Face++ paper in ICCV2013

Extensive Facial Landmark Localization with Coarse-to-fine Convolutional Neural Network
Erjin Zhou, Haoqiang Fan, Zhimin Cao, Yuning Jiang and Qi Yin
ICCV workshop on 300 Faces in-the-Wild Challenge,2013. [PDF] [SLIDES]

We present a new approach to localize extensive facial landmarks with a coarse-to-fine convolutional network cascade. Deep convolutional neural networks (DCNN) have been successfully utilized in facial landmark localization for two-fold advantages: 1) geometric constraints among facial points are implicitly utilized; 2) huge amount of training data can be leveraged. However, in the task of extensive facial landmark localization, a large number of facial landmarks (more than 50 points) are required to be located in a unified system, which poses great difficulty in the structure design and training process of traditional convolutional networks.