Computer Vision Skin Texture Analysis Using Facial Feature Points
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Solution Overview
Problem
Conventional methods for testing skin texture, such as bioelectrical impedance analysis, are limited in precision, require direct skin contact, and involve costly hardware, making them inconvenient and impractical for widespread use.
Innovation Solution
A method and device for testing skin texture using computer vision processing, which acquires face images, identifies face complexion regions and features, and analyzes skin texture features like freckles, pores, wrinkles, and smoothness through algorithms, without the need for direct contact or expensive hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bioelectrical impedance analysis is used to test skin texture, then electrical impedance features can be measured, but testing precision is poor due to contact impedance and limited features can be evaluated
Solution Approach 1:
The patent replaces the bioelectrical impedance analysis method with a computer vision-based optical system. Instead of using electrical impedance measurement devices that require skin contact, the invention uses image acquisition devices (cameras) to capture skin texture images and processes these images through algorithms to extract skin texture features. This substitution eliminates contact impedance issues and enables non-invasive, high-precision skin texture evaluation.
Solution Approach 2:
The patent creates a digital copy of the skin texture through image acquisition. By capturing the skin surface through a camera and processing the image data, the system generates a digital representation that preserves all skin texture information without requiring physical contact with the skin. This allows for precise measurement of multiple features simultaneously without the limitations of electrical impedance methods.
2Measurement precision
If professional hardware apparatuses are used for skin texture testing, then testing can be performed, but the method requires additional professional hardware and is limited to specific testing sites with high cost
Solution Approach 1:
The patent makes the skin texture testing system universal by using standard image acquisition devices that are already widely available in various settings. The system can be deployed in diverse environments including homes, salons, and clinics without requiring specialized testing sites. The computer vision approach allows the same device to perform multiple functions (image capture, skin texture analysis, and various skincare assessments) making it adaptable to different application groups and scenarios.
Solution Approach 2:
The patent enables self-service skin texture testing by using devices that can be operated by ordinary users without professional training. The automated image processing and feature extraction algorithms perform the complex analysis tasks that would otherwise require professional operators, allowing users to conduct their own skin assessments conveniently at any location.
3Ease of operation
If conventional manual observation is used to identify skin texture, then simple observation can be performed, but testing precision is insufficient for accurate skin texture evaluation
Solution Approach 1:
The patent introduces an intermediary computational system that bridges simple image capture and accurate skin texture measurement. The computer vision algorithms act as intermediaries, automatically processing the captured images to extract precise skin texture features. This intermediary layer maintains operational simplicity for users while achieving high measurement precision through automated image processing and feature extraction.
Data Source
AI summary
The disclosure discloses a method for testing skin texture, a method for classifying skin texture and a device for testing skin texture. The method for testing skin texture includes: a face image is; a face complexion region and face feature points in the face image are acquired; and a face skin texture feature from the face image is acquired according to the face complexion region and the face feature points.