Head pose estimation and facial landmark localization are crucial problems which have a large amount of applications. We propose a cross-cascading regression network which simultaneously perform head pose estimation and facial landmark detection by integrating information embedded in both head poses and facial landmarks. The network consists of two sub-models, one responsible for head pose estimation and the other for facial landmark localization, and a convolutional layer (channel unification layer) which enables the communication of feature maps generated by both sub-models. To be specific, we adopt integral operation for both pose and landmark coordinate regression, and exploit expectation instead of maximum value to estimate head pose and locate facial landmarks. Results of extensive experiments demonstrate that our approach achieves state-of-the-art performance on the challenging AFLW dataset.