The accurate localization of facial landmarks is at the core of several face analysis tasks, such as face recognition and facial expression analysis, to name a few. In this work, we propose a novel localization approach based on a deep learning architecture that utilizes two paired cascaded subnetworks with convolutional neural network units. The cascaded units of the first subnetwork estimate heatmap-based encodings of the landmarks’ locations, while the cascaded units of the second subnetwork receive as inputs the outputs of the corresponding heatmap estimation units, and refine them through regression. The proposed scheme is experimentally shown to compare favorably with contemporary state-of-the-art schemes, especially when applied to images depicting challenging localization conditions.

面部标志物的精确定位是一些面部分析任务的核心，例如面部识别和面部表情分析等。在这项工作中，我们提出了一种基于深度学习架构的新颖定位方法，该方法利用两个成对的级联子网和卷积神经网络单元。第一个子网的级联单元估计地标位置的基于热图的编码，而第二个子网的级联单元接收相应的热图估计单元的输出作为输入，并通过回归进行优化。实验证明了该方案可与当代最新方案相媲美，尤其是应用于描述具有挑战性的本地化条件的图像时。