MTCNN-UGAN: A Self-Attention Enhanced Face Replacement Pipeline for Film and Television Video Frames - Wu, Yumeng; Wang, Xiao (Informatica, 2025)
At present, face replacement technology in film and television videos faces problems such as low accuracy and high resource consumption. Research proposes an automated face replacement technology that integrates improved multi task cascaded convolutional neural networks (MTCNN) and generative adversarial networks (GAN). In the face detection stage, MTCNN is used, and a median filter preprocessing and depthwise separable convolution model are introduced. In the face replacement stage, a U-Net based generative adversarial network (UGAN) is constructed, whose generator consists of an encoder and a decoder, and is embedded with a dual skip connection residual module. The discriminator adopts a self attention mechanism and a video stabilization module. In the experiment, WIDER FACE and Celeb Faces Attributes Dataset (CelebA) were used for face detection tasks. The face replacement task used a high-resolution Celebrity Mask High Quality (CelebAMask HQ) dataset and a Deepfake Model Attribution Dataset (FDM). Meanwhile, the study introduced FaceSwap technology and attribute preserving generative adversarial network (AP-GAN) as comparative baselines. In face detection experiments, the research model performed best in terms of accuracy as well as training loss in different face detection scenes. For example, the accuracy of the research model in complex scenes was 93.25%, and the training loss was 0.221. In the face replacement experiment, the model replaces faces in four image sets. Its color as well as face contour structure was well preserved and face replacement was more natural. In the similarity index comparison, the research model performed the highest face replacement similarity index at different frame numbers with an average value of 0.994. The research model also performed the best in the face replacement imaging peak signal-to-noise ratio test with an average value of 35.65. Finally, in the face replacement composite test, the research model performed the best in both structural similarity and state error. In conclusion, the technique has good application results. This study can provide technical support for the improvement of face replacement technology as well as face characterization.