3-D Facial Landmarks Detection for Intelligent Video Systems

Facial landmark detection is a fundamental research topic in computer vision that is widely adopted in many applications. Recently, thanks to the development of convolutional neural networks, this topic has been largely improved. This article proposes facial-landmark detector, which is based on a state-of-the-art architecture for landmark localization called stacked hourglass network, to obtain accurate facial landmark-points. More specifically, this article uses residual networks as the backbone instead of a 7 x 7 convolution layer. Additionally, it modifies the hourglass modules by using the residual-dense blocks in the mainstream for capturing more efficient features and the 1 x 1 convolution layers in the branch streams for reducing the model size and computational time, instead of the original residual blocks. The proposed architecture also enhances the features from modified hourglass modules with finer-resolution features via a lateral connection to generate more accurate results. The proposed network can outperform other state-of-the-art methods on the AFLW2000-3D dataset and the LS3D-W dataset, the largest three-dimensional (3-D face) alignment dataset to date.

Automated summary

This article introduces a facial-landmark detector based on a stacked hourglass network and residual networks, which improves accuracy by modifying hourglass modules and using 1 x 1 convolution layers in branch streams. The proposed network outperforms other state-of-the-art methods on 3-D face alignment datasets.