Deepfake defense not only requires the research of detection but also requires the efforts of generation methods. However, current deepfake methods suffer the effects of obscure workflow and poor performance. To solve this problem, we present DeepFaceLab, the current dominant deepfake framework for face-swapping. It provides the necessary tools as well as an easy-to-use way to conduct high-quality face-swapping. It also offers a flexible and loose coupling structure for people who need to strengthen their pipeline with other features without writing complicated boilerplate code. We detail the principles that drive the implementation of DeepFaceLab and introduce its pipeline, through which every aspect of the pipeline can be modified painlessly by users to achieve their customization purpose. It is noteworthy that DeepFaceLab could achieve cinema-quality results with high fidelity. We demonstrate the advantage of our system by comparing our approach with other face-swapping methods.For more information, please visit:https://github.com/iperov/DeepFaceLab/.

Facial recognition has become one of the most debated technologies of recent times. Tech giants like IBM, Microsoft, Amazon, Google and others have been doing extensive research around it to help enhance numerous consumer applications, enhance security, help organisations go touchless in pandemic and more. While these are large-scale applications, installing facial recognition software into a personal computer can help in various ways such as automated login, use as a biometric for more convenient access, verify personal identity and more. Various organisations are now looking to install facial recognition software into personal computers, and with this article, we take you through nine best facial recognition software for your PC. The custom facial recognition software from Clarifai offers two powerful ways to integrate AI, which are Clarifai API and the portal.

The future of facial recognition technology may depend on one very specific part of the face: the area around the eyes. Before the global pandemic, facial recognition systems typically worked by comparing measurements between different facial features in one image to those in another picture. But when you're wearing a mask over your nose, mouth, and cheeks, you're offering up a fraction of the information normally used to figure out your identity. Now, numerous facial recognition companies say they are focusing on better identifying people based on the portion of the face above the nose and, in particular, the eye region. The stakes are high to get it right, and soon.

The future of facial recognition technology may depend on one very specific part of the face: the area around the eyes. Before the global pandemic, facial recognition systems typically worked by comparing measurements between different facial features in one image to those in another picture. But when you're wearing a mask over your nose, mouth, and cheeks, you're offering up a fraction of the information normally used to figure out your identity. Now, numerous facial recognition companies say they are focusing on better identifying people based on the portion of the face above the nose and, in particular, the eye region. The stakes are high to get it right, and soon.

Facial-recognition algorithms from Los Angeles startup TrueFace are good enough that the US Air Force uses them to speed security checks at base entrances. But CEO Shaun Moore says he's facing a new question: How good is TrueFace's technology when people are wearing face masks? "It's something we don't know yet because it's not been deployed in that environment," Moore says. His engineers are testing their technology on masked faces and are hurriedly gathering images of masked faces to tune their machine-learning algorithms for pandemic times. Facial recognition has become more widespread and accurate in recent years, as an artificial intelligence technology called deep learning made computers much better at interpreting images.

Sign in to report inappropriate content. Shaun Moore is the founder and CEO of Trueface, a facial recognition company working to make computers see like humans. Trueface is involved with many companies and most recently has been helping the US Air Force increase its base security. In the podcast we talk about how facial recognition should be used, the ethics in its application, and the consequences of countries like China using the technology.

Cameras with artificial intelligence (AI) software that the South Korean government claims can detect the likelihood of crime will be installed in Seoul within the year. The Seocho District of South Korea's capital and Electronics and Telecommunications Research Institute (ERTI), a national research institute, said they will install 3,000 cameras at the district by July. The cameras will use AI software that processes the location, time, and behaviour patterns of passersby to measure the likelihood of a crime taking place. The cameras will automatically measure whether somebody is walking normally or tailing someone. It will also detect what passersby are wearing -- such as hats, masks, or glasses -- and what they are carrying with them such as bags or dangerous objects that have a strong possibility of being used to commit a crime.

The Air Force has signed a deal with Trueface, a developer of computer vision systems, to provide facial recognition, license plate recognition and weapon detection for an unnamed air base. This comes after the Air Force hired the company earlier this year to conduct research on how to use facial recognition software on bases. The new pact was built out of the initial research they worked on this year, according to a Medium post from Mason Allen of Trueface. Both the Air Force and Trueface declined to say which base the technology would be used but Shaun Moore, CEO of Trueface, said the company had more plans to work with the government and military institutions on these kinds of projects. "The goal here is to protect the assets and people on base," Moore said in an interview.

Facial recognition technology has finally begun to enter the science fiction landscape we had all dreamt it would soon do. This change in pace is attributed to the significant advances in machine learning, resilient ODM (original design manufacturer) competition and increases in processing power that will forever democratize and commoditize facial recognition technology. When we consider the applications of this core technology, it is essential also to understand the positive consequences of implementing facial recognition for non-conspicuous use cases. To get further insights on this matter, I sat down with Shaun Moore and Nezare Chafni the founders of TrueFace.ai. With such technical advancements in facial recognition has its integration into products become more straightforward?

Facial recognition technology is more prevalent than ever before. It's being used to identify people in airports, put a stop to child sex trafficking, and shame jaywalkers. One major flaw: It sometimes can't tell the difference between a living person's face and a photo of that person held up in front of a scanner. TrueFace.AI facial recognition is trying to fix that flaw. Launched on Product Hunt in June, it's meant to detect "picture attacks."