Binary gradient pattern (BGP) is a concise and efficient descriptor for face recognition which is robust to light, expression and occlusion. It has achieved remarkable results in face recognition applications. However, BGP descriptor is a universal operator, which does not reflect the particularity of human face in the process of face recognition. This paper draws on the experience of human face recognition based mainly on facial features(including eyes, nose and mouth), and proposes a method of face recognition based on heuristic information. This method firstly determines the general location of the above facial features according to human experience. Secondly, BGP operator is used to extract the features of the face, and we divide faces into several sub-blocks to obtain the histogram features of each sub-block. Finally, the features corresponding to the positions of the features are weighted. The method is fully validated in Yale and ORL libraries. Compared with the original BGP method, the recognition accuracy and robustness are significantly improved.