The dense and low-resolution face is difficult to be detected under the influence of attitude, occlusion and scale change. We propose a context-sensitive multi-scale face detection (CSMS) method to solve this problem. First, the CSMS method introduces an extraction module which combines the face context information to enrich the discriminant information by effectively fusing the features of multiple receptive fields. Secondly, from the point of view of model structure design, the CSMS method uses multi-scale features to extract scale-specific feature vectors and achieve the robust scale variety in face detection. In the training phase, the CSMS method adopts the end-to-end learning method, and introduces the training method focusing on the hard negative examples to solve the class imbalance problem in the small-scale target detection, and improves the ability of the network to distinguish the difficult examples. Experimental results show that the proposed method is robust in unconstrained environments and achieves advanced detection performance on the Wider Face dataset.