Face Swapping for Film and Television Video based on FaceNet and Local Translation Warp

Face swapping, a study of editing the face identity attributes, has received more attention in recent years due to the breakthrough progress of deep learning. Due to its application and technical value, this paper attempts to swap faces in a selected video clip. To begin with, we use the deep learning framework facenet pytorch to build a MTCNN model for face detection, next obtain the face feature vector based on FaceNet, and compare the Euclidean distance for target face recognition. Then use the InsightFace library to locate the 106 facial landmarks of the target person. Lastly, select the appropriate facial landmarks on the two-dimensional image to realize the pixel-based local translation operation, so that the face contour changes.