Facial color data acquisition method based on light sensation conduction

Abstract

The invention discloses a face color data acquisition method based on light sensation conduction. The method comprises the following steps: S1, acquiring face color data by using a multispectral sensor; s2, using a light source with adjustable wavelength to irradiate the face; s3, preprocessing the collected face color data; s4, synchronously collecting and processing physiological data related to the face color data; s5, analyzing the face color data and the physiological data through an artificial intelligence model, identifying potential signs of health problems, generating a health analysis report and performing disease early warning; s6, in combination with the face color change trend and the physiological data change, time sequence analysis is carried out, and the health trend is predicted. According to the method, the accuracy and timeliness of health monitoring are greatly improved by combining facial color change with multi-dimensional physiological data monitoring and utilizing AI analysis and a time sequence prediction method, the risk that early health problems are not found in time can be effectively avoided, and the possibility of delayed treatment is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of data acquisition, and particularly relates to a method for acquiring facial color data based on light-sensing conduction. Background Art

[0002] The method for acquiring facial color data based on light-sensing conduction is a technology that acquires facial color information by using optical sensors (such as cameras, light-sensing devices, or laser scanners). This method obtains the color, texture, and other related features of the human face surface through physical phenomena such as the reflection, refraction, and scattering of light. Its core idea is to obtain color data related to facial skin through the conduction method of irradiating light sources and receiving light signals, and it is usually used in fields such as face recognition, facial expression analysis, and medical detection;

[0003] During health monitoring and medical detection, the use of facial color changes may reflect information such as blood circulation and skin health, and is used for health monitoring and disease warning. However, facial color changes often do not immediately reflect pathological changes and may have a certain time lag. When relying on this method for health monitoring, there may be a risk that early health problems are not detected in a timely manner, delaying the treatment opportunity. Summary of the Invention

[0004] The present invention is proposed to solve the problems existing in the prior art, and its purpose is to provide a method for acquiring facial color data based on light-sensing conduction.

[0005] The present invention is achieved through the following technical solutions:

[0006] A method for acquiring facial color data based on light-sensing conduction, comprising the following steps:

[0007] S1. Use a multispectral sensor to acquire the color data of the face;

[0008] S2. Irradiate the face with a light source with adjustable wavelength, and adjust the wavelength of the light source to adapt to the reflection characteristics of different facial regions;

[0009] S3. Preprocess the acquired facial color data

[0010] S4. Synchronously acquire and process the physiological data related to the facial color data

[0011] S5. Analyze the facial color data and physiological data through an artificial intelligence model, identify potential signs of health problems, generate a health analysis report and issue a disease warning;

[0012] S6. Combine the trend of facial color changes with the changes in physiological data for time series analysis and predict the health trend.

[0013] In the above technical solution, the multispectral sensor includes an infrared spectral sensor and at least one RGB camera.

[0014] In the above technical solution, the wavelength range of the infrared spectral sensor is 800nm - 2500nm; the resolution of the RGB camera is at least 1080p.

[0015] In the above technical solution, the adjustable - wavelength light source includes white - light LEDs, infrared light, and near - infrared light sources.

[0016] In the above technical solution, the wavelength range of the white - light LED is 400nm - 700nm.

[0017] In the above technical solution, the pre - processing includes denoising, light compensation, and color - space normalization.

[0018] In the above technical solution, the denoising uses an adaptive filtering algorithm to remove noise in the image; the light compensation uses the Gaussian light - compensation method to correct color deviation caused by uneven illumination; the color - space normalization is performed by the standard - deviation normalization method.

[0019] In the above technical solution, the normalization process of the facial color data includes normalizing the color channels, and the normalization formula is:

[0020]

[0021] where C(i,j) is the color value of the facial image at pixel i,j, μC is the mean of color channel C, σC is the standard deviation of color channel C, and C norm (i,j) is the normalized color value.

[0022] In the above technical solution, the physiological data related to the facial color data includes body temperature, heart rate, blood oxygen saturation, and blood pressure; the physiological data is monitored in real - time by a wearable device.

[0023] The beneficial effects of the present invention are:

[0024] The present invention provides a method for collecting facial color data based on light - sense conduction. By combining facial color changes with multi - dimensional physiological data monitoring, and using AI analysis and time - series prediction methods, it solves the time - lag problem between facial color changes and pathological changes. Through early detection, long - term data accumulation, and personalized health monitoring, it greatly improves the accuracy and timeliness of health monitoring, can effectively avoid the risk of early health problems not being detected in time, and reduces the possibility of delayed treatment. Description of the Drawings

[0025] Figure 1This is a flowchart of the method for collecting facial color data based on light-sensing conduction of the present invention.

[0026] For those of ordinary skill in the art, without creative efforts, other relevant drawings can be obtained based on the above drawings. Detailed implementation manners

[0027] In order to enable those in the technical field to better understand the technical solution of the present invention, the technical solution of the present invention will be further described below in conjunction with the drawings in the specification and through specific implementation manners.

[0028] Example 1

[0029] As Figure 1 shown, a method for collecting facial color data based on light-sensing conduction includes the following steps:

[0030] S1. Use a multispectral sensor to collect the color data of the face;

[0031] The multispectral sensor includes an infrared spectral sensor and at least one RGB camera;

[0032] The wavelength range of the infrared spectral sensor is 800nm - 2500nm, and the infrared spectral sensor is used to capture the reflected light signals of the facial skin at different wavelengths;

[0033] The reflected light signal R face (λ i ) of each wavelength λ i is the functional relationship between the reflection intensity and the light source intensity I λ as follows: R face (λ i ) = α · I λ · e -β·d ,

[0034] where: α is the reflection coefficient on the surface of the facial skin, β is the absorption coefficient of the facial skin, and d is the propagation depth of light in the skin;

[0035] The resolution of the RGB camera is at least 1080p,

[0036] By using a high-resolution RGB camera and an infrared spectral sensor with a wide wavelength range, the system can accurately capture the facial color changes, especially the reflection information at different wavelengths, which helps to identify subtle changes in facial health and early signs of diseases;

[0037] S2. Use a light source with adjustable wavelength to irradiate the face, and adjust the wavelength of the light source to adapt to the reflection characteristics of different facial regions;

[0038] The adjustable-wavelength light source includes a white light LED, an infrared light, and a near-infrared light source;

[0039] The wavelength range of the white light LED is from 400 nm to 700 nm, and its intensity can be adjusted according to the characteristics of the captured facial area. Using the adjustable-wavelength white light LED light source can provide optimized illumination under different environmental and facial area conditions, ensuring more accurate acquisition of facial color data, adapting to changes in different skin colors and skin types, and reducing errors caused by environmental interference;

[0040] The adjustment of the light source wavelength λ adjusted (k) is adjusted by controlling the light source current intensity I k where the light source adjustment formula is:

[0041]

[0042] In the formula: γj is the adjustment coefficient corresponding to each light source type, I k is the light source current at the k-th step, n is the number of different light sources, and ε is the deviation generated during the adjustment process;

[0043] S3. Preprocess the captured facial color data

[0044] The preprocessing includes denoising, illumination compensation, and color space normalization;

[0045] The denoising uses an adaptive filtering algorithm to remove noise in the image;

[0046] The illumination compensation uses the Gaussian illumination compensation method to correct the color deviation caused by uneven illumination;

[0047] The color space normalization performs color space normalization through the standard deviation normalization method

[0048] The preprocessing can effectively remove the influence of environmental noise, illumination, shadows, and other environmental factors, ensuring the accuracy of facial color data under different illumination conditions. At the same time, through color space normalization, the system can provide stable health monitoring results and avoid errors caused by external factors;

[0049] The normalization process of the facial color data includes normalizing the color channels, and the normalization formula is:

[0050]

[0051] In the formula, C(i,j) is the color value of the facial image at pixel i,j, μC is the mean of the color channel C, σC is the standard deviation of the color channel, and C norm(i,j) is the standardized color value. It can improve the performance, stability, and generalization ability of the model;

[0052] S4. Synchronously collect and process physiological data related to facial color data

[0053] The physiological data related to the facial color data includes body temperature, heart rate, blood oxygen saturation, and blood pressure; the physiological data is monitored in real time by a wearable device and is processed synchronously with the facial color data;

[0054] S5. Analyze the facial color data and physiological data through an artificial intelligence model, identify potential signs of health problems, generate a health analysis report, and issue disease warnings;

[0055] The artificial intelligence model is a convolutional neural network based on deep learning, which is used to jointly analyze the facial color data and physiological data, identify potential health problems, and generate a real-time health warning report. By using the convolutional neural network in deep learning technology, it can process complex facial color changes and physiological data, automatically identify potential health problems. The training of this model can be continuously optimized to improve the diagnostic accuracy and real-time performance, early warning of diseases, and help with early intervention;

[0056] S6. Combine the trend of facial color changes with the changes in physiological data, conduct time series analysis, and predict health trends to provide support for long-term health management.

[0057] The time series analysis is implemented through a long short-term memory network model, which uses the time series data of facial color changes and the change trends of physiological data to predict health trends and provide personalized health management suggestions. By using the LSTM model for time series analysis, it can capture the long-term trends of facial color data and physiological data, identify potential patterns of health changes, provide accurate health management suggestions for users, and predict the development of diseases in advance, enhancing the forward-looking nature of health monitoring.

[0058] The applicant declares that the above description is only a specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention fall within the protection scope and the disclosure scope of the present invention.

Claims

1. A method for collecting facial color data based on light perception conduction, characterized in that: It includes the following steps: S1. Use a multispectral sensor to collect the color data of the face; S2. Use a light source with adjustable wavelength to irradiate the face, and adjust the wavelength of the light source to adapt to the reflection characteristics of different facial regions; S3. Preprocess the collected facial color data S4. Synchronously collect and process the physiological data related to the facial color data S5. Analyze the facial color data and physiological data through an artificial intelligence model, identify potential signs of health problems, generate a health analysis report and issue disease warnings; S6. Combine the trend of facial color changes with the changes in physiological data for time series analysis and predict health trends.

2. The method for collecting facial color data based on light perception conduction according to claim 1, wherein: The multispectral sensor includes an infrared spectral sensor and at least one RGB camera.

3. The method for collecting facial color data based on light perception conduction according to claim 2, characterized in that: The wavelength range of the infrared spectral sensor is 800nm to 2500nm; the resolution of the RGB camera is at least 1080p.

4. The method for collecting facial color data based on light perception conduction according to claim 1, wherein: The light source with adjustable wavelength includes white light LEDs, infrared light, and near-infrared light sources.

5. The method for collecting facial color data based on light perception conduction according to claim 1, wherein: The wavelength range of the white light LEDs is 400nm to 700nm.

6. The method for collecting facial color data based on light perception conduction according to claim 1, wherein: The preprocessing includes denoising, light compensation, and color space normalization.

7. The method for collecting facial color data based on light sensing conduction according to claim 6, wherein: The denoising uses an adaptive filtering algorithm to remove noise in the image; the light compensation uses the Gaussian light compensation method to correct the color deviation caused by uneven illumination; the color space normalization is performed through the standard deviation normalization method.

8. The method for collecting facial color data based on light perception conduction according to claim 6, characterized in that: The normalization process of the facial color data includes normalizing the color channels, and the normalization formula is: Where C(i, j) is the color value of the facial image at pixel i, j, μC is the mean of color channel C, σC is the standard deviation of color channel C, and C norm (i, j) is the color value after normalization.

9. The method for collecting facial color data based on light-sensing conduction according to claim 1, wherein: The physiological data related to the facial color data includes body temperature, heart rate, blood oxygen saturation, and blood pressure; The physiological data is monitored in real time through wearable devices.

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