Floor-Ladder Framework For Face Beautification Based On Neural Network And Marquardt Mask

Recently,the field of image and video processing has been interesting for scholars.Face beautification is the technique,which has been applied to process digital human face.Every person is selfish by nature,especially when it comes to photos.People always want to adjust the picture,taken by themselves,specifically as a self-portrait and are curious about the useful resource.Digital image processing is one of the challenging tasks of computer vision field and understanding human face on the image is the task which this field can easily handle.The recognized face on the image may be modified,beautified,decomposed and warped with the aid of extremely good techniques,to collect necessities for deformation.Therefore,studying face beautification techniques and developing different user-orientated editing frameworks,have a tremendous importance.The purpose of this thesis is aimed at the studies of image deformation strategies in terms of face beautification.Recently,researchers proposed many effective digital face manipulating methods.Nowadays,with a huge existence of programs,developing new high-tech digital cameras,with a fast development of the social networks,there has been an outstanding effect on the processing of human faces in images.Primarily based on the previous studies in psychology area,it is far determinate that more youthful faces obtain more attractiveness.Generally,face beautification technique can be divided into fundamental issues: face shape and face texture adjustments.Current approaches mean to apply the strategies one after the other.This thesis makes a peculiarity of face beautification approach based on the proposed technique of manipulating face shape and face texture collectively with newly proposed approaches.First of all,this thesis discusses and summarizes the commonplace principles of face attractiveness with regards to face shape and face beautification process.In the processing of face shape,primarily based on extracting face landmark points within the image,existed algorithms assume the usage of extracted points as a vector and,as a result,geometry modification is applied.Owing to the increase policies of the face shape,we expanded the geometric change technique to making use of lengthening skull model.Meanwhile,depending on the gender,either man or woman,Marquardt mask is applied.The facial skin and skin color texture with none spots or any face furrows play a prime role in facial beauty.In the processing of the face texture,we interpret the beautiful face via applying the reversed aging rules.Firstly,extracted points from the image form a mask,contour of the face,without eyes,nose,and mouth.Motivated via the layered optimization techniques,the layer decomposition algorithm is applied.Detail layer with the defined location is derived.Then according to an effective claim,we set up Single Layer Order Neural Network(SLONN)aiming to capture the reversed aging rules of the face skin precisely at the mask,which is mention above.The Peak Shift algorithm is designed to train the SLONN.Based on age evolution guidelines to produce digitalized beautified human faces and main techniques of face beautification,we propose a Floor-Ladder Framework(FLN),which adopts three flooring and each floor includes two ladders: Single Layer Order Neural Network(SLONN)and the Skull Model,prolonged by the Marquardt mask.The experiment results show that evaluating with different previous techniques of face beautification and some commercial systems,our work correctly produces a beautified human face without losing personal features.