FoxNet: A Multi-face Alignment Method.

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