Published on Mon Apr 22 2019

FoxNet: A Multi-face Alignment Method

Yuxiang Wu, Zehua Cheng, Bin Huang, Yiming Chen, Xinghui Zhu, Weiyang Wang

Multi-face alignment aims to identify geometry structures of multiple faces. Our bottom-up architecture maps the landmarks to the high-dimensional space. By clustering the features belonging to the same face, our approach can align the multi-person facial landmarks synchronously.

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Abstract

Multi-face alignment aims to identify geometry structures of multiple faces in an image, and its performance is essential 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, which can simultaneously localize multi-person facial landmarks with high precision.In more detail, our bottom-up architecture maps the landmarks to the high-dimensional space with which landmarks of all faces are represented. By clustering the features belonging to the same face, our approach can align the multi-person facial landmarks synchronously.Extensive experiments show that our method can achieve high performance in the multi-face 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 with top-down methods. Our dataset is publicly available at https://github.com/AISAResearch/FoxNet