In recent years， the algorithms of detecting facial landmarks in static images have been greatly improved. However， facial landmark detection and tracking are still challenging due to the changes of the factors such as head posture， occlusion and illumination in real videos. In order to solve this problem， a video facial landmark tracking algorithm based on multi-view constrained cascade regression was proposed. Firstly， the 3-dimensional and 2-dimensional sparse point sets were used to establish a transformation relationship and estimate the initial shape. Secondly， due to the large posture difference between face images， affine transformation was used to correct the pose of the face images. When the shape regression model was constructed， the multi-view constrained cascade regression model was used to reduce the shape variance， so that the learned regression model had stronger robustness to the shape variance. Finally， a reinitialization mechanism was adopted， and Normalized Cross Correlation （NCC） template matching tracking algorithm was used to establish the shape relationship between consecutive frames when the feature points were correctly located. The experimental results on the public data set used for testing show that the average error of the proposed algorithm is less than 10% of the interocular distance.