Research On Facial Expression Recognition Based On Texture Information

The technology of facial expression recognition is mainly applied in areas ofhuman-computer interaction,security,medical,communications,automotive and soon.Facial expression recognition is a topic that is full of challenginginterdisciplinary,because facial expression recognition has relation to the topicsthat included digital image processing,motion tracking,affectivecomputing,pattern recognition,physiology,machine vision,psychology andbiometrics.Expression of the human face contains a wealth of emotional andpsychological information that can reflect the thinking activity of brain.Thefacial expression recognition involves two major problems,one is how to get theexpression characteristics of the human face and the second is how to effectiveanalytical expression feature and get the correct identification.The main contentof this paper is as follows.1．In order to overcome the recognition effect expression recognition shiftand uneven illumination,an improved local binary pattern algorithm isproposed.The algorithm has strong rotational invariance and gray-scaleinvariance,and can tolerate a certain degree of image rotation and invariance.Inthis paper the traditional local binary pattern algorithm is improved,so thealgorithm is more robust and stable when the sample image has noise or a lowresolution.2．In the expression recognition the feature vector that is extracted by usinggabor wavelet has huge dimension,a method that combine gabor wavelet toextract the feature and sparse representation to reduce dimensions is proposed inthis paper.To reduce dimensions after using the gabor wavelet is the key to itsapplication.The traditional PCA algorithm can reduce the dimensions,but PCAdoes not take into account the distinction between the various types of characteristics.The theory of sparse representation is the projection of the signalto transform space and obtained a compact and accurate representation.Thereforeusing sparse representation to reduce the dimension can be beneficial to theaccuracy of the expression recognition.3．In response to the wavelet transform has obvious deficiencies whenextracting feature in the edges of the image,but image edge contains a wealth offacial expression information,so a method of facial expression recognition basedon curvelet feature is proposed in this paper.The curvelet feature contains lots ofthe edge information of the image, which is conducive to represent the feature offacial expression.In the different scales the curvelet coefficient of image containdifferent texture informations,the curvelet coefficient is extracted after using thecurvelet transform on the images and selecting the suitable curvelet coefficient asthe expression feature,which can describe the facial expressions texture very welland recognize the different facial expressions.