The traditional face annotation methods for news images mainly rely on similarity information of the faces, and have poor ability to distinguish non-noise faces from noise faces and to annotate non-noise faces. Aiming at this issue, a face annotation method based on multi-modal information fusion was proposed. Firstly, according to the co-occurrence relations between faces and names, face-name match degrees based on face similarity were obtained by using a modified K-Nearest Neighbor (KNN) algorithm. After that, face importance degrees were characterized by the size and position information of faces extracted from images, and name importance degrees were characterized by the name position information extracted from images. Finally, Back Propagation (BP) neural network was applied to fuse the above information to infer labels of faces, and an annotation result correcting strategy was proposed to further improve the annotation results. Experimental results on Label Yahoo!News dataset demonstrate that the accuracy, precision and recall of the proposed method reach 77.11%, 73.58% and 78.75% respectively; compared with the methods only based on face similarity, the proposed method has outstanding ability to distinguish non-noise faces from noise faces and to annotate non-noise faces.