Facial landmark localization plays a critical role in many face analysis tasks. In this paper, we present a novel local-global aggregate network (LGA-Net) for robust facial landmark localization of faces in the wild. The network consists of two convolutional neural network levels which aggregate local and global information for better prediction accuracy and robustness. Experimental results show our method overcomes typical problems of cascaded networks and outperforms state-of-the-art methods on the 300-W [1] benchmark.