Indian Angel Network (IAN), Indian angel investor network, announced undisclosed investment in Gurgaon-based Staqu, an Artificial Intelligence (AI) focused research startup working in automated image understanding technology. The funding will be used to further build and democratize technology and strengthen the team. Staqu, founded in 2015, comprises of researchers and engineers as a part of its core team. Atul Rai, co-founder and CEO said, "We plan to invest this round to expand the computational strength of our VGrep Lab (AI research lab at Staqu) and fuel it with clusters of GPUs and other technical resources. Currently, we are applying our research to solve pressing problems in the e-commerce sector.

Back in March, Facebook acquired the quirky face-tracking app MSQRD, allowing users to try on masks and swap faces with friends and even celebrities. Now, you'll be able to do this in a live broadcast. Announced at VidCon on Thursday, Facebook users will soon be able to go live directly through the MSQRD app. Back in March, Facebook acquired the quirky face-tracking app MSQRD, allowing users to try on masks and swap faces with friends and even celebrities. Now, you'll be able to do this in a live broadcast Free app called Masquerade has dozens of live filters, which can be used for videos and still photos.

We're all a bit worried about the terrifying surveillance state that becomes possible when you cross omnipresent cameras with reliable facial recognition -- but a new study suggests that some of the best algorithms are far from infallible when it comes to sorting through a million or more faces. The University of Washington's MegaFace Challenge is an open competition among public facial recognition algorithms that's been running since late last year. The idea is to see how systems that outperform humans on sets of thousands of images do when the database size is increased by an order of magnitude or two. "We're the first to suggest that face recs algorithms should be tested at'planet-scale,'" wrote the study's lead author, Ira Kemelmacher-Shlizerman, in an email to TechCrunch. "I think that many will agree it's important.

The FBI has access to nearly 412 million facial photos in its facial recognition system--perhaps including the one on your driver's license. But according to a new government watchdog report, the bureau doesn't know how error-prone the system is, or whether it enhances or hinders investigations. Since 2011, the bureau has quietly been using this system to compare new images, such as those taken from surveillance cameras, against a large set of photos to look for a match. That set of existing images is not limited to the FBI's own database, which includes some 30 million photos. The bureau also has access to face recognition systems used by law enforcement agencies in 16 different states, and it can tap into databases from the Department of State and the Department of Defense.

Like many autistic children, Julian Brown has trouble reading emotions in people's faces, one of the biggest challenges for people with the neurological disorder. Now the 10-year-old San Jose boy is getting help from'autism glass' -- an experimental device that records and studies faces in real-time and alerts him to the emotions they're expressing. The facial recognition software was developed at Stanford University and runs on Google Glass, a computerised headset with a front-facing camera and a tiny display just above the right eye. Julian Brown has trouble reading emotions in people's faces, one of the biggest challenges for people with the neurological disorder. Now the 10-year-old San Jose boy is getting help from'autism glass' Autism glass records and studies faces in real-time.

In this paper, we tackle the question of discovering an effective set of spatial filters to solve hyperspectral classification problems. Instead of fixing a priori the filters and their parameters using expert knowledge, we let the model find them within random draws in the (possibly infinite) space of possible filters. We define an active set feature learner that includes in the model only features that improve the classifier. To this end, we consider a fast and linear classifier, multiclass logistic classification, and show that with a good representation (the filters discovered), such a simple classifier can reach at least state of the art performances. We apply the proposed active set learner in four hyperspectral image classification problems, including agricultural and urban classification at different resolutions, as well as multimodal data. We also propose a hierarchical setting, which allows to generate more complex banks of features that can better describe the nonlinearities present in the data.

IBM is using the power of its Watson supercomputer, "cognitive imaging" and artificial intelligence to help doctors better diagnose patients facing major diseases. The tech giant on Wednesday announced its Watson Health medical-imaging collaborative, which includes 16 members from health systems, academic medical centers and imaging-tech vendors. IBM said participants could train its Watson for early detection of ailments like breast cancer, heart disease, diabetes, eye problems and other overlooked health conditions. Cognitive computing and imaging lets health care providers draw insights from massive volumes of sharable data, such as electronic health records, lab results and other reports. "Our collaboration allows us to help shape the future of medicine by joining efforts to create the tools which will be vital for physicians to make correct decisions based on evidence and complex sources of clinical data," said Dr. Jack Ziffer, chief medical officer for Baptist Health South Florida.

After several months of beefing up the Watson Health Unit, IBM on Wednesday announced it has recruited 16 other entities involved in the health care sector to from a new Watson Health medical imaging collaborative. The global collaborative aims to advance cognitive imaging in a range of medical specialties, from eye care to the treatment of heart and brain disease. The group plans to use Watson to analyze previously "invisible" unstructured imaging data, found in places such as radiology and pathology reports, as well as broad swaths of data collected from sources like population-based disease registries. "There is strong potential for systems like Watson to help to make radiologists more productive, diagnoses more accurate, decisions more sound, and costs more manageable," Nadim Michel Daher, a medical imaging and informatics analyst for Frost & Sullivan, said in a statement. "This is the type of collaborative initiative needed to produce the real-world evidence and examples to advance the field of medical imaging and address patient care needs across large and growing disease states."

As the urgency to save the world's coral reefs increases, scientists using facial recognition technology and 360 degree underwater photos have announced the development of new software that allows them to pull comprehensive scientific data from their vast archive of digital images.

Did you know that OpenCV can detect cat faces in images…right out-of-the-box with no extras? But after Kendrick Tan broke the story, I had to check it out for myself…and do a little investigative work to see how this cat detector seemed to sneak its way into the OpenCV repository without me noticing (much like a cat sliding into an empty cereal box, just waiting to be discovered). In the remainder of this blog post, I'll demonstrate how to use OpenCV's cat detector to detect cat faces in images. This same technique can be applied to video streams as well. If you take a look at the OpenCV repository, specifically within the haarcascades directory (where OpenCV stores all its pre-trained Haar classifiers to detect various objects, body parts, etc.), you'll notice two files: Both of these Haar cascades can be used detecting "cat faces" in images.