Research On Facial Expression Recognition Algorithm In Natural Environment

With the rapid development of computer hardware and software,the arrival of the era of big data,people's demand for intelligent human-computer interaction is increasing.To promote the application of intelligent human-computer interaction technology in reality,it is necessary to conduct in-depth research on facial expression recognition in the natural environment.This paper mainly studies the static facial expression recognition algorithm in the natural environment.In the image preprocessing stage,we first introduce the database used to study expression recognition in the natural environment.Then,using skin-based segmentation technology,the face region is initially and quickly located,and then the Adaboost algorithm is used to accurately locate the face and the human eye.Based on the position information of the eyes,the face alignment work is completed.A 2D*2.2D face region clipping standard is proposed through experiments,and the face pure expression area of the SFEW database image is successfully obtained.Finally,the tasks of image scale normalization,noise filtering and contrast enhancement are completed by first-order interpolation,median filtering and histogram equalization.In the feature extraction part,the LBP operator can not reflect the shortcomings of the relationship between neighboring pixels.An improved LBP operator can be considered,which can take into account the relationship between the neighborhood pixels and the relationship between the neighborhood and the center point.The fusion feature extraction algorithm of Gabor wavelet + basic LBP+PCA and Gabor+improved LBP+PCA is proposed.These two fusion algorithms can extract Gabor wavelets to extract different directions and size texture features.LBP algorithm can effectively reduce illumination interference.PCA algorithm Can eliminate the advantages of redundant information.In the classification algorithm part,the performance of the KNN classifier with different K values and the SVM classifier is compared.It is proved that the SVM classifier has better comprehensive ability than the KNN classifier in the expression recognition classification.In the expression recognition part,an expression recognition system was designed on the MATLAB platform using the SVM classifier.Experiments were carried out on four expression databases to verify the effectiveness of the fusionfeature algorithm proposed in this paper.The reasons for the differences in the recognition of different expressions in the SFEW database were analyzed and a solution was proposed.