论文笔记1.1——Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks

  使用多任务级联的卷积神经网络将人脸检测与对准结合起来

  作者：张凯鹏，张展鹏，李志峰，IEEE高级会员，余乔，IEEE高级会员

Abstract—Face detection and alignment in unconstrained en-
vironment are challenging due to various poses, illuminations and
occlusions. Recent studies show that deep learning approaches
can achieve impressive performance on these two tasks. In this
paper, we propose a deep cascaded multi-task framework which
exploits the inherent correlation between them to boost up their
performance. In particular, our framework adopts a cascaded
structure with three stages of carefully designed deep convolu-
tional networks that predict face and landmark location in a
coarse-to-fine manner. In addition, in the learning process, we
propose a new online hard sample mining strategy that can im-
prove the performance automatically without manual sample
selection. Our method achieves superior accuracy over the
state-of-the-art techniques on the challenging FDDB and WIDER
FACE benchmark for face detection, and AFLW benchmark for
face alignment, while keeps real time performance.

摘要–(人脸校准（alignment）是给你一张脸，你给我找出我需要的特征点的位置，比如鼻子左侧，鼻孔下侧，瞳孔位置，上嘴唇下侧等等点的位置。如果觉得还是不明白，看下图：)
图中红色框框就是在做detection，白色点点就是在做alignment。

由于多种多样的姿势，光照，遮挡的问题，在非限制场景下的人脸检测与校准是非常有挑战性的。

目前绝大多数的人脸识别数据集都是非限制场景下的，例如LFW。限制场景就是指基于某一特定环境下，比如一个证件照的数据集就是限制场景下，因为都是在同样的场景（差不多的背景，差不多的光照）下采集的。非限制场景则与之相反，例如LFW（Labeled Faces in the Wild）中的wild指的就是不限制某一特定场景下。
：弓長知行
链接：https://www.jianshu.com/p/506b7ef10b40
来源：简书
最近研究表明，深度学习的方法在人脸检测与校准这两个任务上的表现很好。在这篇论文中，我们提出了