边缘图像是人脸图像的一种重要表征方法，它对人脸图像的分析有重要的作用。提出了一种提取人脸二值边缘图像的新方法。该方法利用小波变换进行图像的高频重构，并利用其多尺度分析特性进行图像边缘的提取，包括两次二值化过程和一次去噪过程。提取的边缘图像具有较高的质量，同一人脸部件的边缘连通性好，不同部件的边缘粘连现象少，且具有较好的光照鲁棒性。将所提取的二值边缘图像用于AR库和Yale库图像的人脸4种动作单元的识别，识别率在93％以上，初步表明其适合于人脸图像的表情分析。

Edge map is an important representation of human face image. It is of significance for automatic face image analysis. This paper presents a new method for the extraction of face image edges and a scheme for facial expression analysis based on the resulting binary edge images (BEI). Making use of the multi-resolution property of wavelet transform, the edge detection method includes two binarization steps and a noise removing step. Our method provides a robust solution to face image edge extraction, in particular, if changes of lighting conditions are encountered. Experimental results show that different components in a BEI are well separated and pixels of the same face component are connected well. Preliminary experimental results on facial expression analysis on the subsets of AR and Yale face databases show that recognition rates of above 93% can be achieved when our facial analysis scheme is used for recognizing four action units.