Research On Skin Detection Algorithm Based On Deep Learning And Density Peak Clustering

Skin detection technology is one of the basic research topics of human body pattern recognition.It has a vital application in the fields such as medical,web image filtering,gesture recognition and facial expression recognition.Although there are many skin detection algorithms now,it also has so much works to do.In this thesis,we deeply study on three stages of skin detection,namely: the pretreatment stage,the judgment stage and the post-processing stage of skin detection technology,and propose some improved algorithms about the detection method.The main contents and innovations can be summarized as follows:(1)Uneven distribution of light illumination in the shooting procedure is easy to produce an image whose gray values at different pixels are different,which always increases obstacles for the skin detection process.This thesis reduces the effect of uneven illumination phenomenon by combining Z-score normalization and convolution transformation in pretreatment stage of skin detection technology.Simulation results show that this method modifies the gray value of the image and represents a significant advantage.The main advantage of this algorithm is that it can compensates not only lower gray value but also higher gray value.Moreover,it can changes the gray values for an entire image mostly into an interval between 76 and175.This new algorithm increases the efficiency for the skin detection.(2)The traditional skin detection method is realized by establishing an underlying characteristic model of skin pixels firstly and then finishing the detection by checking whether the characteristic of the candidate pixel belong to range of the feature information.However,this type of detection method only takes into account the basic characteristics of skin pixels(such as YCbCr color space characteristics),and does not establish a deeper level of skin pixel characteristics.Furthermore,it must have some limitations in the application procedure.This thesis proposes a method that establishing the characteristics of the skin pixels by the depth learning algorithm in the judgment phase of skin detection technology,and realizing the skin detection by comparing the characteristics of the image to be examined with the real skin pixels.Simulation results show that this algorithm has a positive effect on the skin detection procedure when compared with other skin detection algorithms,it not only improves the positive rate but also reduces the false positive rate.(3)The approximated skin area of complex images always cause interference to the skin detection algorithm.This thesis reduces the interference by using the density peak clustering analysis algorithm in the post-processing stage of skin detection technology.Simulation results show that this algorithm has a positive effect on enhancing skin detection technology,it not only weaken the interference of similar skin pixels but also improve the detection rate of the skin.