Two-dimensional Fisher linear discriminant analysis is an i mportant feature extraction method for face recognition.However,the method needs many coefficients to represent feature ma trices of images.Moreover,it only considers the correlation of the image between columns,which ignores the correlation betw een the lines.To solve the above problems, this paper proposes a block-based bi-directional Fisher linear discriminant analysis algorithm.First,the blo ck image is used to obtain the important local information.Then,the extracted feature information is obtained based on bi-dir ectional projection.Finally,the Frobenius distance of the feature matrix is calculated and classified.We have carried out experiments on ORL,YALE and FERET face databases,and make comparison wi th other methods of face recognition.Under the premise of determining the size of the image block,changing the number of training samples and the feature dimensions,the best recognition rate of the proposed method is higher than 93.08,and the average error rate is higher than 0.15,which are obviously superior to those of other methods.It shows that this method has high robustness to illumination,fac ial expression and occlusion in face image recognition.