification, multi-angle images of the user's face are captured in real time, pixel-level semantic segmentation is performed through an improved lightweight DeepLabv3+ model, and the attention mechanism is used to enhance eye shadow boundary / lip line edge detection, generate a real-time makeup area mask and extract sub-pixel contour coordinates; the actual contour data is spatiotemporally aligned with the expected boundary of the teaching video through a timestamp synchronization engine, and the local area distance is calculated using an improved dynamic time warping algorithm as an offset quantification indicator. When the brow peak offset exceeds a preset value or the lip line overlap is less than a preset value, the teaching system automatically switches to a frame-level precision playback mode, and at the same time, a dynamic guidance trajectory is superimposed through the mirror AR engine, and the tactile feedback module is linked to generate a vibration prompt at the makeup brush handle.

[0038] Optionally, after S130, the step further includes: Obtain ambient light intensity and color temperature data in real time; Calculate the color rendering deviation of the target cosmetic color number under the ambient light intensity and color temperature data through a lighting simulation model; When the color rendering deviation exceeds a preset value, color number alternatives are automatically selected from the makeup teaching video, and a virtual makeup trial effect comparison chart of the original scheme and the alternative scheme is displayed side by side on the mirror display interface.

[0039] In the specific embodiments of this specification, the current environmental parameters are mapped to the LAB color space of cosmetic color numbers for Monte Carlo ray tracing simulation. When it is detected that the lip color display is greater than the preset value or the saturation deviation of the eyeshadow is greater than the preset value, the distributed cosmetic knowledge graph is called to retrieve candidate color numbers of the same color system. The improved collaborative filtering algorithm is used to combine the user's skin color type and makeup style preference to screen out alternative solutions, and the lightweight StyleGAN generative adversarial network is used to synchronously render the virtual makeup effects of the original color and the alternative color. The AR split-screen technology is used to retain the standard color of the teaching video on the left side of the mirror and superimpose the comparison graph of the corrected color under the simulation of dynamic ambient light on the right side. At the same time, the voice system is linked to give real-time guidance such as "It is detected that the cold light environment has an impact. It is recommended to switch to coral color number 03".

[0040] In the present invention, the full-link intelligence from user feature recognition to makeup generation is realized through a multi-modal data fusion architecture. High-precision 3D facial modeling, real-time pose perception and dynamic ambient light compensation technologies are innovatively integrated, combined with a lightweight deep learning model and an augmented reality interaction system to form a "teaching - operation - correction" closed-loop control system; the technical difference is reflected in the cross-dimensional parameter coupling ability - dynamically associating biometric recognition, physical environment perception and the beauty makeup knowledge graph, achieving millimeter-level boundary alignment of personalized makeup through the generative adversarial network, and uniquely creating a three-way collaborative optimization algorithm for brushes - cosmetics - skin types, and finally constructing an intelligent beauty makeup ecosystem with real-time feedback, adaptive correction and cross-scene continuous services.

[0041] Figure 2 It is a schematic diagram of the principle of a makeup assistance device based on an intelligent makeup mirror provided by an embodiment of this specification. This device may include: An acquisition module 10, configured to acquire user voice instructions and user appearance data; An analysis module 20, configured to obtain makeup requirement keywords based on the analysis of the user instructions; An assisted makeup module 30, configured to obtain a makeup teaching video based on the makeup requirement keywords and the user appearance data, and display the makeup teaching video through the intelligent makeup mirror.

[0042] Optionally, after the acquisition module 10, it further includes: Analyzing based on the user appearance data to obtain the user's facial contour curvature, relative positions of facial features, skin texture features, and user skin color classification; Constructing a user's three-dimensional facial model based on the user's facial contour curvature, relative positions of facial features, and skin texture features; Performing partition processing on the three-dimensional facial model to obtain a contouring partition adapted to the user's appearance; Based on the user's skin color classification and skin texture features, determine the target cosmetics and their color numbers.

[0043] Optionally, after the auxiliary makeup module 30, it further includes: Obtain the holding posture image of the makeup brush in real time; Analyze the holding posture image of the makeup brush to obtain the model of the makeup brush; Based on the model of the makeup brush, obtain the optimal dipping amount and the type of cosmetics.

[0044] Optionally, after the auxiliary makeup module 30, it further includes: Obtain the image of the makeup brush dipping cosmetics in real time; Analyze the image of the makeup brush dipping cosmetics to predict the dipping amount of the makeup brush; When the dipping amount of the makeup brush exceeds the threshold, activate the LED ring light strip on the mirror edge to emit a red light warning, and trigger the air pump device to blow off the excess powder.

[0045] Optionally, after the auxiliary makeup module 30, it further includes: Obtain the facial image during the user's makeup process in real time; Extract the facial image during the user's makeup process through an image segmentation model to obtain the boundary between the made-up area and the non-made-up area; Compare the boundary with the expected boundary of the target makeup in the makeup teaching video to generate a boundary offset; Based on the boundary offset, dynamically adjust the playback progress of the makeup teaching video and overlay a correction trajectory hint layer.

[0046] Optionally, after the auxiliary makeup module 30, it further includes: Obtain the ambient light intensity and color temperature data in real time; Calculate the color rendering deviation of the target cosmetics color number under the ambient light intensity and color temperature data through a light simulation model; When the color rendering deviation exceeds the preset value, automatically screen the color number alternative plan from the makeup teaching video, and display the virtual makeup test effect comparison chart of the original plan and the alternative plan side by side on the mirror display interface.

[0047] The functions of the device in the embodiments of the present invention have been described in the above method embodiments. Therefore, for the details not described in this embodiment, reference can be made to the relevant descriptions in the foregoing embodiments, and no further elaboration will be made here.

[0048] Based on the same inventive concept, the embodiments of this specification also provide an electronic device.

[0049] Embodiments of the electronic device according to the present invention are described below. The electronic device can be regarded as a specific physical implementation of the above method and apparatus embodiments of the present invention. Details described in the embodiments of the electronic device of the present invention should be regarded as a supplement to the above method or apparatus embodiments; for details not disclosed in the embodiments of the electronic device of the present invention, reference can be made to the above method or apparatus embodiments for implementation.

[0050] Figure 3 FIG. is a schematic structural diagram of an electronic device provided by an embodiment of this specification. The following will refer to Figure 3 to describe the electronic device 300 according to this embodiment of the present invention. Figure 3 The electronic device 300 shown is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present invention.

[0051] As Figure 3 shown, the electronic device 300 is presented in the form of a general computing device. The components of the electronic device 300 may include but are not limited to: at least one processing unit 310, at least one storage unit 320, a bus 330 connecting different system components (including the storage unit 320 and the processing unit 310), a display unit 340, etc.

[0052] Among them, the storage unit stores program code, and the program code can be executed by the processing unit 310, so that the processing unit 310 executes the steps according to various exemplary embodiments of the present invention described in the above processing method part of this specification. For example, the processing unit 310 can execute steps as Figure 1 shown.

[0053] The storage unit 320 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 3201 and / or a cache storage unit 3202, and may further include a read-only storage unit (ROM) 3203.

[0054] The storage unit 320 may further include a program / utilities 3204 having a set (at least one) of program modules 3205. Such program modules 3205 include but are not limited to: an operating system, one or more application programs, other program modules, and program data. The implementation of a network environment may be included in each or some combination of these examples.

[0055] The bus 330 may represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any bus structure in a variety of bus structures.

[0056] The electronic device 300 can also communicate with one or more external devices 400 (such as a keyboard, a pointing device, a Bluetooth device, etc.), and can also communicate with one or more devices that enable the audience to interact with the electronic device 300, and / or communicate with any device that enables the electronic device 300 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 350. Moreover, the electronic device 300 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 360. The network adapter 360 can communicate with other modules of the electronic device 300 through the bus 330. It should be understood that although Figure 3 not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0057] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described in the present invention can be implemented by software, or can be implemented by the way of software combined with necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, and the software product can be stored in a computer-readable storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on the network, including several instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the present invention. When the computer program is executed by a data processing device, the computer-readable medium can implement the above method of the present invention, that is: as Figure 1 shown in the method.

[0058] Figure 4 is a schematic diagram of the principle of a computer-readable medium provided by the embodiments of this specification.

[0059] Implement Figure 1The computer program of the method shown can be stored on one or more computer-readable media. The computer-readable media can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0060] The computer-readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, in which the readable program code is carried. Such a propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted with any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the above.

[0061] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the viewer's computing device, partially on the viewer's device, executed as a stand-alone software package, partially on the viewer's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the viewer's computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0062] In summary, the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that general-purpose data processing devices such as microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing some or all of the methods described herein. Such a program for implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, or provided on a carrier signal, or in any other form.

[0063] In the specific embodiments described above, the object, technical solution and beneficial effects of the present invention are further described in detail. It should be understood that the present invention is not inherently related to any specific computer, virtual device or electronic device, and various general-purpose devices can also implement the present invention. The above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0064] Each embodiment in this specification is described in a progressive manner, and the same or similar parts among the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0065] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.

Claims

1. A makeup assistance method based on an intelligent makeup mirror, characterized in that, Including: Obtain user voice instructions and user facial appearance data; Based on the analysis of the user instructions, obtain makeup requirement keywords; Based on the makeup requirement keywords and the user facial appearance data, obtain a makeup teaching video and display the makeup teaching video through an intelligent makeup mirror.

2. The makeup assistance method based on an intelligent makeup mirror according to claim 1, wherein After obtaining the user facial appearance data, it further includes: Based on the analysis of the user facial appearance data, obtain the user's facial contour curvature, relative positions of facial features, skin texture features, and user skin color classification; Based on the user's facial contour curvature, relative positions of facial features, and skin texture features, construct a user's three-dimensional facial model; Perform zoning processing on the three-dimensional facial model to obtain a contour zoning suitable for the user's facial appearance; Based on the user skin color classification and skin texture features, determine the target cosmetics and their color numbers.

3. The makeup assistance method based on an intelligent makeup mirror according to claim 2, wherein After obtaining the makeup teaching video based on the makeup requirement keywords and the user facial appearance data and displaying the makeup teaching video through the intelligent makeup mirror, it further includes: Real-time obtain the image of the holding posture of the makeup brush; Analyze the image of the holding posture of the makeup brush to obtain the model of the makeup brush; Based on the model of the makeup brush, obtain the optimal dipping amount and the type of cosmetics.

4. The makeup assistance method based on an intelligent makeup mirror according to claim 3, wherein, After obtaining the optimal dipping amount and the type of cosmetics based on the model of the makeup brush, it further includes: Real-time obtain the image of the makeup brush dipping cosmetics; Analyze the image of the makeup brush dipping cosmetics to predict the dipping amount of the makeup brush; When the dipping amount of the makeup brush exceeds the threshold, activate the LED ring-shaped light strip on the edge of the mirror to emit a red light warning and trigger the air pump device to blow away the excess powder.

5. The makeup assistance method based on an intelligent makeup mirror according to claim 4, wherein, After obtaining the makeup teaching video based on the makeup requirement keywords and the user facial appearance data and displaying the makeup teaching video through the intelligent makeup mirror, it further includes: Real-time obtain the facial image during the user's makeup process; Extract the facial image during the user's makeup process through an image segmentation model to obtain the boundary between the made-up area and the non-made-up area; Compare the boundary with the expected boundary of the target makeup in the makeup teaching video to generate a boundary offset; Based on the boundary offset, dynamically adjust the playback progress of the makeup teaching video and overlay a correction trajectory prompt layer.

6. The makeup assistance method based on an intelligent makeup mirror according to claim 5, wherein After obtaining the makeup teaching video based on the makeup requirement keywords and the user facial appearance data and displaying the makeup teaching video through the intelligent makeup mirror, it further includes: Real-time obtain the environmental light intensity and color temperature data; Calculate the color rendering deviation of the target cosmetics color number under the environmental light intensity and color temperature data through a light simulation model; When the color rendering deviation exceeds the preset value, automatically screen color number alternative schemes from the makeup teaching video and display a comparison chart of the virtual makeup trial effects of the original scheme and the alternative scheme side by side on the mirror display interface.

7. A makeup assistance device based on an intelligent makeup mirror, characterized in that, Including: An acquisition module for acquiring user voice instructions and user facial appearance data; An analysis module for obtaining makeup requirement keywords based on the analysis of the user instructions; An auxiliary makeup module, configured to obtain a makeup teaching video based on the makeup requirement keywords and the user's appearance data, and display the makeup teaching video through a smart makeup mirror.

8. An electronic device, wherein, The electronic device includes: a processor; and, a memory storing computer-executable instructions, the executable instructions, when executed, causing the processor to execute the method according to any one of claims 1-6.

9. A computer-readable storage medium, wherein, The computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method according to any one of claims 1-6 is implemented.

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