"What exactly is computer vision then? Computer vision is a research field working to equip computers with the ability to process and understand visual data, as sighted humans can. Human brains process the gigabytes of data passing through our eyes every second and translate that data into sight - that is, into discrete objects and entities we can recognise or understand. Similarly, computer vision aims to give computers the ability to understand what they are seeing, and act intelligently on that knowledge."
– Computer vision: Cheat Sheet. ZDNet.com (December 6, 2011), by Natasha Lomas.
London-headquartered BeMyEye has made another acquisition, its third in a little over three years. This time the retail execution monitoring service is purchasing Russian crowdsourcing and image recognition provider Streetbee. The acquisition will see BeMyEye launch "Perfect Shelf," which will use image recognition technology to lower the cost for consumer goods companies wanting to get "objective and actionable" in-store insights. These will typically include share of shelf and planogram compliance (the specific placement of products on a store shelf). More broadly, BeMyEye offers a platform to enable companies and brands to crowdsource various in-store data.
On Tuesday, a group of 90 advocacy groups penned a letter to Amazon, Google, and Microsoft, requesting that the companies pledge not to sell facial recognition technology to the government. The American Civil Liberties Union (ACLU), the Refugee and Immigrant Center for Education and Legal Services (RAICES), and the Electronic Frontier Foundation (EFF) were among the groups that pressed these companies. The letter marks mounting pressure on some of Silicon Valley's most influential companies and their ramping efforts to build facial recognition systems. "We are at a crossroads with face surveillance, and the choices made by these companies now will determine whether the next generation will have to fear being tracked by the government for attending a protest, going to their place of worship, or simply living their lives," Nicole Ozer, technology and civil liberties director for the ACLU of California, said. Recently, Google and Microsoft have acknowledged the risks involving facial recognition services and their potential for misuse and surveillance by bad actors.
A facial recognition scan could become part of a standard medical checkup in the not-too-distant future. Researchers have shown how algorithms can help identify facial characteristics linked to genetic disorders, potentially speeding up clinical diagnoses. In a study published this month in the journal Nature Medicine, US company FDNA published new tests of their software, DeepGestalt. Just like regular facial recognition software, the company trained their algorithms by analyzing a dataset of faces. FDNA collected more than 17,000 images covering 200 different syndromes using a smartphone app it developed named Face2Gene.
Human beings have 17 different facial expressions that tell those around us we are feeling happy. Experts have discovered that the human face is capable of contorting itself into more happy faces than any other emotion. Only three facial guises successfully convey fear, four show surprise, and five display sadness and anger. Experts have discovered that the human face is capable of contorting itself into more happy faces than any other emotion. Researchers at the Ohio State University compiled a list of 821 words that expressed emotions and had these translated into a number of languages including Spanish, Mandarin Chinese, Farsi and Russian.
We are going to use this existing model and build our own on top of it. This approach brings with it numerous advantages. For instance, it will save us a lot of time, some of the parameters that the Inception has already learned can be reused and we can still build a pretty accurate classifier with far less training data. This process of reusing pre-trained models on different but related tasks is known as Transfer Learning in the world of Deep Learning. First step is to download the training images for your classifier.
A judge in California ruled Thursday that U.S. authorities cannot force people to unlock technology with fingerprint or facial recognition, even with a search warrant. A judge in California ruled Thursday that U.S. authorities cannot force people to unlock technology via fingerprint or facial recognition, even with a search warrant. Magistrate Judge Kandis Westmore, of the U.S. District Court for the Northern District of California, made the ruling as investigators tried to access someone's property in Oakland. Two people allegedly used Facebook messenger to threaten a victim with the release of an "embarrassing video" if they didn't hand over money. Authorities investigating the case requested a search and seizure warrant "to seize various items" believed to be at a home connected to the suspects.
Consider looking at a photograph of a chair. We humans have the remarkable capacity of inferring properties about the 3D shape of the chair from this single photograph even if we might not have seen such a chair ever before. A more representative example of our experience though is being in the same physical space as the chair and accumulating information from various viewpoints around it to build up our hypothesis of the chair's 3D shape. How do we solve this complex 2D to 3D inference task? What kind of cues do we use?
A team of Portuguese researchers have developed a way to identify and track individual animals with artificial intelligence but without facial recognition, which could eventually be applied to public surveillance of humans, Defense One reports. The researchers used a convolutional neural network (CNN) to create idtracker.ai, CNNs are commonly used in facial biometrics, and NIST recently singled them out as the advance most responsible for the dramatic improvement of the technology's accuracy over the past five years. According to the researchers, idtracker.ai is "species agnostic," so will work with people or any other kind of animal. Microsoft called for government regulation of facial recognition in July of last year, saying it raises issues about privacy and other fundamental human rights.
I have written many times before about how AI is changing the landscape of marketing. It gives marketers the opportunity to reach more people while delivering personalized, relevant and timely content to them. Particularly interesting, is the use of AI in the retail industry. Many people fear that e-commerce giants threaten the existence of local retailers, but, brick-and-mortar stores aren't dying, they're simply evolving. The use of technology is enhancing customer connectivity and experience at every touchpoint, both online and in-store.
While there have been a few massive surveillance startups in China that have raised funds on the back of computer vision advances, there's seemed to be less fervor outside of that market. Tel Aviv-based AnyVision is aiming to leverage its computer vision chops in tracking people and objects to create some pretty clear utility for the enterprise world. After announcing a $27 million Series A in mid-2018, the computer vision startup is bringing Lightspeed Venture Partners into the raise, closing out the round at $43 million. "When you have a company with the technology AnyVision has, and the market need that I'm hearing from across industries, what you need to do is push the gas pedal and build an organization which can monetize and take on this opportunity to grow massively," Lightspeed partner Raviraj Jain told TechCrunch. Right now the 200-person company has its eyes on the security and identity markets as it aims to bring its computer vision technology into more industry-tailored solutions.