Face Alignment With Two-stage Localization Model

Face alignment is one of the most classic problems in computer vision,which aims at localizing landmark automatically.Accurately locating facial landmarks is very meaningful for many visual tasks,such as face recognition,3D face reconstruction,facial expression analysis,face pose estimation,etc.However,the input images for many face-related applications are captured in unconstrained environments,and face alignment in such cases is still very challenging due to the large variations in background,illumination,pose and image quality.This paper mainly focuses on the problem of face alignment under unconstrained conditions.The main contributions are as follows:1)This paper presents the experimental results that reasonable initial locations of landmarks can lead to a significant reduction in localization error rate for cascaded regression models.Based on the findings,this paper proposed a coarse-to-precise face alignment algorithm framework,which divides the face alignment into two sub-problems:coarse localization and precise localization,and it's suggested to design dedicated methods for each sub-problem.2)As for the coarse localization problem,we have designed and implemented a model based on deep convolutional neural networks,which take the whole face image as input and predict the coordinates of all facial landmarks.The results on 300-W test set showed that this model can reduce the localization failure rate effectively.3)As for the precise localization problem,we have presented a cascaded regression model,which shares the parameters among all iterations.After concatenating the coarse and precise localization models,we achieved the state-of-the-art result on 300-W test set.Moreover,this paper also points out that the regression model can be interpreted as a gradient prediction model.The experimental results showed that the mean error rate can be further reduced by introducing some techniques in gradient descent method.