Summary
Congratulations! We have now finished all the coding in this book. We have learned how to use dlib to detect faces and facial landmarks and how to use OpenCV to warp and align a face. We also learned how to use warping and masking to do face swapping. As a matter of fact, we spent most of the chapter learning about face image processing and spent very little time on the deep learning side. We have implemented autoencoders by reusing and modifying the autoencoder code from the previous chapter.
Finally, we went over an example of improving deepfake by using GANs. faceswap-GAN improves deepfake by adding a residual block, a self-attention block, and a discriminator for adversarial training, all of which we have already learned about in previous chapters.
In the next chapter, which is also the final chapter, we will review the techniques we have learned in this book and look at some of the pitfalls in training GANs for real-world applications. Then, we will go over a few...