Low resolution is an important factor that affects the accuracy of face recognition. To overcome the limitation of low-resolution facial images on face recognition， one effective solution is adopting super-resolution methods to reconstruct low-resolution images and then identify the generated facial images. However， existing super-resolution methods typically fail to consider facial identity preservation during reconstruction， which directly results in poor face recognition performance of reconstructed images. To address the issue mentioned above， this paper proposes a face super-resolution reconstruction method with identity preserving， called IPNet. This method can simultaneously improve the quality of low-resolution facial images and preserve the identity of reconstructed images. IPNet consists of a semantic segmentation network and a face generator. The semantic segmentation network is introduced to extract low-dimensional latent code and multi-resolution spatial features. Then， the extracted features guide the face generator to output super-resolution images similar to the authentic images. Furthermore， we introduce the face recognition network to integrate the face identity information into the super-resolution model， thus maintaining the identity of reconstructed facial images consistent with original images. Experimental results show that IPNet achieves better results than other comparison methods in terms of both super-resolution image quality and identity preservation， demonstrating effectiveness of the proposed method.