Most face landmark detection algorithms include two steps:face detection and face landmark localization, increasing the processing time. Aiming at the problem, a one-step and real-time algorithm for multi-face landmark localization was proposed. The corresponding heatmaps were generated as data labels by the face landmark coordinates. Deep residual network was used to realize the early feature extraction of image and feature pyramid network was used to fuse the information features representing receptive fields with different scales in different network depths. And then based on intermediate supervision, multiple landmark prediction networks were cascaded to realize the one-step coarse-to-fine facial landmark regression without face detection. With high accuracy localization, a forward propagation of the proposed algorithm only takes about 0.0075 s (133 frames per second), satisfying the requirement of real-time facial landmark localization. And the proposed algorithm has achieved the mean error of 6.06% and failure rate of 11.70% on Wider Facial Landmarks in-the-Wild (WFLW) dataset.