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

In this paper we consider the problem of multi-view face detection. While
there has been significant research on this problem, current state-of-the-art
approaches for this task require annotation of facial landmarks, e.g. TSM [25],
or annotation of face poses [28, 22]. They also require training dozens of
models to fully capture faces in all orientations, e.g. 22 models in HeadHunter
method [22]. In this paper we propose Deep Dense Face Detector (DDFD), a method
that does not require pose/landmark annotation and is able to detect faces in a
wide range of orientations using a single model based on deep convolutional
neural networks. The proposed method has minimal complexity; unlike other
recent deep learning object detection methods [9], it does not require
additional components such as segmentation, bounding-box regression, or SVM
classifiers. Furthermore, we analyzed scores of the proposed face detector for
faces in different orientations and found that 1) the proposed method is able
to detect faces from different angles and can handle occlusion to some extent,
2) there seems to be a correlation between dis- tribution of positive examples
in the training set and scores of the proposed face detector. The latter
suggests that the proposed methods performance can be further improved by using
better sampling strategies and more sophisticated data augmentation techniques.
Evaluations on popular face detection benchmark datasets show that our
single-model face detector algorithm has similar or better performance compared
to the previous methods, which are more complex and require annotations of
either different poses or facial landmarks.

本文提出了一种基于卷积神经网络的 Deep Dense Face Detector (DDFD) 方法，不仅不需要姿态或者地标注释，而且能够探测多个视角下人脸的位置，并且表现出与其他方法相当或优于其他更为复杂的方法。