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There is the most characteristic information in the regions of the eyebrows, eyes and mouth about facial expressions. In order to make full use of these features, reduce the amount of unavailable information in the image and occupation of the memory during the recognition process and improve the accuracy and speed of facial expression recognition. Firstly, Haar and AdaBoost face detection algorithms are used to recognize the human face in the image, get face images, and extract eyebrows, eyes and mouth. Generate the binaryzation of image of the eyebrow, eye and mouth. Getting the image of descending dimension by PCA algorithm and a column vector was composed of image of binaryzation and image of descending dimension. The samples are generated by an expression database and trained by neural network samples for facial expression recognition. The results show that the speed of facial expression recognition is faster than that of Gabor algorithm; The recognition accuracy is higher than that using the PCA algorithm and the neural network algorithm alone;The consumption of memory is less than that of the Gabor algorithm and the operation is smoother. The conclusion is that the local feature information is preserved as much as possible, because the eyebrows, eyes and mouths which contain the facial feature information are extracted, the accuracy of facial expression recognition is improved. At the same time, the PCA algorithm is used for the image of descending dimension and reduce the redundancy of information effectively.