Summary: The aim of this paper is to improve the recognition rate of the wavelet-based methods for face recognition. A multiscale face recognition method based on second-generation curvelet transform is proposed. All face images are decomposed by using digital curvelet transform via wrapping. Curvelet coefficients of low frequency and high frequency in different scales and of various angles are obtained. Most significant information of faces is contained in the low frequency coefficients which are important for face recognition. Then, the low frequency coefficients are applied as study samples to the BP neural network. Finally, low frequency coefficients of some test face images are used to simulate the neural network to get the face recognition results. The experiments that are performed on the Cambridge university ORL database show that the proposed method has better performance than the wavelet-based method, and that the recognition rate is improved to 95%(with 2.5% improvement).