Faces play a magnificent role in human robot interaction, as they do in our daily life. The inherent ability of the human mind facilitates us to recognize a person by exploiting various challenges such as bad illumination, occlusions, pose variation etc. which are involved in face recognition. But it is a very complex task in nature to identify a human face by humanoid robots. The recent literatures on face biometric recognition are extremely rich in its application on structured environment for solving human identification problem. But the application of face biometric on mobile robotics is limited for its inability to produce accurate identification in uneven circumstances. The existing face recognition problem has been tackled with our proposed component based fragmented face recognition framework. The proposed framework uses only a subset of the full face such as eyes, nose and mouth to recognize a person. It's less searching cost, encouraging accuracy and ability to handle various challenges of face recognition offers its applicability on humanoid robots. The second problem in face recognition is the face spoofing, in which a face recognition system is not able to distinguish between a person and an imposter (photo/video of the genuine user). The problem will become more detrimental when robots are used as an authenticator. A depth analysis method has been investigated in our research work to test the liveness of imposters to discriminate them from the legitimate users. The implication of the previous earned techniques has been used with respect to criminal identification with NAO robot. An eyewitness can interact with NAO through a user interface. NAO asks several questions about the suspect, such as age, height, her/his facial shape and size etc., and then making a guess about her/his face.

Brad Smith, Microsoft's president and chief legal officer, dialed up the urgency on Thursday, arguing that delays to enacting new rules could "exacerbate societal issues." Society is ill-served "by a commercial race to the bottom, with tech companies forced to choose between social responsibility and market success," he wrote in a blog post. Mr. Smith also was scheduled to speak about Microsoft's position Thursday at the Brookings Institution in Washington, D.C., the same day a group of tech leaders from Microsoft and other companies visited the White House for a summit on issues including artificial intelligence. Microsoft's advocacy of regulation underlines the ambivalence over powerful new technologies enabled by advances in AI. Adoption of facial recognition is proceeding quickly--especially in China, where the government uses it extensively for surveillance--stirring concerns about potential misuse.

In July, we shared our views about the need for government regulation and responsible industry measures to address advancing facial recognition technology. As we discussed, this technology brings important and even exciting societal benefits but also the potential for abuse. We noted the need for broader study and discussion of these issues. In the ensuing months, we've been pursuing these issues further, talking with technologists, companies, civil society groups, academics and public officials around the world. We've learned more and tested new ideas. Based on this work, we believe it's important to move beyond study and discussion.

Artificial intelligence has made major strides in the past few years, but those rapid advances are now raising some big ethical conundrums. Chief among them is the way machine learning can identify people's faces in photos and video footage with great accuracy. This might let you unlock your phone with a smile, but it also means that governments and big corporations have been given a powerful new surveillance tool. A new report from the AI Now Institute (large PDF), an influential research institute based in New York, has just identified facial recognition as a key challenge for society and policymakers. The speed at which facial recognition has grown comes down to the rapid development of a type of machine learning known as deep learning.

WASHINGTON – Microsoft said Thursday it was adopting a set of principles for deployment of facial recognition technology, calling on industry rivals to follow suit and for new laws to avert a dystopian future. Microsoft President Brad Smith made the announcement at a Brookings Institution speech and an accompanying blog post, saying it was urgent to begin placing limits on facial recognition to avoid the surveillance state described in George Orwell's "1984." "We must ensure that the year 2024 doesn't look like a page from the novel '1984,' " Smith said. "An indispensable democratic principle has always been the tenet that no government is above the law. Today this requires that we ensure that governmental use of facial recognition technology remain subject to the rule of law. New legislation can put us on this path."

After a hellish year of tech scandals, even government-averse executives have started professing their openness to legislation. But Microsoft president Brad Smith took it one step further on Thursday, asking governments to regulate the use of facial-recognition technology to ensure it does not invade personal privacy or become a tool for discrimination or surveillance. Tech companies are often forced to choose between social responsibility and profits, but the consequences of facial recognition are too dire for business as usual, Smith said. "We believe that the only way to protect against this race to the bottom is to build a floor of responsibility that supports healthy market competition," he said in a speech at the Brookings Institution. "We must ensure that the year 2024 doesn't look like a page from the novel 1984."

The study of fairness in intelligent decision systems has mostly ignored long-term influence on the underlying population. Yet fairness considerations (e.g. affirmative action) have often the implicit goal of achieving balance among groups within the population. The most basic notion of balance is eventual equality between the qualifications of the groups. How can we incorporate influence dynamics in decision making? How well do dynamics-oblivious fairness policies fare in terms of reaching equality? In this paper, we propose a simple yet revealing model that encompasses (1) a selection process where an institution chooses from multiple groups according to their qualifications so as to maximize an institutional utility and (2) dynamics that govern the evolution of the groups' qualifications according to the imposed policies. We focus on demographic parity as the formalism of affirmative action. We then give conditions under which an unconstrained policy reaches equality on its own. In this case, surprisingly, imposing demographic parity may break equality. When it doesn't, one would expect the additional constraint to reduce utility, however, we show that utility may in fact increase. In more realistic scenarios, unconstrained policies do not lead to equality. In such cases, we show that although imposing demographic parity may remedy it, there is a danger that groups settle at a worse set of qualifications. As a silver lining, we also identify when the constraint not only leads to equality, but also improves all groups. This gives quantifiable insight into both sides of the mismatch hypothesis. These cases and trade-offs are instrumental in determining when and how imposing demographic parity can be beneficial in selection processes, both for the institution and for society on the long run.

Artificial Intelligence – specifically machine learning and deep learning – was everywhere in 2018 and don't expect the hype to die down over the next 12 months. The hype will die eventually of course, and AI will become another consistent thread in the tapestry of our lives, just like the internet, electricity, and combustion did in days of yore. But for at least the next year, and probably longer, expect astonishing breakthroughs as well as continued excitement and hyperbole from commentators. This is because expectations of the changes to business and society which AI promises (or in some cases threatens) to bring about go beyond anything dreamed up during previous technological revolutions. AI points towards a future where machines not only do all of the physical work, as they have done since the industrial revolution but also the "thinking" work – planning, strategizing and making decisions.

The rapper 2 Milly filed a lawsuit Wednesday against the makers of Fortnite, saying they were illegally using a dance he created in their wildly popular video game. The Brooklyn-based rapper, whose real name is Terrence Ferguson, alleges that the North Carolina-based Epic Games misappropriated his moves without compensation or credit in the lawsuit, filed in federal court in Los Angeles. The lawsuit states that the dance known on Fortnite as "Swipe It", one of many that players can buy for their characters, is taken from the "Milly Rock", a dance he came up with in 2011 that caught on as a craze in the summer of 2015 after the release of a song and video of the same name. Ferguson says that the game both steals his creation and as a result appropriates his likeness. He's asking for a judge's order that the game stop using the dance, and for damages to be determined later.

Amazon Web Services (AWS) has been heavily focused on machine learning over the past few years, releasing a number of products and features which showcase how effective the technology can be for organisations and consumers. But while the technology – much like its parent artificial intelligence – can do a lot of good, there are always questions about what this means in the long-term for humanity, both in terms of a reduction in jobs but also in terms of how these products and services can be used for unethical reasons. The following post emphasises on why technologies such as AI, machine learning turns out to be a big deal for python experts. In a press Q&A last week at AWS reInvent in Las Vegas, AWS CEO Andy Jassy fielded several questions about how the company intends to ensure its machine learning capabilities are used ethically by customers. In response, Jassy cited use cases such as reducing human trafficking and reuniting children with parents where machine learning has already had a positive influence. However, he acknowledged that people may use machine learning for the wrong reasons too.