Multi-view face detection in open environment is a challenging task due to
diverse variations of face appearances and shapes. Most multi-view face
detectors depend on multiple models and organize them in parallel, pyramid or
tree structure, which compromise between the accuracy and time-cost. Aiming at
a more favorable multi-view face detector, we propose a novel funnel-structured
cascade (FuSt) detection framework. In a coarse-to-fine flavor, our FuSt
consists of, from top to bottom, 1) multiple view-specific fast LAB cascade for
extremely quick face proposal, 2) multiple coarse MLP cascade for further
candidate window verification, and 3) a unified fine MLP cascade with
shape-indexed features for accurate face detection. Compared with other
structures, on the one hand, the proposed one uses multiple computationally
efficient distributed classifiers to propose a small number of candidate
windows but with a high recall of multi-view faces. On the other hand, by using
a unified MLP cascade to examine proposals of all views in a centralized style,
it provides a favorable solution for multi-view face detection with high
accuracy and low time-cost. Besides, the FuSt detector is alignment-aware and
performs a coarse facial part prediction which is beneficial for subsequent
face alignment. Extensive experiments on two challenging datasets, FDDB and
AFW, demonstrate the effectiveness of our FuSt detector in both accuracy and
speed.

本文提出了一种基于漏斗结构的多视图人脸检测框架 (FuSt)，该框架使用级联分类器和形状索引特征，具有高准确性和低时间成本，能够对多种不同的面部形态进行检测并进行人脸对齐。实验证明，该检测器在两个具有挑战性的数据集上表现出卓越的准确性和速度。