Face frontalization is a critical and difficult task to face pose reconstruction. Previous researches use simple posture information as guidance, such as pose coding and facial landmarks. To explore the guidance effect of profile faces, we propose detailed features that provide much detailed information. In this paper, we devise Detailed Feature Guided Generative Adversarial Pose Reconstruction Network (DGPR). Firstly, frontal pose coding and profile detailed features are fed into DGPR to generate detailed features of front face. Then, the second generator combines frontal detailed features and profile face to obtain front face. Besides, we propose a conditional enhancement loss to strengthen the guiding role of detailed features, and a smoothing loss to reduce edge sharpness in generated faces. Experimental results show that our method generates photorealistic front faces and outperforms state-of-the-art methods on M 2 FPA and CAS-PEAL. Specifically, DGPR improves the face recognition accuracy under ±60 • , ±75 • , ±90 • by 2%, 1%, and 6% respectively, compared with state-of-the-art methods on M 2 FPA. And the average rank-1 recognition rate achieved 99.95% on CAS-PEAL, which is improved by 0.05%. The results demonstrate the effect of detailed features and corresponding modules.