Research On 3D Face Reconstruction And Occlusion Ratio

With the development of computer science,videoconferencing has become a common phenomenon.However,faces which are in videos seem to look down or look up in some facilities due to the angle of camera.Thus,correcting the angles of these faces became a new demand.A solution to this problem is that we can construct a 3D model from a single image and then rotate this model and project it to 2D plane.The basis of correcting angles of faces is getting 3D face reconstruction model which is robust and high quality.Meanwhile,we need compute proportion of occluded faces.If this proportion is large,we don not correct the angle of the face.Thus,we are required to do two works.One is training 3D face reconstruction mode from single image using large face pictures.Another is training a model that can output proportion of occluded faces.People are used to utilize the method that can detect 68 key points for 3D construction,It has some drawbacks.For instance,the number of the key points are kind of small and 3D model will not be high-quality.In order to get robust and high-quality 3D face reconstruction,we use the algorithm that can detect 240 facial feature points instead of the classic algorithm that can detect 68 facial feature points.In previous works,the lack of training data can lead to impractical 3D models.In order to solve the problem that the training data is not sufficient and various,we created a larger training data which can cover different scenes,illumination,poses,races.The number of face pictures is about 0.8 million.In early works,people prefer to research the problems like Occlusion key points detection and face segmentation.We are interested in computing the proportion of occluded faces.We trained a model that can show us the proportion of occluded faces.We use the method of supervised training based on the net that trains 3D face reconstruction model.We selected 200k pictures to compute the true proportion of occluded faces.