The Research Of Facial Expression Recognition Based On Multi-feature And Combining Multiple Classifiers

Facial expression recognition is one of the most challenging research topics in the fields of artificial intelligence and human-machine interaction. Aiming at letting computers recognize human facial expressions automatically. Consequently analyzes emotions and psychology. This will further strengthen the friendliness and intelligence of human-computer interaction. It has both high research value and wide range of potential market value.Facial expression recognition system consists of such modules as face detection, feature extraction and expression classification. This paper mainly studies a number of key issues in the process of feature extraction and expression classification, analyzes the deficiencies in the existing algorithms. Several improved algorithms and methods for these tasks are developed. And simulation experiments are did. The major contributions of this paper are as follows:Firstly, improve the Gabor feature extraction algorithm. We propose a new Gabor features dimension reduction method that utilizes estimation of distribution algorithms (EDA) to search optimal Gabor kernels'scales and orientations. Experimental results on JAFFE database demonstrate that our method is more effective for both dimension reduction and image representation than traditional Gabor filter bank.Secondly, propose two directional two dimensional direct LDA((2D)2DLDA) algorithm. DLDA algorithm first works in row direction of image and then works in the column direction of image to directly extract the image scatter matrix from 2D image. Compress the dimensions from column direction to row direction, reduce the feature dimensions. Experimental results on JAFFE database demonstrate that our proposed algorithm not only enhance the ability to reduce dimensions, but also improve the recognition rates.Thirdly, apply the two-dimensional discriminant locality preserving projections ,(2D-DLPP) algorithm. Experimental results on JAFFE database show that 2D-DLPP is better than 2DLPP and LPP in expression feature extraction and classification. Lastly, propose a multi-feature and combining multiple classifiers method for facial expression recognition. We develop a model of combining multiple classifiers based on nerve net. The outputs of three classifiers are input to the model to get facial expression recognition. Experimental results on JAFFE database demonstrate that our proposed method is superior to the single feature and single classifier.The experimental results show that our improved Gabor feature extraction algorithm and proposed two directional two dimensional direct LDA algorithms not only enhance the ability to reduce dimensions, but also improve the recognition rates. Our proposed multi-feature and combining multiple classifiers method get satisfactory results. The high recognition rate reaches 1.43% on JAFFE database.