• Understanding Fake Faces

    Face recognition research is one of the most active topics in computer vision (CV), and deep neural networks (DNN) are now filling the gap between human-level and computer-driven performance levels in face verification algorithms chemdraw 다운로드. However, although the performance gap appears to be narrowing in terms of accuracy-based expectations, a curious question has arisen; specifically, “Face understanding of AI is really close to that of human?” In the present study, in an effort to confirm the brain-driven concept, we conduct image-based detection, classification, and generation using an in-house created fake face database 박스헤드 버그판 다운로드. This database has two configurations: (i) false positive face detections produced using both the […]

  • RSGAN: Face Swapping and Editing using Face and Hair Representation in Latent Spaces

    In this paper, we present an integrated system for automatically generating and editing face images through face swapping, attribute-based editing, and random face parts synthesis 리눅스 서버에서 파일 다운로드. The proposed system is based on a deep neural network that variationally learns the face and hair regions with large-scale face image datasets. Different from conventional variational methods, the proposed network represents the latent spaces individually for faces and hairs Download Get Gear. We refer to the proposed network as region-separative generative adversarial network (RSGAN). The proposed network independently handles face and hair appearances in the latent spaces, and then, face swapping is achieved by replacing the latent-space representations of the […]