Facial skin quality evaluation method

Abstract

The invention discloses a facial skin quality evaluation method, which comprises the steps of collecting a facial image; graying the facial image through an open source library, and determining coordinates of the face; constructing a mask image of a facial skin region; segmenting the facial image; performing preprocessing and feature extraction; training a classification model of a support vectormachine (SVM) with the classification type being C_SVC, the kernel type being polynomial kernel POLY and the kernel parameter degree being 1.0 so as to obtain a trained SVM classifier; marking the recognized facial acne position with a black circle, and marking the recognized wrinkle position with a black box; and evaluating the skin quality according to the number of the recognized acnes and thefacial proportion occupied by the wrinkles. The facial skin quality evaluation method can eliminate the subjectivity of manual evaluation by judging after learning. Meanwhile, the facial skin qualityevaluation method unifies the brightness, performs Gamma correction and graying, extracts original LBP features and carries out histogram equalization, thereby reducing the calculation amount, and improving the real-time performance and the self-adaptation.

Description

technical field [0001] The invention relates to a facial skin quality evaluation method, which belongs to the fields of facial recognition and skin quality evaluation. Background technique [0002] Reasonable evaluation of facial skin quality plays an important role in the beauty industry, dermatology and plastic surgery and other fields. At present, products such as skin testers on the domestic and foreign markets have the disadvantages of being expensive, bulky, and inconvenient to carry. If the manual evaluation method is adopted, it is difficult to achieve the goal of consistent evaluation because different people generally have different evaluations on the same skin sample. It is very important for dermatologists to provide objective results without human subjectivity. [0003] Machine vision can avoid repetition and subjective consciousness in the evaluation of skin quality, so machine vision has strong application value in the field of non-clinical skin. Facial ski...

AI Technical Summary

Problems solved by technology

However, the former is generally sensitive to external conditions such as lighting, and has poor adaptability. The latter generally involves a large number of optimization algorithms, requires a large amount of calculation, and has poor real-time performance.

For facial acne and wrinkle detection, there are mainly detection technologies based on image processing, but this detection method is often poorly adaptive, and different parameter settings may be required for different skin samples

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