The Age-Gated Internet Is Sweeping the US. Half of the country now requires age verification to watch porn or access "harmful" content. Digital rights advocates are pushing back against legislation they say will make the internet less safe. To prove you're an adult, you may have to upload your ID or submit to an age-verifying face scan. Members of Congress considered 19 online safety bills Tuesday that may soon have a major impact on the future of the internet as age-verification laws have spread to half of the US and around the world .

In July, Siyan, a UK-based Discord user, logged on one morning and found himself unable to access some of his text chats marked NSFW. The channel, a popup informed him, was now age-restricted. The United Kingdom had enacted its far reaching child safety laws, which includes an age requirement system to verify users are over 18. Discord's updates required users to verify their age, either by government ID or a face scan. Siyan (who requested to only be referred to by his screen name for privacy reasons) describes himself as "painfully over the age of needing to fake an ID." He didn't want to take a photo of his ID.

After a fairly long wait, Apple's debut mixed reality headset -- its first new device since the Apple Watch -- is almost here. The Vision Pro launches on February 2, and to ensure it fits as well in demos as it will in real life, you'll have to put most of an hour aside to play. According to Bloomberg's Mark Gurman, customers who ask for a demo will have to go through face scans and the assembly of a custom Vision Pro, before sitting through a walkthrough of the interface, controls and device calibration. Apple Store employees will even scan glasses to figure out lens prescriptions for the Vision Pro. All of that could well burn through any intrigue and excitement for the headset, but at least you'll get a meaty 25-minute demo. If you're planning to buy a Vision Pro in-store without trying, you maverick, you'll still have to go through the face scans.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. In December, the US Transportation Security Administration (TSA), an agency within Biden's Department of Homeland Security, acknowledged it has significantly expanded facial recognition technology at security checkpoints in airports across the United States. Under the expanded program, 16 of the nation's largest airports are now using face scans as a way to verify the identities of travelers, including in Atlanta, Boston, Denver, and Los Angeles. The TSA's initial test facial recognition program started under the Trump administration in 2017.

Clearview AI, the surveillance firm notoriously known for harvesting some 20 billion face scans off of public social media searches, said it may bring its technology to schools and other private businesses. In an interview with Reuters on Tuesday, the company revealed it's working with a U.S. company selling visitor management systems to schools. That reveal came around the same time as a horrific shooting at Robb Elementary School in Uvalde, Texas that tragically left 19 children and two teachers dead. Though Clearview wouldn't provide more details about the education-linked companies to Gizmodo, other facial recognition competitors have spent years trying to bring the tech to schools with varying levels of success and pushback. New York state even moved to ban facial recognition in schools two years ago.

The revelations raise new questions about the McLean, Va.-based contractor, which saw its business explode during the pandemic: 10 federal agencies, 30 states and more than 500 companies now pay ID.me to confirm the identities of Americans seeking services such as unemployment insurance or online tax records. The company last year was valued at $1.5 billion, and its government contracts have totaled in the hundreds of millions of dollars.

Its permanent searchable database is accessed by more than 2,400 police agencies including US Immigration and Customs Enforcement (ICE). Clearview AI uses an algorithm to extract unique features in the human face to create a trackable "faceprint." The EU has stringent personal privacy standards, including the GDPR and the Right to Be Forgotten, which are in conflict with Clearview AI's methods. Facial recognition technology has received substantial backlash for its racial bias and inaccuracy, which have resulted in numerous false arrests. At least 14 US cities have banned facial recognition use, and Maine and Massachusetts passed statewide laws banning the tech from law enforcement.

Law enforcement and private businesses have used face recognition for years, but use of the technology in distributing government aid has expanded rapidly during the pandemic. States and federal agencies have turned to face recognition as a contactless, automated way of verifying the identity of people applying for unemployment and other public benefits. Experts and activists worry that failures of this technology could prevent people from getting benefits they desperately need--and that it could be even more dangerous if it works as designed. The pandemic accelerated the use of many biometric data collection tools--temperature checks at doors, thermal cameras in schools, face scans at airports. When it comes to benefits such as unemployment, state governments are turning in particular to facial recognition, to verify people's identity before releasing money they are entitled to.

Deep learning methods have brought many breakthroughs to computer vision, especially in 2D face recognition. However, the bottleneck of deep learning based 3D face recognition is that it is difficult to collect millions of 3D faces, whether for industry or academia. In view of this situation, there are many methods to generate more 3D faces from existing 3D faces through 3D face data augmentation, which are used to train deep 3D face recognition models. However, to the best of our knowledge, there is no method to generate 3D faces from 2D face images for training deep 3D face recognition models. This letter focuses on the role of reconstructed 3D facial surfaces in 3D face identification and proposes a framework of 2D-aided deep 3D face identification. In particular, we propose to reconstruct millions of 3D face scans from a large scale 2D face database (i.e.VGGFace2), using a deep learning based 3D face reconstruction method (i.e.ExpNet). Then, we adopt a two-phase training approach: In the first phase, we use millions of face images to pre-train the deep convolutional neural network (DCNN), and in the second phase, we use normal component images (NCI) of reconstructed 3D face scans to train the DCNN. Extensive experimental results illustrate that the proposed approach can greatly improve the rank-1 score of 3D face identification on the FRGC v2.0, the Bosphorus, and the BU-3DFE 3D face databases, compared to the model trained by 2D face images. Finally, our proposed approach achieves state-of-the-art rank-1 scores on the FRGC v2.0 (97.6%), Bosphorus (98.4%), and BU-3DFE (98.8%) databases. The experimental results show that the reconstructed 3D facial surfaces are useful and our 2D-aided deep 3D face identification framework is meaningful, facing the scarcity of 3D faces.

Google has announced that it's ending a controversial program that targeted homeless black people and scanned their faces to create AI training data in exchange for measly $5 gift cards. After an internal investigation prompted by news reports about the practice, Google says it's no longer sending third-party contractors out to gather face scans, according to New York Daily News. Instead, it will gather faces exclusively within Google campuses. But, bizarrely, a Google spokesperson is still defending the now-cancelled program. The original goal was to acquire face scans of dark-skinned people so that Google could present its facial recognition algorithms with a more diverse set of training data, a longstanding problem within AI that has led to widespread algorithmic bias against black people and other racial minorities.