An image RGB color space-based skin surface roughness detection method

Abstract

The invention provides a skin surface roughness detection method and belongs to the application field of skin image processing. Image processing is performed on macro-skin digital images with the same resolution ratio, and color space pixel values of the skin images are directly used to calculate a mean value of deviation absolution values, which is used as a roughness feature value of the skin images to indicate skin roughness. According to the method, color space pixel values are directly used for roughness feature value detection; image data are read and graying is performed; skin roughness can be detected only through pixel-level processing on images; a calculated roughness value not only reflects the influence of texture, but also reflects the influence of pores on the skin image roughness; with high detection accuracy and speed, the method enjoys a good application prospect and high market value.

Description

technical field [0001] The invention belongs to the application field of skin image processing, relates to skin roughness detection technology, in particular to a skin surface roughness detection method based on image RGB color space. Background technique [0002] Skin surface state detection is an important means of skin aging and anti-aging research, and it is also one of the important indicators for the objective evaluation of skin care products. With the improvement of people's living standards, the beauty and care of facial skin has received widespread attention. How to objectively and quantitatively analyze and identify skin images has become one of the research hotspots in the field of skin image processing in recent years. One of the important applications It is the detection and analysis of the roughness of the skin surface. [0003] At present, the skin surface state detection technology at home and abroad is still relatively backward. Most of the existing methods...

AI Technical Summary

Problems solved by technology

This method cannot directly obtain the roughness index according to the texture feature value, and this type of method ignores the influence of pores on skin roughness, and pore features are one of the important factors affecting skin roughness

The existing skin surface state detection methods also include machine learning methods, which detect the skin surface roughness state through the storage and learning of a large number of skin images. This method is related to the number and quality of learning samples, and the accuracy of roughness detection needs to be improved. , and the learning cost is high, and the detection calculation time is long

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