Analysis Of Facial Expression Features Based On Depth Learning

In the whole social communication process of human society,facial expression plays an important role in information transmission media,and plays an important role in coordinating interpersonal relationship.Psychologist A.Mehrabian finds it through research,in human daily life,the proportion of information conveyed by facial expression occupies more than half of the total information.Therefore,the research on facial expression is also popular,since the last century in 70 s,automatic facial expression analysis of the characteristics has driven a lot of researches,including human-computer interaction,intelligent control,machine vision,affective computing,etc.,it mainly focuses on automatic expression recognition.Because the classification and recognition of facial expression features can be very valuable for scientific research or business,and then we can re-use the obtained facial expression information to discover greater value,including business and other aspects.This paper references to the data of a large number of domestic and foreign literature,based on automatic facial expression feature analysis,the relevant research background,significance and related technologies are analyzed and discussed in detail.In the early stage,the technology applied in traditional image recognition technology was analyzed,which mainly focus on image processing,it is reflected in the preliminary processing of the initial samples.,the main purpose is to reduce the data acquisition of light and noise brought by the late in error,grey preprocessing and geometric preprocessing can be adopted,Such as rotary cutting,image location,geometric lighting normalization and so on,early treatment is more conducive to feature extraction and classification accuracy of the following expressions is improved,then for the pre-processed data set for further feature extraction and selection,including the extraction before the relevant research institute of the Gabor,wavelet transform SIFT and PCA dimension of facial expression image features,and then through the Adaboost algorithm for feature selection,and finally take the popular points Class algorithms include KNN,SVM,neural networks,and HMM for classification of facial features.After taking a CNN network model based on deep learning,applied to the face recognition data sets,the method of feature extraction based on typical feature regions and LBP texture operator is proposed before,and to optimize the parameters and structure of network model based on the experimental results,so as to achieve a standard optimization model,and the use of deep learning including model generalization ability,the simple operation and other advantages,compared with the previous image recognition method,in order to reflect the characteristics of CNN face image recognition based on deep learning,mainly in the high accuracy and easy operation.The whole experiment is carried out under the Tensorflow platform to realize multi classification facial expression recognition.