Abstract
Facial landmarks are highly correlated with each other since a certain landmark can be estimated by its neighboring landmarks. Most of the existing deep learning methods only use one fully-connected layer called shape prediction layer to estimate the location of facial landmarks. In this paper, we propose a novel deep learning framework named Multi-Center Learning with multiple shape prediction layers for face alignment. In particular, each shape prediction layer emphasizes on the detection of a certain cluster of semantically relevant landmarks respectively. Challenging landmarks are focused firstly, and each cluster of landmarks is further optimized respectively. Moreover, to reduce the model complexity, we propose a model assembling method to integrate multiple shape prediction layers into one shape prediction layer. Extensive experiments demonstrate that our method is effective for handling complex occlusions and appearance variations with real-time performance. The code for our method is available at https://github.com/ZhiwenShao/MCNet-Extension.
Abstract (translated)
面部地标彼此高度相关，因为某个地标可以通过其相邻地标来估计。大多数现有的深度学习方法仅使用一个称为形状预测层的全连接层来估计面部标志的位置。在本文中，我们提出了一种新的深度学习框架，名为多中心学习，具有多个形状预测层，用于面部对齐。特别地，每个形状预测层分别强调对某一语义相关地标群的检测。首先关注具有挑战性的地标，并且分别进一步优化每个地标群。此外，为了降低模型复杂度，我们提出了一种模型组装方法，将多个形状预测层集成到一个形状预测层中。大量实验表明，我们的方法可以有效地处理复杂的遮挡和外观变化，并具有实时性能。我们的方法代码可以在https://github.com/ZhiwenShao/MCNet-Extension上找到。
URL
https://arxiv.org/abs/1808.01558
