基于深度卷积神经网络的端对端人脸对齐算法

End-to-end face alignment algorithm based on deep convolution neural network

针对现有基于深度学习的人脸对齐算法难以实现真正意义上“端对端”、浅层特征表征能力及鲁棒性差的问题,提出一种基于深度卷积神经网络的端对端人脸对齐算法。该算法网络含5个模块,使用堆叠卷积层提取人脸特征,通过增加隐含层的宽度达到丰富特征的目的,在前3个模块引入监督约束层提取更有效的人脸特征,采用两次迭代训练过程获取更佳的网络模型,该网络能够学习高维空间特征并预测人脸特征点的坐标位置。实验结果表明,即使在遮挡、复杂姿势、光照等情况下,该算法依然可以取得较优的人脸对齐效果。

For the existing face alignment algorithms based on deep learning,it is difficult to achieve an end-to-end training in the real sense,the shallow network has limited capacity to represent features and is poor in robustness.To solve these problems,an end-to-end face alignment method based on deep convolution neutral network was proposed.The network of algorithm contained five modules and stacked convolutional layer was used to extract features.The width in the hidden layers was increased to achieve rich features.Supervisory signal was added to the first three modules to get more effective features,two iterative procedures were designed to get a better network model.The network can learn global high-level features and directly predict the coordinates of facial landmarks.Extensive experiments demonstrate that the propsed method possesses superior capability of handling occlusions and complex variations of pose,illumination and so on.