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Dubai CCTV cameras to use AI, face recognition

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Dubai: Thousands of CCTV cameras of various Dubai government agencies will now provide live feed to a central command centre, officials said. Under a new Artificial Intelligence (AI) network, security cameras across will relay live images of security breaches live to the central command centre, Dubai Police said. The cameras will monitor criminal behaviour in three sectors -- tourism, traffic and bricks and mortar facilities. The network, said the police, is being phased in via different stages to meet the Dubai 2021 Vision requirements of a smart city. Announcing the programme, Major-General Khalil Ebrahim Al Mansouri, Assistant Commander-in-Chief for Criminal Investigation Affairs, said the new project called'Oyoon' (eyes) will tackle crimes in the city and help reduce traffic accident deaths and congestion.


On effective human robot interaction based on recognition and association

arXiv.org Artificial Intelligence

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.


India is trying to build the world's biggest facial recognition system

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India has just 144 police officers for every 100,000 citizens, compared to 318 per 100,000 citizens in the European Union. In recent years, authorities have turned to facial recognition technology to make up for the shortfall. New Delhi's law enforcement agencies adopted the technology in 2018, and it's also being used to police large events and fight crime in a handful of other states, including Andhra Pradesh and Punjab. But India's government now has a much more ambitious plan. It wants to construct one of the world's largest facial recognition systems.


Machine Learning Systems for Highly-Distributed and Rapidly-Growing Data

arXiv.org Machine Learning

The usability and practicality of any machine learning (ML) applications are largely influenced by two critical but hard-to-attain factors: low latency and low cost. Unfortunately, achieving low latency and low cost is very challenging when ML depends on real-world data that are highly distributed and rapidly growing (e.g., data collected by mobile phones and video cameras all over the world). Such real-world data pose many challenges in communication and computation. For example, when training data are distributed across data centers that span multiple continents, communication among data centers can easily overwhelm the limited wide-area network bandwidth, leading to prohibitively high latency and high cost. In this dissertation, we demonstrate that the latency and cost of ML on highly-distributed and rapidly-growing data can be improved by one to two orders of magnitude by designing ML systems that exploit the characteristics of ML algorithms, ML model structures, and ML training/serving data. We support this thesis statement with three contributions. First, we design a system that provides both low-latency and low-cost ML serving (inferencing) over large-scale and continuously-growing datasets, such as videos. Second, we build a system that makes ML training over geo-distributed datasets as fast as training within a single data center. Third, we present a first detailed study and a system-level solution on a fundamental and largely overlooked problem: ML training over non-IID (i.e., not independent and identically distributed) data partitions (e.g., facial images collected by cameras varies according to the demographics of each camera's location).


India is trying to build the world's biggest facial recognition system

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The child labor activist, who works for Indian NGO Bachpan Bachao Andolan, had launched a pilot program 15 months prior to match a police database containing photos of all of India's missing children with another one comprising shots of all the minors living in the country's child care institutions. He had just found out the results. "We were able to match 10,561 missing children with those living in institutions," he told CNN. "They are currently in the process of being reunited with their families." Most of them were victims of trafficking, forced to work in the fields, in garment factories or in brothels, according to Ribhu. This momentous undertaking was made possible by facial recognition technology provided by New Delhi's police.