A new series of patents by Facebook's parent company Meta suggest possible plans from the company to capture users' behavioral biometrics data. Specifically, the patents mention pupil movements, body poses, and crumpled noses, which the company would use to make digital avatars realistically animated. The patents were reviewed by The Financial Times, according to which Meta also intends to use the biometric data to provide hyper-targeted advertising and sponsored content. In fact, one of the patents analyzed by the publication was granted to Meta by the United States Patent and Trademark Office (USPTO) earlier this month and refers to the tracking of users' facial expressions through a virtual reality headset to "adapt media content" based on those responses. A separate patent describes an avatar personalization engine capable of creating a 3D avatar of a user based on biometrics collected from a submitted photo.

The CEO of ID.me, a service used by dozens of states to verify unemployment benefits claimants as well as several federal agencies, has walked back previous claims that the company does not use a more powerful method of facial recognition. To learn more about the example of Eric Jaklitsch of New Jersey referenced in the statement below, visit: https://t.co/OLQX1gAhYL "ID.me uses a specific '1 to Many' check on selfies tied to government programs targeted by organized crime to prevent prolific identity thieves and members of organized crime from stealing the identities of innocent victims en masse," Blake Hall said in a statement. "This step is internal to ID.me and does not involve any external or government database." That contrasts with comments Hall made earlier this week.

On Monday, ID.me released a statement from CEO and founder Blake Hall about what the vendor said is its commitment to federal guidelines for facial recognition technology. Hall said the vendor uses one-to-one face match technology and not one-to-many facial recognition. One-to-one face match is a simple application of the technology that is comparable to using one's face to unlock a smartphone or be verified at an airport, Hall said in an interview with TechTarget. "It's something that Americans do broadly all across the country when they're proving their identity in person," Hall said. "What it specifically is not is like taking one person's photo and then taking like a city's worth of images and trying to like match that person's face."

But first, let s address the question, What is computer vision? In simple terms, computer vision trains the computer to visualize the world just like we humans do. Computer vision techniques are developed to enable computers to see and draw analysis from digital images or streaming videos. The main goal of computer vision problems is to use the analysis from the digital source data to convert it into something about the world. Computer vision uses specialized methods and general recognition algorithms, making it the subfield of artificial intelligence and machine learning.

Image and face recognition platforms and solutions have been a major focus in the technology sector over the past two decades. Images and face recognition technology are used in many industries, including healthcare, security, e-commerce and security. This has resulted in remarkable progress. Experts believe this technology can perform at or even surpass human-level in many medical diagnoses and security domains. Many brands now use image recognition technology to harness the intersection of visual analytics and text to understand the industry and target audience, and to deploy visual intelligence to drive meaningful communications.

Technology, wielded right, is targeted at bettering the lives and status of the people. No matter how good a piece of tech is, though, there will always be some poor sides to it. That is why we have regulatory agencies for these kinds of issues. The pieces of tech that make it through are those that are deemed to have many more benefits than the demerits which could be lurking around. Today, we have such controversies regarding facial recognition. With major players like Amazon withdrawing its commercial software from the market for a while, it becomes important to see where the argument could sway for this piece of tech in the future.

From angry handbags to washing machines in distress, humans see faces in all sorts of inanimate objects – a peculiar phenomenon known as'face pareidolia'. Now, researchers in Maryland have found that these faces are more likely to perceived as young and male than old and female. The academics tested nearly 4,000 volunteers with photos to stimulate pareidolia, including images of an'alarmed' teapot, a'relaxed' potato and a'disgusted' green apple on a branch. Participants perceived illusory faces as having a specific emotional expression, age and gender, but they were mostly perceived as young and male by both men and women. Researchers weren't sure why this was, although it's possible humans are more prone to seeing men because we were more exposed to male faces during our earliest stages of development.

With an abundance of password managers, browsers, and mobile operating systems all making it easy, and more apps adopting fingerprint or face recognition support, logging into our numerous accounts is more straightforward and seamless than ever. It's important not to get complacent though--whether it's through moving to new devices or because of shady activity that hasn't been authorized, plenty of users still find themselves locked out of their accounts on a regular basis. If that should happen, you're going to have to fall back on the various recovery processes implemented by these accounts, which normally involve a backup email address. Keeping this email address up to date and secure is vital--not just in case you need to gain access to a locked account, but also to guard against other people trying to reset your login credentials. If this backup email isn't valid, or has been compromised, you're opening yourself up to numerous potential problems.

The facial expressions in the acquired 3D images were distinguished through machine learning with an average of 85% accuracy – a statistically …

Even after more than a hundred years after its introduction, histology remains the gold standard in tumor diagnosis and prognosis. Anatomic pathologists evaluate histology to stratify cancer patients into different groups depending on their tumor genotypes and phenotypes, and their clinical outcome [1,2]. However, human evaluation of histological slides is subjective and not repeatable [3]. Furthermore, histological assessment is a time-consuming process that requires highly trained professionals. With significant technological advances in the last decade, techniques such as whole slide imaging (WSI) and deep learning (DL) are now widely available.