"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.
Consumer privacy has made big headlines in the recent years with the Facebook Cambridge Analytica Scandal, Europe's GDPR and high-profile breaches by companies like Equifax. It's clear that the data of millions of consumers is at risk every day, and that companies that wish to handle their data must do so with the highest degree of protection around both security and privacy of that data, especially for companies that build and sell AI-enabled facial recognition solutions. As CEO of an AI-enabled software company specializing in facial recognition solutions, I've made data security and privacy among my top priorities. Our pro-privacy stance goes beyond mere privacy by design engineering methodology. We regularly provide our customers with education and best practices, and we have even reached out to US lawmakers, lobbying for sensible pro-privacy regulations governing the technology we sell.
The ban comes after civil liberties groups highlighted what they described as faults in facial recognition algorithms after NIST found most facial recognition software was more likely to misidentify people of colour than white people. The Boston ban follows a ban imposed by San Francisco on the use of face recognition technology last year. The ban prevents any city employee using facial recognition or asking a third party to use the technology on its behalf. Boston's police department said it had not used the technology over what it called reliability fears, though it's clear the best systems are reasonably accurate in average working conditions. Critics also opposed the technology on the basis it might discourage citizens' rights to protest.
A good dataset serves as the backbone of an Artificial Intelligence system. Data assists in various ways as it helps understand how the system is performing, understand meaning insights and others. At the premier annual Computer Vision and Pattern Recognition conference (CVPR 2020), several datasets have been open-sourced in order to help the community achieve higher accuracies and insights. Below here we have listed the top 10 Computer Vision datasets that are open-sourced at the CVPR 2020 conference. About: FaceScape is a large-scale detailed 3D face dataset that includes 18,760 textured 3D face models, which are captured from 938 subjects and each with 20 specific expressions.
One of the most important concepts in facial analysis using images, is to define our region of interest (ROI), we must define in our image a specific part where we will filter or perform some operation. For example, if we need to filter the license plate of a car, our ROI is only on the license plate. The street, the body of the car and anything else that is present in the image is just a supporting part in this operation. In our example, we will use the opencv library, which already has supported to partition our image and help us identify our ROI. In our project we will use the ready-made classifier known as: Haar cascade classifier. This specific classifier will always work with gray images.
The definition of an image is very simple: it is a two-dimensional view of a 3D world. Furthermore, a digital image is a numeric representation of a 2D image as a finite set of digital values. We call these values pixels and they collectively represent an image. Basically, a pixel is the smallest unit of a digital image (if we zoom in a picture, we can detect them as miniature rectangles close to each other) that can be displayed on a computer screen. For a more detailed explanation check out our post on how to access and edit pixel values in OpenCV.
In a letter to congress sent on June 8th, IBM's CEO Arvind Krishna made a bold statement regarding the company's policy toward facial recognition. "IBM no longer offers general purpose IBM facial recognition or analysis software," says Krishna. "IBM firmly opposes and will not condone uses of any technology, including facial recognition technology offered by other vendors, for mass surveillance, racial profiling, violations of basic human rights and freedoms, or any purpose which is not consistent with our values and Principles of Trust and Transparency." The company has halted all facial recognition development and disapproves or any technology that could lead to racial profiling. The ethics of face recognition technology have been in question for years. However, there has been little to no movement in the enactment of official laws barring the technology.
Detroit's police chief admitted on Monday that facial recognition technology used by the department misidentifies suspects about 96 percent of the time. It's an eye-opening admission given that the Detroit Police Department is facing criticism for arresting a man based on a bogus match from facial recognition software. Last week, the ACLU filed a complaint with the Detroit Police Department on behalf of Robert Williams, a Black man who was wrongfully arrested for stealing five watches worth $3,800 from a luxury retail store. Investigators first identified Williams by doing a facial recognition search with software from a company called DataWorks Plus. Under police questioning, Williams pointed out that the grainy surveillance footage obtained by police didn't actually look like him.
On Tuesday, a number of AI researchers, ethicists, data scientists, and social scientists released a blog post arguing that academic researchers should stop pursuing research that endeavors to predict the likelihood that an individual will commit a criminal act, as based upon variables like crime statistics and facial scans. The blog post was authored by the Coalition for Critical Technology, who argued that the utilization of such algorithms perpetuates a cycle of prejudice against minorities. Many studies of the efficacy of face recognition and predictive policing algorithms find that the algorithms tend to judge minorities more harshly, which the authors of the blog post argue is due to the inequities in the criminal justice system. The justice system produces biased data, and therefore the algorithms trained on this data propagate those biases, the Coalition for Critical Technology argues. The coalition argues that the very notion of "criminality" is often based on race, and therefore research done on these technologies assumes the neutrality of the algorithms when in truth no such neutrality exists.
Computer vision summit CVPR has just (virtually) taken place, and like other CV-focused conferences, there are quite a few interesting papers. More than I could possibly write up individually, in fact, so I've collected the most promising ones from major companies here. Facebook, Google, Amazon and Microsoft all shared papers at the conference -- and others too, I'm sure -- but I'm sticking to the big hitters for this column. Redmond has the most interesting papers this year, in my opinion, because they cover several nonobvious real-life needs. One is documenting that shoebox we or perhaps our parents filled with old 3x5s and other film photos.
The iPad Pro is the most expensive, and the most capable tablet in the lineup. It boasts a completely different design when compared to the standard iPad or iPad Air. Instead of a Lightning port for charging, syncing and accessories, you'll find a USB-C port. The Home button is gone, replaced by Apple's Face ID facial recognition tech. And, unlike on the iPhone, you can use Face ID with the iPad in either portrait or landscape orientation.