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。

多视角人脸检测的漏斗结构级联与对齐感知

Funnel-Structured Cascade for Multi-View Face Detection with  Alignment-Awareness

Cascade classifiers are widely used in real-time object detection. Different
from conventional classifiers that are designed for a low overall
classification error rate, a classifier in each node of the cascade is required
to achieve an extremely high detection rate and moderate false positive rate.
Although there are a few reported methods addressing this requirement in the
context of object detection, there is no principled feature selection method
that explicitly takes into account this asymmetric node learning objective. We
provide such an algorithm here. We show that a special case of the biased
minimax probability machine has the same formulation as the linear asymmetric
classifier (LAC) of Wu et al (2005). We then design a new boosting algorithm
that directly optimizes the cost function of LAC. The resulting
totally-corrective boosting algorithm is implemented by the column generation
technique in convex optimization. Experimental results on object detection
verify the effectiveness of the proposed boosting algorithm as a node
classifier in cascade object detection, and show performance better than that
of the current state-of-the-art.

提供了一种基于线性不对称分类器的新型提升算法，可用作级联对象检测中的节点分类器，并显示比当前最先进技术的性能更好。