单眼图像的3D人脸对齐是识别伪装人脸的关键过程。通过对齐促进的3D人脸重建可以恢复人脸结构，有助于减轻伪装干扰。本文提出了一种双重注意机制和有效的端到端端3D人脸对齐框架。我们通过深度可分离卷积，密集连接卷积和轻量级通道注意机制构建稳定的网络模型。为了增强网络模型提取人脸区域空间特征的能力，我们采用空间分组智能特征增强模块来提高网络的表示能力。..

Dual Attention MobDenseNet(DAMDNet) for Robust 3D Face Alignment

3D face alignment of monocular images is a crucial process in the recognition of faces with disguise.3D face reconstruction facilitated by alignment can restore the face structure which is helpful in detcting disguise interference.This paper proposes a dual attention mechanism and an efficient end-to-end 3D face alignment framework.We build a stable network model through Depthwise Separable Convolution, Densely Connected Convolutional and Lightweight Channel Attention Mechanism. In order to enhance the ability of the network model to extract the spatial features of the face region, we adopt Spatial Group-wise Feature enhancement module to improve the representation ability of the network.Different loss functions are applied jointly to constrain the 3D parameters of a 3D Morphable Model (3DMM) and its 3D vertices. We use a variety of data enhancement methods and generate large virtual pose face data sets to solve the data imbalance problem. The experiments on the challenging AFLW,AFLW2000-3D datasets show that our algorithm significantly improves the accuracy of 3D face alignment. Our experiments using the field DFW dataset show that DAMDNet exhibits excellent performance in the 3D alignment and reconstruction of challenging disguised faces.The model parameters and the complexity of the proposed method are also reduced significantly.The code is publicly available at https:// github.com/LeiJiangJNU/DAMDNet