Multi-Scale Aggregation Network for Direct Face Alignment

Abstract

Face alignment has been extensively researched in computer vision while remaining a challenging task. Direct face alignment based on convolutional neural networks (CNN) without relying on cascaded regression has recently emerged and achieved promising performance. In this paper, we propose a multi-scale aggregation network (MAN) for direct face alignment by aggregating features from intermediate layers of a CNN. Specifically, MAN adopts a new convolutional architecture to aggregate features at all scales in different semantic levels, which establishes highly informative facial representations for accurate alignment. Moreover, we introduce the attention mechanism into the network, which drives it to focus on the spatial regions closely related to facial landmarks for further improved performance. Our MAN achieves a general end-to-end learning architecture for multi-scale feature aggregation, which, coupled with spatial attention mechanism, is well-suited for direct face alignment. Extensive experiments conducted on four benchmark datasets, including AFLW, 300W, CelebA and 300VW, show that MAN consistently produces high performance and surpasses several state-of-the-art methods in most cases.