一种端到端的人脸对齐方法

An End-to-End Face Alignment Method

现有的人脸对齐方法多数是非端到端的,中间过程需要大量的人工干预,导致人脸关键点检测的稳定性较差。为此,提出一种端到端的基于深度学习的人脸对齐方法。基于MobileNets系列网络的子模块,使用类VGG结构的方式进行搭建,将整张图片作为输入,采用基于深度可分离卷积模块进行特征提取,并运用改进的倒残差结构避免网络训练过程的梯度消失,减少特征损失。在此基础上将眼间距离作为正规化方法,在300W人脸数据集上进行测试,结果表明,与CDM、DRMF等方法相比,该方法在保证较优精度的同时,具有良好的实时性。

Most of the existing face alignment methods are not end-to-end,and require frequent manual intervention,which leads to a reduction in their stability.To address the problem,an end-to-end face alignment method based on deep learning is proposed.The network required by this method is constructed based on the sub-modules of the MobileNet series in a structure similar to VGG.Taking the entire image as the input,the depth-wise separable convolution module is used for feature extraction,and the method employs an improved inverted residual structure to avoid the disappearance of gradients in the network training process while reducing the loss of features.The distance between eyes is taken as the basis for normalization.The designed network is tested on the 300W face dataset and compared with CDM,DRMF methods.The experimental results show that the proposed algorithm displays excellent accuracy and real-time performance.