For it difficult to distinguish faces and no-faces in the face detection, the paper proposes a new method based on the algorithm of Adaboost and PCA that can improve the accuracy of face detection. The method uses two-stage detector to detect faces, first it takes the algorithm of Adaboost as primary detector scan pictures of face quickly and merge all windows of face that be judged as face, then verify the correctness according to sending window’s features to secondary detector, that can excludes the windows of no- face, last verify the exporting parameters of face which includes the size and position of the window. Different algorithm test results show the accuracy of face detection based on this method was 92.6%, and the detection rate was 94.1%; the accuracy of based on the Adaboost was 62.5%, which was 88%; the accuracy of based on SVM was 54%, which was 89%; the accuracy of based on FSS was 66%, which was 92%. The experiment result shows that this method can distinguish human face pattern and non-face pattern. So in that sense, a two-stage detector of the cascade"s Adaboost and PCA algorithms can significantly improve the performance of the face detection system.