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.
In the past few years, there's been a dramatic rise in the adoption of face recognition, detection, and analysis technology. You're probably most familiar with recognition systems, like Facebook's photo-tagging recommender and Apple's FaceID, which can identify specific individuals. Detection systems, on the other hand, determine whether a face is present at all; and analysis systems try to identify aspects like gender and race. All of these systems are now being used for a variety of purposes, from hiring and retail to security and surveillance. Many people believe that such systems are both highly accurate and impartial.
CNL Software has entered into a technology partnership with Herta Security under the CNL Software Technology Alliance Program. Herta develops user-friendly software solutions that enable the integration of facial recognition in security applications. According to the announcement, Herta's deep learning algorithms encode faces directly into small templates, which are very fast to compare and yield more accurate results. This provides a technological advantage when working with partners, as it allows the development of more robust, safer and efficient solutions. IPSecurityCenter PSIM takes a vendor agnostic approach to implement flexible and scalable security management software.
As of today, lots of companies state to assist security firms, the army, in addition to consumers prevent crime and defend their private, homes, and buildings belongings. This particular article intends to offer business leaders in the security space with a concept of what they are able to presently expect from Ai in the business of theirs. We wish this report allows company leaders in security to garner insights they are able to confidently relay to the executive teams of theirs so they are able to make educated choices when thinking about AI adoption. At the minimum, this article intends to serve as a technique of decreasing the time industry leaders in physical security spend researching AI businesses with whom they might (or might not) be keen on working. Evolv Technology claims to offer a physical security system that consists of the Evolve Edgepersonnel threat screening machine that works with the Evolv Pinpoint automated facial recognition application.
There are few processes in life as nerve-wracking and tedious as going through security at an airport. Whether it's adhering to Transportation Security Administration (TSA) rules of removing laptops from bags, or navigating the seemingly endless, winding queue, getting screened before a flight is time-consuming. But with the help of the Department of Homeland Security (DHS), researchers are working on integrating video surveillance with artificial intelligence (AI) to make this vital security process much smoother. The development new technology to streamline airport security has stagnated in recent decades. A lack of innovation, coupled with a need for increased screening in the wake of events like the 9/11 attacks, have only made the process worse.