Facial Skin Quality Detection And Evaluation System

With the rapid development of medical beauty technology and cosmetics industry,people are paying more and more attention to their skin quality.At present,the mainstream skin quality detection method mostly uses large-scale mechanical skin detection equipment,but this method has certain limitations in terms of real-time and portability.Therefore,how to detect and evaluate the skin quality objectively,conveniently and accurately is a problem that needs to be solved urgently in the field of medical cosmetology.This thesis takes the quality of facial skin as the research object,researches on skin quality detection algorithm,and develops a set of facial skin quality detection and evaluation system which based Android.The system achieves the goal of real-time and convenient skin quality detection.The specific research content of this thesis is as follows:(1)This thesis constructs a database of facial skin quality images and studies related detection indicators of skin quality.This thesis collected the facial skins of 142 volunteers of different ages and genders,and obtained 12,875 skin images.At the same time,based on the description of skin quality in the field of medical cosmetology,eight common skin information,such as skin oil,skin color,acne,pores,spots,deep wrinkles,dry lines and nevus,were selected as the detection indexes in this thesis.And the grading standards for each index have been formulated,and the database labeling work has been completed in this thesis,which provided the data basis for the study of skin quality detection algorithm in this thesis.(2)This thesis proposes a skin quality detection algorithm based on feature fusion and multilabel multi-classification.Firstly,the thesis selects five classic feature extraction operators as candidate features.By designing feature selection experiments based on the Bo W model and SVM classifier,the best characterization features are selected for each skin detection index.According to the above experimental results,the skin image's SURF feature,LBP feature and color moments of the skin image are weighted and fused.The skin image is preprocessed by wavelet threshold denoising,and the features are dimensioned and reduced by normalization and principal component analysis.Finally,the random forest model is used to train the classifier to realize the task of multi-label and multi-classification of facial and skin quality images.Experimental verification shows that the average detection accuracy of the algorithm reaches 87.5%,which is a certain degree of improvement compared to the single feature detection algorithm.(3)This thesis proposes a skin quality detection algorithm based on improved Mobile Net V3 network.This thesis is based on the lightweight convolutional neural network Mobile Net V3,and uses the fusion dual attention mechanism to replace the original SE module to achieve the improvement and optimization of the network structure.Then,the improved Mobile Net V3 network is used to train the self-built dataset in this thesis.By comparing the performance of the improved network with the original Mobile Net V3,VGG19 and Resnet18 in terms of detection accuracy,number of parameters and reasoning speed,it is verified that the improved network in this thesis has high accuracy,small number of parameters and fast speed.(4)This thesis develops and implements a set of Android-based facial skin quality detection and evaluation system.The system selects the trained Mobile Net V3 network with dual attention mechanism as the skin quality detection model of the system.The system realizes the detection of eight indexes on the skin images which are read locally.At the same time,this thesis constructs an evaluation system for facial skin quality,comprehensively evaluates skin quality according to skin water and oiliness,skin tone and various blemishes,and generates a total skin quality score.The system develops the functional modules such as login registration,skin quality detection and evaluation,and history detection record.Finally,the system developed in this thesis was tested and the compatibility test was carried out,and the system was verified to have good performance in the aspects of skin quality detection and system compatibility.