Face Feature Extraction And Hairstyle Classification Based On Image

Face feature extraction is the key technology of facial image analysis.It is widely used in face recognition,face expression analysis,3D face reconstruction and other fields.However,the problem of inaccurate localization of face feature points still exists.Hair plays an important role in human appearance.Due to the lack of effective hairstyle classification technology,the authenticity of 3D face deformation and 3D fitting has been greatly reduced.Therefore,this thesis has carried on the related research to the above question,the research content mainly includes:(1)The detection of face existence and rationality.The preprocessed image is processed by skin color detection algorithm to enhance the face region in the image.The covariance matrix is used to reconstruct the coordinate system in the face region,and the threshold relation is constructed according to the position of the center point.Then we can construct the minimum external bounding box of the face(including the neck)region.According to the threshold relation,the existence and rationality of face are determined.(2)Face feature extraction.In order to obtain the face feature data in the test image,the Active Shape Model(ASM)algorithm is used to deal with the problem.The local contour model is constructed by using gray value information directly.The gray value is more sensitive to external natural factors.In this paper,the local contour model is constructed by using the edge direction of the feature points and the pixels of the positive and negative directions.The method can acquire the gray distribution characteristic of the image in the normal direction of the feature point and the related pixel point,effectively utilizing the image information.In the process of ASM searching,the translation,rotation and scale parameters of the template shape are determined based on the eye position information.Then,the feature points are matched by the improved search strategy to further improve the accuracy of feature point matching.(3)Hairstyle classification.An energy model based on color space and curve change is proposed to segment the hair region.Based on the image data of contour detection,the edge of hair is detected and segmented.The classification of the hairstyle of the front image is realized by using principal component analysis(PCA)and support vector machine(SVM).At the same time,the curvedness is defined by the curvature of each pixel point on the contour curve.A local search algorithm based on curvedness is proposed to classify the length of the side hair.Then,the local area search method of curvature and the experience distance are used to achieve the braided hair hairstyle classification.Through the experiment,the feasibility of the above method is verified.The experimental results show that the improved ASM method achieves good feature point localization results,and the hairstyle classification algorithm in this thesis has also got a good hair classification result.Finally,this thesis analyzes the shortcomings of face feature extraction and hairstyle classification,and puts forward the further research direction.