Abstract
Multi-face alignment aims to identify geometry structures of multiple human face in a image, and its performance is important for the many practical tasks, such as face recognition, face tracking and face animation. In this work, we present a fast bottom-up multi-face alignment approach landmark detection approach, which can simultaneously localize multi-person facial landmarks with high precision. In more detail, unlike previous top-down approach, our bottom-up architecture maps the landmarks to the high-dimensional space. Then, the discriminative high-dimensional features are aggregated to represent the landmarks. By clustering the features belonging to the same face, our approach can align the multi-person facial landmarks synchronously. Extensive experiments are conducted in this paper, and the experimental results demonstrate that our method can achieve the high performance in the multiface landmark alignment task while our model is extremely fast. Moreover, we propose a new multi-face dataset to compare the speed and precision of bottom-up face alignment method. Our dataset is publicly available at https://github.com/AISAResearch/FoxNet
Abstract (translated)
多人脸对齐的目的是识别图像中多人脸的几何结构，其性能对于人脸识别、人脸跟踪和人脸动画等许多实际任务都具有重要意义。本文提出了一种快速的自底向上多人脸对准方法-地标检测方法，该方法能同时对多人面部地标进行高精度的定位。更为详细的是，与以前的自顶向下方法不同，我们自下而上的架构将地标映射到高维空间。然后，将识别性的高维特征集合起来表示地标。通过对同一人脸的特征进行聚类，可以实现多人面部标志的同步对齐。本文进行了大量的实验，实验结果表明，该方法能在快速完成多平面地标对准任务的同时，达到较高的性能。此外，我们还提出了一种新的多面数据集来比较自下而上的面对齐方法的速度和精度。我们的数据集在https://github.com/aisaresearch/foxnet上公开
URL
https://arxiv.org/abs/1904.09758
