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.
Vladimir Putin was not in attendance, but his loyal lieutenants were. On 14 July last year, the Russian prime minister, Dmitry Medvedev, and several members of his cabinet convened in an office building on the outskirts of Moscow. On to the stage stepped a boyish-looking psychologist, Michal Kosinski, who had been flown from the city centre by helicopter to share his research. "There was Lavrov, in the first row," he recalls several months later, referring to Russia's foreign minister. "You know, a guy who starts wars and takes over countries." Kosinski, a 36-year-old assistant professor of organisational behaviour at Stanford University, was flattered that the Russian cabinet would gather to listen to him talk. "Those guys strike me as one of the most competent and well-informed groups," he tells me. Kosinski's "stuff" includes groundbreaking research into technology, mass persuasion and artificial intelligence (AI) – research that inspired the creation of the political consultancy Cambridge Analytica. Five years ago, while a graduate student at Cambridge University, he showed how even benign activity on Facebook could reveal personality traits – a discovery that was later exploited by the data-analytics firm that helped put Donald Trump in the White House.
Erik Learned-Miller is one reason we talk about facial recognition at all. In 2007, years before the current A.I. boom made "deep learning" and "neural networks" common phrases in Silicon Valley, Learned-Miller and three colleagues at the University of Massachusetts Amherst released a dataset of faces titled Labelled Faces in the Wild. To you or me, Labelled Faces in the Wild just looks like folders of unremarkable images. You can download them and look for yourself. There's boxer Joe Gatti, gloves raised mid-fight.
It was the first day of school in Russia, a much-beloved unofficial holiday, and President Vladimir Putin was on stage in a national TV broadcast, chatting with jeans-clad teenagers about the future. "Artificial intelligence is the future," he told them, "not only for Russia, but for all humankind. It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world." Then, this March, in the final moments of Putin's re-election campaign, came a stern message to lawmakers at his annual address to parliament: "The speed of technological progress is accelerating sharply...
In previous work [6, 9, 10], we advanced a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measure of similarity based primarily on a Bayesian (MAP) analysis of image differences, leadingto a "dual" basis similar to eigenfaces . The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenface matching was recently demonstrated using results from DARPA's 1996 "FERET" face recognition competition, in which this probabilistic matching algorithm was found to be the top performer. We have further developed a simple method of replacing the costly compution of nonlinear (online) Bayesian similarity measures by the relatively inexpensive computation of linear (offline) subspace projections and simple (online) Euclidean norms, thus resulting in a significant computational speedup for implementation with very large image databases as typically encountered in real-world applications.