中国科学院自动化研究所机构知识库

Face detection and alignment are considered as two independent tasks and conducted sequentially in most face applications. However, these two tasks are highly related and they can be integrated into a single model. In this paper, we propose a novel single-shot detector for joint face detection and alignment, namely FLDet, with remarkable performance on both speed and accuracy. Specifically, the FLDet consists of three main modules: Rapidly Digested Backbone (RDB), Lightweight Feature Pyramid Network (LFPN) and Multi-task Detection Module (MDM). The RDB quickly shrinks the spatial size of feature maps to guarantee the CPU real-time speed. The LFPN integrates different detection layers in a top-down fashion to enrich the feature of low-level layers with little extra time overhead. The MDM jointly performs face and landmark detection over different layers to handle faces of various scales. Besides, we introduce a new data augmentation strategy to take full usage of the face alignment dataset. As a result, the proposed FLDet can run at 20 FPS on a single CPU core and 120 FPS using a GPU for VGA-resolution images. Notably, the FLDet can be trained end-to-end and its inference time is invariant to the number of faces. We achieve competitive results on both face detection and face alignment benchmark datasets, including AFW, PASCAL FACE, FDDB and AFLW.