Region-Based Context Enhanced Network for Robust Multiple Face Alignment
Region-Based Context Enhanced Network for Robust Multiple Face Alignment
复制标题
用于鲁棒多脸对齐的基于区域的上下文增强网络
DOI：
10.1109/tmm.2019.2916455
复制
发表时间：
2019-12-01
影响因子：
7.3
通讯作者：
Li, Stan Z.
中科院分区：
文献类型：
--
作者：
Lin, Xuxin;Liang, Yanyan;Li, Stan Z.
最近的人脸对齐研究涉及开发一个孤立的算法，以及裁剪的人脸图像。在实际应用中，使用现成的人脸检测器很难获得预期的输入。在本文中，我们试图通过建立一个新的联合多任务模型，它允许我们同时检测多个人脸和他们的地标在一个给定的场景图像之间的桥梁人脸检测和人脸对齐。与基于流水线的框架相比，通过级联单独的模型，我们的目标是通过在特定任务的模块之间共享和转换特征表示来提出一个端到端的卷积网络。为了学习用于无约束人脸对齐的鲁棒地标估计器，设计了三种类型的上下文增强块来编码具有多级上下文、多尺度上下文和全局上下文的特征图。在后处理步骤中，我们开发了一种基于点分布模型的形状重建算法来细化界标离群点。大量的实验表明，我们的结果是鲁棒的地标定位任务和估计的人脸区域的位置不敏感。此外，我们的方法在几个具有挑战性的数据集（包括300 W，AFLW和COFW）上的性能明显优于最近的最先进方法。
The recent studies for face alignment have involved developing an isolated algorithm on well-cropped face images. It is difficult to obtain the expected input by using an off-the-shelf face detector in practical applications. In this paper, we attempt to bridge between face detection and face alignment by establishing a novel joint multi-task model, which allows us to simultaneously detect multiple faces and their landmarks on a given scene image. In contrast to the pipeline-based framework by cascading separate models, we aim to propose an end-to-end convolutional network by sharing and transform feature representations between the task-specific modules. To learn a robust landmark estimator for unconstrained face alignment, three types of context enhanced blocks are designed to encode feature maps with multi-level context, multi-scale context, and global context. In the post-processing step, we develop a shape reconstruction algorithm based on point distribution model to refine the landmark outliers. Extensive experiments demonstrate that our results are robust for the landmark location task and insensitive to the location of estimated face regions. Furthermore, our method significantly outperforms recent state-of-the-art methods on several challenging datasets including 300 W, AFLW, and COFW.