Aimed at the detection difficult problem of feather quill crease, the idea of non-linear manifold is introduced into crease target recognition. A feather quill crease recognition method based on locality preserving projection and manifold kernel function is proposed for feature extraction. Firstly, covariance matrices are computed as the crease descriptors of feather quill, and an affine invariance metric which is adopted to make the space meet the requirement of Riemannian manifold is used to measure the distance between the two samples. Secondly, the neighbors of a selected point can be determined by the proposed manifold kernel function to make choice of the nearest neighboring points in line with the hypothesis of data distribution with non-linear manifold. The kernel matrix is defined based on the manifold distance and category labels. Finally, the locality preserving projections algorithm is used to reduce the dimensionality of the feather quill images. The simulated experiment results suggest that the proposed algorithm is robust to the variation of illumination and residual noises image segmentation, and achieves better performance compared with many popular recognition algorithms．